Cooks in batch for booked events (weddings, functions, corporate) charging per plate; capacity is meals-per-event × events, not fixed seats, with advance deposits funding the food buy and strong wedding-season peaks.
Turnover · metro₹20.19 L
Turnover · non-metro₹14.13 L
EBITDA · metro₹6.06 L
Photographs alone±134%
Full instrument stack±11% · tier A
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Plates / event (batch capacity) | Claim | 41.18 | 50.9653 | 69.9244 | plates |
| Events / serving day | Claim | 0.5097 | 0.5819 | 0.726 | events |
| Price / plate | Claim | 182.3389 | 218.2247 | 281.2674 | ₹ |
| Serving days / yr (seasonal) | Claim | 271.2 | 312 | 360 | days |
Registry: gm [0.38, 0.46, 0.54] · opex [0.136, 0.16, 0.184] · DIO 25.3d · DSO 25d · DPO 15d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹4,500 / ₹4,800 / ₹6,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned prep kitchen; substitute a fixed-asset/collateral note (vessels, cooking range, transport) for rent
Balance-sheet build
inventory low & event-driven (bought against confirmed orders) — DIO 4–6d; receivables material (event clients 15–30d, DSO 18–25) partly offset by customer advances (current liability); payables = raw-material suppliers 10–20d; fixed assets = cooking range, bulk vessels, refrigeration, transport, tents/serving gear
Guided capture — photo order
1 exterior 2 kitchen 3 storage 4 machinery 5 event_order_book 6 dispatch 7 qr_code 8 utility_meter 9 gst_board 10 licence 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Event order/booking register (12 months) Observed
Dated bookings give events/yr directly and expose seasonality.
Signed quotation / invoice per plate Observed
Contracted per-plate rate constrains cover across menu tiers.
Kitchen batch capacity (vessels, burners, staff) Observed
Cooking gear + crew size caps plates deliverable per event.
Advance-deposit ledger External
Deposits received corroborate event count and fund the food buy (lowers WCR).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 & e-invoice | declared per-event billing vs order-book-derived turnover (concordance) | strong |
| Advance-deposit / bank AA feed | deposits and settlements → event count and receivable pattern; advances = customer liability offsetting WCR | strong |
| UPI/QR settlement | balance payments and smaller orders → banked share | medium |
| FSSAI (catering) licence | legitimacy + declared capacity band | medium |
| Electricity/LPG bill | kitchen load → batch-capacity sanity; power/fuel cost | medium |
| Rental agreement | prep-kitchen rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — no walk-in footfall; activity = meals dispatched per event captured via dispatch/order book |
| B2B / counterparties | count event counterparties from e-way bills, GSTR-1 line items and the booking register; venue/decorator tie-ups bound event flow; peak concentration in wedding/festival months |
Variance path: Photographs alone put annual turnover within ±134% at 90% coverage, from this trade's own assessed quartile spread on 30 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 33.1% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: GST 3B/GSTR-1 & e-invoice (declared per-event billing vs order-book-derived turnover (concordance)); Advance-deposit / bank AA feed (deposits and settlements → event count and receivable pattern).
Fills seats across meal sittings (peak-hour turns) at a per-head cover, topped up by takeaway and Swiggy/Zomato orders, earning a gross margin over food cost after paying kitchen labour, rent and aggregator commission.
Turnover · metro₹26.21 L
Turnover · non-metro₹18.35 L
EBITDA · metro₹9.17 L
Photographs alone±77%
Full instrument stack±10% · tier A
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Covers (seats) | Observed | 17.4786 | 26.2179 | 37.8703 | seats |
| Table turns / day (incl. takeaway equiv.) | Observed | 1.0085 | 1.5128 | 2.1179 | turns |
| Average cover / head | Claim | 141.1915 | 211.7872 | 302.5532 | ₹ |
| Operating days / yr | Claim | 300 | 312 | 360 | days |
Registry: gm [0.58, 0.64, 0.7] · opex [0.2465, 0.29, 0.3335] · DIO 32.4d · DSO 6d · DPO 15d · η 1.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹5,000 / ₹5,250 / ₹6,750 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note (kitchen fit-out, chillers) for rent
Balance-sheet build
inventory = walk-in/chiller + dry-store worksheet (Observed), perishable so low DIO overrides benchmark; receivables = aggregator settlement float (T+7) only, dine-in cash/UPI ≈ 0; payables = veg daily + grocery 15–30d credit; fixed assets = kitchen equipment, chillers, seating, POS, signage
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 menu_board 5 display 6 storage 7 qr_code 8 aggregator_dashboard 9 utility_meter 10 gst_board 11 licence 12 pukka_invoice 13 kacha_bill 14 udyam
Next-photo evidence · Eq 7
Timed peak-hour occupancy count Observed
Seat count + fill at lunch/dinner peaks bounds usable covers.
POS Z-report day-total & bill count Observed
Bill count / seats gives realised turns; misses no cash covers.
Menu board price sample Observed
Priced menu × typical basket constrains average cover.
Swiggy/Zomato dashboard weekly orders & AOV External
Aggregator AOV and order volume cross-check cover and off-premise share.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Aggregator settlement (Swiggy/Zomato) | off-premise turns & cover → banked order value net of commission; T+7 settlement drives dso | strong |
| UPI/QR settlement | dine-in & takeaway covers → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover (5%/ GST composition) vs photo-derived (concordance) | strong |
| FSSAI licence | legitimacy + declared seating/kitchen scale sanity | medium |
| Electricity bill | connected load (AC + kitchen) → floor/kitchen size; owned/rented; power cost | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3–4 timed exterior/interior captures (weekday lunch, weekday dinner peak, weekend dinner) → seat-fill curve; cross-check vs POS bill count and UPI txns |
| B2B / counterparties | aggregator order feed provides a second, independent throughput signal for off-premise sales |
Variance path: Photographs alone put annual turnover within ±77% at 90% coverage, from this trade's own assessed quartile spread on 53 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 29.3% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: Aggregator settlement (Swiggy/Zomato) (off-premise turns & cover → banked order value net of commission); UPI/QR settlement (dine-in & takeaway covers → banked turnover); GST 3B/GSTR-1 (declared turnover (5%/ GST composition) vs photo-derived (concordance)).
Serves a very high volume of low-ticket cups of tea (and snacks) from a tiny footprint at near-zero inventory, earning a high per-cup margin over milk/tea/sugar cost with minimal overhead — mostly cash with a QR on the side.
Turnover · metro₹16.29 L
Turnover · non-metro₹11.40 L
EBITDA · metro₹6.51 L
Photographs alone±67%
Full instrument stack±10% · tier A
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Standing/bench service spots | Observed | 5.6685 | 6.8987 | 9.3582 | spots |
| Customers / spot / day (very high) | Claim | 27.0496 | 35.0643 | 45.784 | turns |
| Average spend / customer | Claim | 15.7289 | 20.0368 | 28.0515 | ₹ |
| Operating days / yr | Claim | 292.8 | 336 | 360 | days |
Registry: gm [0.52, 0.6, 0.68] · opex [0.17, 0.2, 0.23] · DIO 18.6d · DSO 0d · DPO 3d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Rent/mo · non-metro | ₹5,250 / ₹6,500 / ₹7,375 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + municipal hawker/pitch licence or own frontage in Mitra pack → owned/licensed pitch; substitute a small fixed-asset note (cart, burner, urn) for rent
Balance-sheet build
inventory ≈ nil (milk daily, tea/sugar few days) — DIO 1–2d, ignore benchmark stock build; receivables = 0 (cash/UPI at point of sale); payables = milk vendor 2–5d only; fixed assets = cart/kiosk, LPG burner, urn, benches — collateral-light
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 menu_board 5 storage 6 qr_code 7 utility_meter 8 kacha_bill 9 licence 10 udyam
Next-photo evidence · Eq 7
Timed cups-per-hour count (morning + evening peaks) Observed
Counted cups over two peak hours extrapolated to day bounds the very-high turns.
Daily milk intake (litres) from vendor slip External
Litres/day ÷ millilitres per cup back-solves daily cups independent of till.
Rate card / menu board price sample Observed
Tea + snack prices constrain the tiny average spend.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | partial banked cups → sets a floor; large residual expected (cash-heavy) | medium |
| Milk/dairy vendor supply slip | daily litres → cups/day back-check; strongest volume signal here | strong |
| Electricity/LPG receipt | single burner/light load → micro-scale sanity; power cost negligible | medium |
| Municipal hawker/FSSAI petty licence | legitimacy + pitch tenure | medium |
| GST | usually below threshold / unregistered → GST absent is itself a scale signal | weak |
Activity signals
| Footfall | 2 timed captures at morning (7–10am) and evening (4–7pm) peaks → cups-per-hour curve; cross-check vs milk intake and QR count |
| B2B / counterparties | n/a (pure B2C micro-retail of prepared tea) |
Variance path: Photographs alone put annual turnover within ±67% at 90% coverage, from this trade's own assessed quartile spread on 19 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 52.7% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: Milk/dairy vendor supply slip (daily litres → cups/day back-check).
Delivers a recurring number of subscribed home-style meals per day (lunch and/or dinner) at a low per-meal price, billed monthly, run from a home kitchen or small unit at low overhead — revenue is subscriber count × meals/day.
Turnover · metro₹15.46 L
Turnover · non-metro₹10.82 L
EBITDA · metro₹5.41 L
Photographs alone±68%
Full instrument stack±10% · tier A
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Subscribers / meals dispatched per day | Observed | 31.141 | 41.8312 | 65.5355 | tiffins |
| Meals / subscriber / day (lunch+dinner) | Claim | 1.216 | 1.4 | 1.676 | meals |
| Price / meal | Claim | 66.2 | 80 | 103 | ₹ |
| Operating days / yr | Claim | 316 | 330 | 344 | days |
Registry: gm [0.36, 0.44, 0.52] · opex [0.0765, 0.09, 0.1035] · DIO 4d · DSO 22d · DPO 12d · η 0.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Rent/mo · non-metro | ₹4,500 / ₹4,500 / ₹7,500 |
| Deposit → BS asset | 3 months |
Owned signal: residential electricity bill in proprietor's name + no commercial rental agreement → home kitchen (owned); substitute a small fixed-asset note (cooking range, containers, cycle/scooter) for rent
Balance-sheet build
inventory low (bought against known daily demand) — DIO 3–5d; receivables material because billing is monthly-in-arrears (DSO 18–22) though some plans prepay (advance = current liability); payables = grocer/dairy 7–15d; fixed assets = cooking range, tiffin carriers/containers, delivery cycle/scooter — collateral-light
Guided capture — photo order
1 exterior 2 kitchen 3 storage 4 dispatch 5 subscription_register 6 menu_board 7 qr_code 8 utility_meter 9 gst_board 10 licence 11 udyam
Next-photo evidence · Eq 7
Subscriber / route roster (current month) Observed
Active subscriber list gives daily tiffins directly.
Timed dispatch / dabba count at pack-out Observed
Counted meal boxes leaving the kitchen corroborate the roster.
Monthly plan rate card Observed
Monthly plan ÷ meals served back-solves per-meal price.
Monthly billing / UPI receipts ledger External
Recurring collections confirm lunch-vs-both split and receivable days.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR & AA bank feed | monthly subscription collections → subscriber count & banked turnover; recurring pattern is the key signal | strong |
| Subscriber roster / delivery route | daily tiffins (seats) and lunch/dinner split | strong |
| GST | often below threshold; where filed, monthly turnover concordance | medium |
| FSSAI registration/licence | legitimacy + kitchen scale | medium |
| Electricity/LPG bill | cooking load → meals-capacity sanity; fuel cost | medium |
| Rental agreement | unit rent + deposit (BS) where not a home kitchen | weak |
Activity signals
| Footfall | n/a — no walk-in; activity = meals dispatched per day, captured via a timed dabba/box count at midday pack-out |
| B2B / counterparties | corporate/hostel tie-ups (bulk subscriptions) counted from the roster and monthly invoices bound the higher end of subscriber count |
Variance path: Photographs alone put annual turnover within ±68% at 90% coverage, from this trade's own assessed quartile spread on 10 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR & AA bank feed (monthly subscription collections → subscriber count & banked turnover); Subscriber roster / delivery route (daily tiffins (seats) and lunch/dinner split).
Buys seasonal farm commodity in bulk, cleans/mills/expels it into graded finished goods (dal, ground spice, oil + cake), and earns a thin commodity conversion margin on high throughput sold to wholesalers and traders.
Turnover · metro₹33.60 L
Turnover · non-metro₹23.52 L
EBITDA · metro₹5.04 L
Photographs alone±136%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished output / hr | Observed | 56.8334 | 75.7779 | 98.5109 | kg/hr |
| Productive hrs / day | Claim | 8 | 12 | 12.24 | hr |
| Capacity utilisation | Derived | 0.48 | 0.6 | 0.72 | fraction |
| Realisation / kg finished | Claim | 17.4413 | 20.1246 | 23.2551 | ₹/kg |
| Operating days / yr | Claim | 108 | 306 | 360 | days |
Registry: gm [0.18, 0.21, 0.25] · opex [0.051, 0.06, 0.069] · DIO 24.5d · DSO 30d · DPO 28d · η 1.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹80,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Deposit → BS asset | 6 months |
Owned signal: Udyam + DISCOM bill in proprietor/firm name and no rental agreement → owned industrial shed; substitute fixed-asset/collateral note (plant + land) for rent
Balance-sheet build
Inventory splits RM (seasonal grain/seed, largest and most volatile) + WIP (in-process/settling) + FG (bagged) via stock worksheet, overriding benchmark DIO at peak; receivables = trader credit 25–35d; payables = commission-agent/farmer credit 20–30d; fixed assets = expeller/pulveriser/cleaning line, silos, weighbridge, shed
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 rm_silo 6 wip_floor 7 weighbridge 8 dispatch 9 utility_meter 10 production_register 11 gst_board 12 pukka_invoice 13 licence 14 udyam
Next-photo evidence · Eq 7
Mill/expeller nameplate rating Observed
Installed kg/hr rating caps throughput; cross-check with power draw.
DISCOM demand + monthly units External
kWh vs nameplate load estimates true running utilisation (eta proxy).
Shift / grinding register Claim
Daily start-stop entries bound productive hours through the season.
Finished-goods sale invoice sample External
₹/kg realisation net of by-product credit.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills (inward + outward) | RM procurement tonnage & finished dispatch → throughput ceiling and B2B counterparty count | strong |
| GST GSTR-1/3B | declared turnover vs photo-derived output × price (concordance) | strong |
| Electricity/DISCOM bill | connected load + kWh → running utilisation and processing intensity (eta) | strong |
| AA bank feed | commodity purchase outflows & sale receipts → seasonal working-capital swing | medium |
| Rental agreement / Udyam | owned-vs-rented, deposit (BS), plant registration | medium |
Activity signals
| Footfall | n/a (B2B commodity processor) |
| B2B / counterparties | Count distinct buyer GSTINs on GSTR-1 and inward supplier GSTINs on e-way bills; dispatch register + weighbridge slips bound daily tonnage out and reconcile against uph × hours |
Variance path: Photographs alone put annual turnover within ±136% at 90% coverage, from this trade's own assessed quartile spread on 8 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: E-way bills (inward + outward) (RM procurement tonnage & finished dispatch → throughput ceiling and B2B counterparty count); GST GSTR-1/3B (declared turnover vs photo-derived output × price (concordance)); Electricity/DISCOM bill (connected load + kWh → running utilisation and processing intensity (eta)).
Order-driven workshop where semi-skilled carpenters convert timber and board into furniture and fittings against customer orders; revenue is labour-and-material value added per piece, so output tracks how many orders are on hand (utilisation) rather than steady-state production.
Turnover · metro₹17.16 L
Turnover · non-metro₹12.01 L
EBITDA · metro₹5.15 L
Photographs alone±37%
Full instrument stack±10% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished-piece equivalents / productive hour | Observed | 0.3601 | 0.4731 | 0.6144 | units/hr |
| Productive bench-hours / day | Observed | 7 | 9 | 10 | hrs |
| Order-fill utilisation | Claim | 0.492 | 0.6 | 0.697 | fraction |
| Blended realisation / piece | Claim | 1656.1715 | 2152.4695 | 2979.6328 | ₹ |
| Operating days / yr | Claim | 300 | 312 | 361.6 | days |
Registry: gm [0.28, 0.35, 0.42] · opex [0.0425, 0.05, 0.0575] · DIO 35d · DSO 20d · DPO 25d · η 0.5
Occupancy — owned vs rented
| Rent/mo · metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Rent/mo · non-metro | ₹2,400 / ₹3,000 / ₹4,250 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/family premises; replace rent with fixed-asset note (shed + tools)
Balance-sheet build
Inventory = timber/ply/board/hardware RM + WIP half-built pieces (worksheet overrides DIO) + small FG; receivables low as advances are taken; customer advances on made-to-order work are a real liability; payables = timber supplier 20–30d; fixed assets = table saw, planer, router, hand tools, workbenches.
Guided capture — photo order
1 exterior 2 interior 3 machinery 4 tool_rack 5 storage 6 wip_zone 7 dispatch 8 qr_code 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 gst_board 13 order_book 14 udyam 15 licence
Next-photo evidence · Eq 7
Order pad / advance-booking register Observed
Live orders vs bench count fixes utilisation, the key swing.
Recent job invoice / estimate slip Claim
Anchors blended realisation per piece (often kachha).
Carpenter headcount at benches Observed
Hands on benches cap finished-piece output rate.
Timber / board stock stack photo Observed
Standing RM depth signals sustained working days vs sporadic operation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | advances & retail receipts → banked share; cash residual is high | medium |
| GST 3B/GSTR-1 | declared turnover if registered (many are composition/unregistered → weaker) | weak |
| Electricity bill | light connected load → premises size sanity; owned/rented | medium |
| Timber purchase bills | RM inflow → output capacity & DIO | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | Mostly n/a; occasional walk-in enquiries — one timed exterior capture only if a display frontage exists |
| B2B / counterparties | Estimate forward pipeline from the order pad; interior-fit contractors and shops are repeat buyers — count them from invoices/UPI counterparties where present. Expect a large cash-sale share a bank feed misses. |
Variance path: Photographs alone put annual turnover within ±37% at 90% coverage, from this trade's own assessed quartile spread on 35 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 26.3% on the real book, so a bank feed alone cannot reach it.
Order-driven B2B job-shop that sells billable machine-and-labour hours: customers bring drawings or components, the shop machines/fabricates to spec and invoices per job, so revenue swings with how much of installed machine capacity is booked (utilisation).
Turnover · metro₹60.74 L
Turnover · non-metro₹42.52 L
EBITDA · metro₹7.29 L
Photographs alone±106%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Billable jobs / machine-hour (shop aggregate) | Observed | 1.072 | 1.6081 | 2.2335 | jobs/hr |
| Productive machine-hours / day | Observed | 6 | 8 | 12 | hrs |
| Order-fill utilisation of capacity | Claim | 0.45 | 0.65 | 0.8 | fraction |
| Blended realisation / job | Claim | 1496.6042 | 2328.051 | 3492.0765 | ₹ |
| Operating days / yr | Claim | 246 | 312 | 357.6 | days |
Registry: gm [0.32, 0.4, 0.48] · opex [0.238, 0.28, 0.322] · DIO 16.6d · DSO 45d · DPO 30d · η 1.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹18,000 / ₹35,000 / ₹70,000 |
| Rent/mo · non-metro | ₹4,750 / ₹5,500 / ₹7,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in proprietor's name + no rental agreement in Mitra pack → owned shed; substitute a fixed-asset/collateral note (shed + machines) for rent
Balance-sheet build
Inventory = bar/plate RM stock + WIP jobs on the floor (worksheet overrides DIO) + minimal FG (dispatched to order); receivables material — B2B credit 30–60d drives WCR; customer advances on large jobs sit as a liability; payables = steel supplier 30d; fixed assets = lathes, CNC, milling, welding sets, tooling.
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 tool_rack 6 storage 7 wip_zone 8 dispatch 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 gst_board 13 order_book 14 udyam 15 licence
Next-photo evidence · Eq 7
Open order book / job register Observed
Booked jobs vs machine count fixes utilisation, the dominant swing.
Recent per-job invoice sample External
Cross-checks blended realisation per job against e-invoice.
Machine / lathe / CNC count with plates Observed
Installed spindles cap achievable jobs/hour.
Attendance / shift muster Claim
Confirms single vs double shift → productive hours/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared B2B turnover vs photo-derived (concordance); output-tax base | strong |
| E-way / e-invoice | dispatch value & B2B counterparty count → throughput ceiling | strong |
| Electricity bill | connected load & kWh → machine count and eta triangulation (Eq 8) | medium |
| AA bank feed | B2B receipts vs invoiced sales; receivable ageing (dso) | medium |
| Rental agreement | shed rent expense + deposit (BS) | medium |
Activity signals
| Footfall | n/a (B2B job-shop, no walk-in footfall) |
| B2B / counterparties | Count distinct customers from GSTR-1 / e-way counterparties and open order-book lines; repeat OEM/contractor buyers bound sustainable throughput and receivable concentration |
Variance path: Photographs alone put annual turnover within ±106% at 90% coverage, from this trade's own assessed quartile spread on 51 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared B2B turnover vs photo-derived (concordance)); E-way / e-invoice (dispatch value & B2B counterparty count → throughput ceiling).
Order-driven steel fabrication: MS bar, pipe and sheet are cut, welded and finished into gates, grills, sheds and structures priced per kilogram or per job, part in-workshop and part on-site, so output and power draw both track how many jobs are booked (utilisation).
Turnover · metro₹55.65 L
Turnover · non-metro₹35.19 L
EBITDA · metro₹3.90 L
Photographs alone±153%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Steel fabricated / productive hour (shop aggregate) | Observed | 13 | 22 | 32 | kg/hr |
| Productive welding-hours / day | Observed | 7 | 8.5 | 10 | hrs |
| Order-fill utilisation | Claim | 0.42 | 0.62 | 0.8 | fraction |
| Blended realisation / kg (incl. labour) | Claim | 110 | 160 | 220 | ₹/kg |
| Operating days / yr | Claim | 285 | 300 | 315 | days |
Registry: gm [0.26, 0.33, 0.4] · opex [0.2, 0.26, 0.32] · DIO 28d · DSO 40d · DPO 25d · η 2.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Rent/mo · non-metro | ₹3,500 / ₹7,000 / ₹15,000 |
| Deposit → BS asset | 4 months |
Owned signal: power bill in proprietor's name + no rental agreement → owned yard/shed; substitute fixed-asset/collateral note (yard + welding plant) for rent
Balance-sheet build
Inventory = MS bar/pipe/sheet RM + WIP fabricated gates/structures (worksheet overrides DIO) + FG awaiting dispatch/installation; receivables B2B plus site retention drive WCR (dso 35–50d); material advances from customers are a liability; payables = steel supplier 20–30d; fixed assets = welding sets, cutting/grinding machines, drill, compressor.
Guided capture — photo order
1 exterior 2 interior 3 machinery 4 tool_rack 5 storage 6 wip_zone 7 dispatch 8 utility_meter 9 pukka_invoice 10 kacha_bill 11 gst_board 12 order_book 13 udyam 14 licence
Next-photo evidence · Eq 7
Job order book / site work orders Observed
Booked gate/grill/structure jobs vs plant fixes utilisation.
Per-kg / per-job invoice sample External
Anchors blended ₹/kg realisation.
Welding-set / cutter count with rating Observed
Number & rating of welding sets cap kg/hour throughput.
Monthly kWh from DISCOM bill External
Welding kWh is a strong proxy for productive hours (Eq 8 eta triangulation).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared B2B/contractor turnover vs photo-derived (concordance) | strong |
| E-way / e-invoice | dispatched fabricated value & site counterparties → throughput | strong |
| Electricity bill | welding load & kWh → uph and eta triangulation (very informative) | strong |
| Steel purchase bills | MS RM inflow (kg) → output capacity & material cost | medium |
| AA bank feed | B2B receipts, retention/advances vs invoiced (dso) | medium |
Activity signals
| Footfall | n/a (order/site-driven; no retail footfall) |
| B2B / counterparties | Estimate builders/contractors and site jobs from e-way bills, GSTR-1 counterparties and the work-order book; welding kWh draw curve corroborates active fabrication days versus idle. |
Variance path: Photographs alone put annual turnover within ±153% at 90% coverage in the metro band and ±156% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared B2B/contractor turnover vs photo-derived (concordance)); E-way / e-invoice (dispatched fabricated value & site counterparties → throughput); Electricity bill (welding load & kWh → uph and eta triangulation (very informative)).
Mills wheat into atta/maida or paddy into rice at high tonnage, selling primary output to wholesalers plus by-products (bran, husk, broken grain); a very thin per-kg milling margin on large volume, with power a key cost.
Turnover · metro₹14.52 Cr
Turnover · non-metro₹7.82 Cr
EBITDA · metro₹58.06 L
Photographs alone±102%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Milled output / hr | Observed | 1200 | 1800 | 2500 | kg/hr |
| Productive hrs / day | Claim | 11 | 14 | 16 | hr |
| Capacity utilisation | Derived | 0.48 | 0.6 | 0.72 | fraction |
| Blended realisation / kg | Claim | 27 | 32 | 38 | ₹/kg |
| Operating days / yr | Claim | 275 | 300 | 320 | days |
Registry: gm [0.06, 0.09, 0.12] · opex [0.035, 0.05, 0.07] · DIO 45d · DSO 22d · DPO 22d · η 2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹85,000 / ₹1.60 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: Udyam + DISCOM bill + weighbridge on-site in firm name, no lease → owned mill; substitute plant/land collateral note for rent
Balance-sheet build
Inventory = RM (grain, largest line, seasonal + MSP-linked) + minimal WIP (short mill cycle) + FG (bagged flour/rice) + by-product stock; DIO worksheet overrides benchmark at harvest peak; receivables low 15–25d (part-cash wholesale); payables 15–25d; fixed assets = roller/huller line, silos, weighbridge, packing, DG set
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 rm_silo 5 storage 6 wip_floor 7 weighbridge 8 dispatch 9 utility_meter 10 production_register 11 gst_board 12 pukka_invoice 13 licence 14 udyam
Next-photo evidence · Eq 7
Roller-body/huller nameplate (TPH) Observed
Rated tonnes-per-hour caps milling throughput.
Monthly kWh vs connected load External
Specific energy per tonne fixes running utilisation (eta proxy).
Weighbridge slip book (in/out tonnage) Observed
Daily tonnage in/out bounds running hours and yield.
Atta/rice + by-product sale invoice External
Blended ₹/kg incl. bran/husk credit.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills | grain inward & flour/rice outward tonnage → throughput and buyer count | strong |
| GST GSTR-1/3B | declared turnover vs output × blended price (note exempt/branded mix) | strong |
| Electricity/DISCOM bill | kWh per tonne → utilisation and processing intensity (eta) | strong |
| Weighbridge log | daily in/out tonnage → capacity realisation | strong |
| AA bank feed / rental / Udyam | receipts, owned-vs-rented, deposit, registration | medium |
Activity signals
| Footfall | n/a (B2B miller) |
| B2B / counterparties | Buyer GSTIN count on GSTR-1 (wholesalers, government procurement) and grain-supplier GSTINs on inward e-way bills; weighbridge + dispatch register give ground-truth daily tonnage to bound uph × hours × util |
Variance path: Photographs alone put annual turnover within ±102% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: E-way bills (grain inward & flour/rice outward tonnage → throughput and buyer count); GST GSTR-1/3B (declared turnover vs output × blended price (note exempt/branded mix)); Electricity/DISCOM bill (kWh per tonne → utilisation and processing intensity (eta)).
Batch-fries/roasts and packs branded namkeen and snacks under FSSAI licence, selling through distributors and retail; earns a healthier brand margin than commodity processors but carries distribution, marketing and shelf-life costs.
Turnover · metro₹7.00 Cr
Turnover · non-metro₹3.83 Cr
EBITDA · metro₹62.99 L
Photographs alone±102%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Packed output / hr | Observed | 120 | 180 | 250 | kg/hr |
| Productive hrs / day | Claim | 10 | 12 | 14 | hr |
| Line utilisation | Derived | 0.48 | 0.6 | 0.73 | fraction |
| Realisation / kg (wholesale) | Claim | 150 | 180 | 220 | ₹/kg |
| Operating days / yr | Claim | 280 | 300 | 318 | days |
Registry: gm [0.2, 0.27, 0.34] · opex [0.14, 0.18, 0.23] · DIO 32d · DSO 30d · DPO 28d · η 1.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹50,000 / ₹1.00 L / ₹2.00 L |
| Rent/mo · non-metro | ₹18,000 / ₹40,000 / ₹80,000 |
| Deposit → BS asset | 6 months |
Owned signal: FSSAI address + DISCOM bill + Udyam in firm name, no lease → owned unit; substitute plant/cold-store collateral note for rent
Balance-sheet build
Inventory = RM (besan/oil/spices/packaging film) + WIP (fried/seasoning stage) + FG (packed, shelf-life-limited so DIO capped low) via worksheet; receivables = distributor credit 20–40d; payables = RM/packaging credit 20–35d; fixed assets = fryers, ovens, mixers, auto-packing machines, cold/dry store, brand/trademark (intangible)
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 wip_floor 6 display 7 dispatch 8 utility_meter 9 production_register 10 gst_board 11 pukka_invoice 12 licence 13 udyam
Next-photo evidence · Eq 7
Auto-packing machine pouch counter Observed
Pouches/min × pack weight fixes packed kg/hr.
Daily batch / production sheet Claim
Batches/day × batch size bounds hours and line utilisation.
Distributor tax invoice + price list External
Net wholesale ₹/kg after scheme/margin.
Monthly kWh (fryer/oven load) External
Frying/roasting energy corroborates running utilisation (eta).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST GSTR-1/3B | declared turnover vs output × price; distributor spread | strong |
| E-way bills | RM (besan/oil/spice) inward & FG dispatch to distributors → throughput and distributor count | strong |
| AA bank / UPI-QR settlement | distributor collections + counter cash sales → banked vs cash split | medium |
| Electricity bill | fryer/oven + packing load → utilisation and eta | medium |
| FSSAI licence / Udyam / rental | licensed capacity, owned-vs-rented, deposit | medium |
Activity signals
| Footfall | Optional: factory-outlet counter footfall via 2–3 timed captures cross-checked with counter UPI/QR; minor vs wholesale |
| B2B / counterparties | Distributor/retailer GSTIN count on GSTR-1 and RM-supplier GSTINs on inward e-way; dispatch register cartons/day + packing counter bound daily packed output |
Variance path: Photographs alone put annual turnover within ±102% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST GSTR-1/3B (declared turnover vs output × price); E-way bills (RM (besan/oil/spice) inward & FG dispatch to distributors → throughput and distributor count).
Larger workshop making both batch stock lines and custom furniture from wood, board and foam, often with a showroom carrying finished stock; revenue blends made-to-stock production with made-to-order jobs, so utilisation stays higher than pure job-work but finished-goods inventory is heavy.
Turnover · metro₹2.05 Cr
Turnover · non-metro₹1.34 Cr
EBITDA · metro₹24.64 L
Photographs alone±83%
Full instrument stack±10% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished-piece equivalents / hour (line aggregate) | Observed | 1.55 | 2 | 2.675 | units/hr |
| Productive shop-hours / day | Observed | 8.437 | 9 | 10.1 | hrs |
| Capacity utilisation (batch + order) | Claim | 0.567 | 0.68 | 0.77 | fraction |
| Blended realisation / piece | Claim | 4374 | 5500 | 7752 | ₹ |
| Operating days / yr | Claim | 297 | 305 | 313 | days |
Registry: gm [0.34, 0.42, 0.5] · opex [0.24, 0.3, 0.36] · DIO 60d · DSO 25d · DPO 30d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement for the workshop → owned; showroom on high street is usually rented even when the shed is owned — check both premises separately
Balance-sheet build
Inventory is the heaviest of the four — RM (board/ply/wood/foam/hardware) + WIP + finished-goods stock in showroom/warehouse (DIO 55–65d); receivables = dealer credit + retail EMI (dso 20–30d); customer advances on custom orders are a liability; payables = board/foam suppliers 28–35d; fixed assets = panel saw, edge-bander, CNC router, spray booth, showroom fit-out.
Guided capture — photo order
1 exterior 2 showroom 3 interior 4 machinery 5 tool_rack 6 storage 7 wip_zone 8 display 9 dispatch 10 price_board 11 utility_meter 12 pukka_invoice 13 gst_board 14 order_book 15 udyam 16 licence
Next-photo evidence · Eq 7
Production plan / order + dealer book Observed
Batch plan + open orders vs line capacity fixes utilisation.
Invoice / showroom price-board sample External
Anchors blended realisation across stock and custom lines.
Panel saw / edge-bander / router count Observed
Installed machine line caps finished-piece output rate.
Shift muster / attendance Claim
Confirms single vs double shift → productive hours/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B / e-invoice / e-way | declared turnover, dealer dispatches & counterparties (concordance) | strong |
| UPI/QR settlement | showroom retail sales & advances → banked share | strong |
| Electricity bill | machine load (saw/bander/spray) → uph & eta sanity | medium |
| RM purchase bills (board/foam) | material inflow → output capacity, gm & DIO | medium |
| Rental agreement | workshop + showroom rent & deposit (BS) | medium |
Activity signals
| Footfall | 3 timed showroom captures (weekday evening, weekend) → walk-in curve; cross-check vs UPI/POS retail count and conversion to orders |
| B2B / counterparties | Count dealer/interior-contractor counterparties from GSTR-1 & e-way; dealer credit and order backlog bound sustainable batch throughput |
Variance path: Photographs alone put annual turnover within ±83% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B / e-invoice / e-way (declared turnover, dealer dispatches & counterparties (concordance)); UPI/QR settlement (showroom retail sales & advances → banked share).
Runs banks of sewing machines with operators to stitch garments on a cut-make-trim job-work basis for principals/exporters who supply the fabric; earns a per-piece stitching charge, so revenue is labour-and-machine driven with very low owned raw material.
Turnover · metro₹2.46 Cr
Turnover · non-metro₹1.40 Cr
EBITDA · metro₹24.57 L
Photographs alone±116%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Pieces stitched / hr (all lines) | Observed | 300 | 450 | 620 | pieces/hr |
| Productive hrs / day | Claim | 8.5 | 10 | 11.5 | hr |
| Line/operator utilisation | Derived | 0.56 | 0.7 | 0.82 | fraction |
| Job-work rate / piece | Claim | 18 | 26 | 36 | ₹/piece |
| Operating days / yr | Claim | 285 | 300 | 312 | days |
Registry: gm [0.28, 0.36, 0.45] · opex [0.2, 0.26, 0.32] · DIO 18d · DSO 42d · DPO 15d · η 0.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹16,000 / ₹35,000 / ₹70,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill + Udyam in firm name, no lease → owned shed; substitute machinery/shed collateral note for rent (machines are the main fixed asset)
Balance-sheet build
Owned inventory is LOW — principal supplies fabric (held as non-owned job-work stock, off balance sheet); owned inventory ≈ thread/trims/packaging + WIP (bundles in-line) only → short DIO; receivables = principal credit 35–50d (concentration risk); payables low 10–20d (consumables); fixed assets = sewing/overlock/flatlock machines, cutting tables, pressing (machines are primary collateral)
Guided capture — photo order
1 exterior 2 machinery 3 interior 4 wip_floor 5 dispatch 6 utility_meter 7 production_register 8 gst_board 9 pukka_invoice 10 licence 11 udyam
Next-photo evidence · Eq 7
Installed machines × occupied operator seats Observed
Sewing machines × seated operators × per-operator rate sets piece throughput.
Job-work challan / invoice (pieces × rate) External
Per-piece CMT rate and style mix from principal billing.
Daily dispatch / bundle-completion register Observed
Pieces completed/day reveals true line utilisation and absenteeism drag.
Operator attendance / shift board Claim
Present operators × hours bound effective productive hours.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Job-work challans / e-way (fabric in, garments out) | principal-supplied fabric inward & stitched dispatch → pieces handled and principal count | strong |
| GST GSTR-1/3B (SAC job-work) | declared job-work receipts vs pieces × rate (concordance) | strong |
| AA bank feed | principal payments (often 1–3 principals → concentration) vs invoiced pieces | strong |
| Electricity bill | low load (light sewing) → utilisation sanity, owned-vs-rented; eta is low | medium |
| Rental / Udyam / factory licence | machine count, worker band, owned-vs-rented, deposit | medium |
Activity signals
| Footfall | n/a (B2B job-worker) |
| B2B / counterparties | Principal GSTIN count is small (1–3) → concentration risk read from GSTR-1 and inward fabric challans; occupied-seat count × line balance and dispatch register bound pieces/day and cross-check pieces × rate against banked receipts |
Variance path: Photographs alone put annual turnover within ±116% at 90% coverage in the metro band and ±113% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Job-work challans / e-way (fabric in, garments out) (principal-supplied fabric inward & stitched dispatch → pieces handled and principal count); GST GSTR-1/3B (SAC job-work) (declared job-work receipts vs pieces × rate (concordance)); AA bank feed (principal payments (often 1–3 principals → concentration) vs invoiced pieces).
Converts polymer granules into moulded/extruded packaging (containers, films, bags, pouches) on power-hungry machines against B2B purchase orders, earning a conversion margin per kg over the granule and power cost.
Turnover · metro₹7.99 Cr
Turnover · non-metro₹4.52 Cr
EBITDA · metro₹55.91 L
Photographs alone±98%
Full instrument stack±11% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Converted output / hr | Observed | 80 | 120 | 165 | kg/hr |
| Productive hrs / day | Claim | 16 | 20 | 22 | hr |
| Machine utilisation | Derived | 0.52 | 0.65 | 0.78 | fraction |
| Realisation / kg converted | Claim | 135 | 160 | 195 | ₹/kg |
| Operating days / yr | Claim | 300 | 320 | 340 | days |
Registry: gm [0.15, 0.2, 0.26] · opex [0.1, 0.13, 0.17] · DIO 42d · DSO 58d · DPO 42d · η 3
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.40 L |
| Rent/mo · non-metro | ₹22,000 / ₹50,000 / ₹95,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill + Udyam in firm name, no lease → owned shed; substitute plant/mould-tooling collateral note for rent
Balance-sheet build
Inventory = RM (granules, price-linked to crude) + WIP (on-machine + printing/lamination stages) + FG (finished packaging awaiting despatch); DIO worksheet overrides benchmark; receivables high 45–70d (B2B credit, buyer concentration); payables 30–50d to granule suppliers; fixed assets = injection/extrusion machines, moulds & dies (specialised, part-collateral), chillers, DG set
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 wip_floor 6 dispatch 7 utility_meter 8 production_register 9 gst_board 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
Injection/extruder shot/output counter Observed
Cycle counter × part weight (or extruder throughput) fixes kg/hr.
Monthly kWh vs connected load External
Injection/extrusion energy per kg fixes running utilisation (eta proxy).
B2B purchase order + tax invoice External
₹/kg conversion realisation by product.
Machine shift / job-card register Claim
Running hours per machine across shifts.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills | granule inward & finished dispatch → converted tonnage and B2B buyer count | strong |
| GST GSTR-1/3B | declared turnover vs output × price; concentrated B2B buyers | strong |
| Electricity bill | connected load + kWh → machine utilisation and eta | strong |
| AA bank feed | granule purchases (large lumpy outflows) vs staggered B2B receipts → WC cycle | medium |
| Rental / Udyam | owned-vs-rented, deposit, capacity registration | medium |
Activity signals
| Footfall | n/a (B2B converter) |
| B2B / counterparties | Buyer GSTIN count on GSTR-1 (often a few large FMCG/industrial accounts → concentration risk) and granule-supplier GSTINs on inward e-way; dispatch register cartons/day and machine counters bound daily output |
Variance path: Photographs alone put annual turnover within ±98% at 90% coverage in the metro band and ±104% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: E-way bills (granule inward & finished dispatch → converted tonnage and B2B buyer count); GST GSTR-1/3B (declared turnover vs output × price); Electricity bill (connected load + kWh → machine utilisation and eta).
Converts cotton/blended fibre into yarn on spindles and yarn into grey fabric on looms, running machines near-continuously across shifts; earns a value-addition margin per kg/metre where power and depreciation are the dominant costs.
Turnover · metro₹16.29 Cr
Turnover · non-metro₹10.22 Cr
EBITDA · metro₹97.75 L
Photographs alone±75%
Full instrument stack±10% · tier A
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Yarn/fabric-equiv output / hr | Observed | 80 | 110 | 145 | kg/hr |
| Productive hrs / day | Claim | 18 | 22 | 23.5 | hr |
| Spindle/loom utilisation | Derived | 0.68 | 0.8 | 0.9 | fraction |
| Realisation / kg | Claim | 220 | 255 | 295 | ₹/kg |
| Operating days / yr | Claim | 310 | 330 | 350 | days |
Registry: gm [0.15, 0.19, 0.24] · opex [0.1, 0.13, 0.16] · DIO 55d · DSO 52d · DPO 40d · η 4.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹1.20 L / ₹2.50 L / ₹5.00 L |
| Rent/mo · non-metro | ₹50,000 / ₹1.10 L / ₹2.20 L |
| Deposit → BS asset | 6 months |
Owned signal: HT connection + Udyam + property tax in firm name and no lease → owned mill; substitute plant-and-machinery/land collateral note for rent
Balance-sheet build
Inventory = RM (cotton/fibre bales, price-volatile) + WIP (bobbins/beams on machines, sizeable given long cycle) + FG (yarn cones/grey fabric); DIO worksheet overrides benchmark; receivables 45–60d (trade credit); payables 30–45d to fibre suppliers; fixed assets = spindles, looms, humidification, DG set, HT infra (major collateral)
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 rm_silo 6 wip_floor 7 dispatch 8 utility_meter 9 production_register 10 gst_board 11 pukka_invoice 12 licence 13 udyam
Next-photo evidence · Eq 7
Installed spindle/loom count + machine nameplate Observed
Spindle/loom count × rated speed sets the output ceiling.
HT power bill: contract demand + monthly kWh External
Power is the strongest run-rate signal; kWh vs connected load fixes utilisation.
3-shift attendance / production board Claim
Confirms continuous multi-shift running hours.
Yarn/fabric sale invoice sample External
Count/quality-wise ₹/kg realisation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Electricity/HT power bill | contract demand + kWh → machine run-rate, utilisation, eta (dominant signal) | strong |
| GST GSTR-1/3B | declared turnover vs output × price; yarn-count mix | strong |
| E-way bills | cotton/fibre inward & yarn/fabric outward → throughput and B2B counterparty count | strong |
| AA bank feed | receipts vs invoiced sales; power-bill autodebit corroborates load | medium |
| Udyam / factory licence | installed capacity band, worker count, registration | medium |
Activity signals
| Footfall | n/a (B2B mill) |
| B2B / counterparties | Distinct buyer GSTINs on GSTR-1 (traders/garment units) and fibre-supplier GSTINs on inward e-way bills; dispatch register bales/day bounds output and reconciles with power-derived run-rate |
Variance path: Photographs alone put annual turnover within ±75% at 90% coverage in the metro band and ±83% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Electricity/HT power bill (contract demand + kWh → machine run-rate, utilisation, eta (dominant signal)); GST GSTR-1/3B (declared turnover vs output × price); E-way bills (cotton/fibre inward & yarn/fabric outward → throughput and B2B counterparty count).
Stocks a very deep SKU range of OEM and aftermarket parts, sells over the counter and on credit to garages/mechanics; earns on aftermarket margin, capital is tied up in slow-moving deep inventory and garage receivables.
Turnover · metro₹1.92 Cr
Turnover · non-metro₹88.70 L
EBITDA · metro₹21.15 L
Photographs alone±156%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 500 | 900 | 1600 | ₹ |
| Transactions / day | Observed | 30 | 60 | 100 | count |
| Operating days / yr | Claim | 345 | 356 | 362 | days |
Registry: gm [0.18, 0.23, 0.28] · opex [0.09, 0.12, 0.15] · DIO 110d · DSO 35d · DPO 42d · η 0.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹7,000 / ₹16,000 / ₹35,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned shop/godown; substitute fixed-asset/collateral note (premises + racking) for rent
Balance-sheet build
inventory dominates BS = deep bin-rack SKU worksheet (Observed) overrides benchmark DIO, very high days-on-hand + dead-stock write-down risk; receivables material = garage credit 25–45d (DSO worksheet); payables = distributor credit 30–50d; fixed assets = racking, counters, delivery 2W
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 storage 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 credit_ledger 12 udyam
Next-photo evidence · Eq 7
Counter bill/challan sequence sample Observed
Bill/challan serials across a day fix transaction count including garage credit slips UPI misses.
Fast/slow part price sample Observed
Mix of low-value consumables vs high-value assemblies constrains blended ticket.
Garage/mechanic credit ledger Claim
Trade receivables khata bounds DSO and B2B share of sales.
Bin-rack deep-stock worksheet Observed
Racked SKU depth and dead-stock proportion set inventory value and days-on-hand.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-invoice | declared turnover & inbound part purchases (concordance) | strong |
| UPI/QR settlement | cash-counter banked turnover; credit sales excluded (residual via ledger) | medium |
| AA bank feed | garage receipts → receivables realisation & ageing | medium |
| Electricity bill | low load (retail/storage) → owned/rented; power cost minor | medium |
| Rental agreement | shop rent + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (morning garage-supply rush, afternoon) → footfall curve; cross-check vs bill sequence, not UPI (heavy credit) |
| B2B / counterparties | count garage/fleet counterparties from GSTR-1 + credit ledger; recurring accounts bound throughput |
Variance path: Photographs alone put annual turnover within ±156% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 + e-invoice (declared turnover & inbound part purchases (concordance)).
Buys footwear by size/style matrix and sells at high retail mark-up to walk-ins; profit swings with season and end-of-season discount cycles, with size-curve and style-ageing inventory risk.
Turnover · metro₹1.50 Cr
Turnover · non-metro₹68.16 L
EBITDA · metro₹25.56 L
Photographs alone±152%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 600 | 1000 | 1800 | ₹ |
| Transactions / day | Observed | 22 | 42 | 75 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.3, 0.37, 0.44] · opex [0.16, 0.2, 0.25] · DIO 82d · DSO 2d · DPO 40d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹95,000 / ₹2.00 L |
| Rent/mo · non-metro | ₹10,000 / ₹24,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute fixed-asset/collateral note (frontage, fit-out) for rent
Balance-sheet build
inventory = wall + back-store box worksheet (Observed) overrides benchmark DIO, size-curve/style ageing → markdown risk; near-zero receivables (cash/UPI); payables = brand/distributor credit 30–45d; fixed assets = wall racks, seating, mirrors, POS, AC
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 footfall_timed 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
POS/Z-report day-total Observed
Bill count on a normal day constrains transactions; flag discount-week uplift separately.
Shoe-box MRP price sample Observed
Wall-display price range constrains blended ticket and net-of-discount realisation.
Wall + back-store box worksheet Observed
Box count by size/style sets inventory value and flags broken size-curve dead stock.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked retail turnover vs photo-derived; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived; seasonal filing pattern (concordance) | strong |
| Electricity bill | lighting/AC load → floor size sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) — material opex line | strong |
Activity signals
| Footfall | 3 timed captures (weekday evening, weekend, sale week) → footfall curve with seasonality; cross-check vs POS + UPI count |
| B2B / counterparties | n/a — B2C retail |
Variance path: Photographs alone put annual turnover within ±152% at 90% coverage in the metro band and ±157% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR + card settlement (banked retail turnover vs photo-derived); GST 3B/GSTR-1 (declared turnover vs photo-derived); Rental agreement (rent expense + deposit (BS) — material opex line).
Buys readymade apparel on seasonal cycles and sells at 30–45% mark-up to walk-in retail; profit swings with festival/wedding peaks and end-of-season discounting, with style/ageing inventory risk.
Turnover · metro₹1.93 Cr
Turnover · non-metro₹93.19 L
EBITDA · metro₹25.13 L
Photographs alone±150%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 700 | 1200 | 2200 | ₹ |
| Transactions / day | Observed | 25 | 45 | 80 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.3, 0.36, 0.42] · opex [0.18, 0.23, 0.28] · DIO 85d · DSO 3d · DPO 40d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹50,000 / ₹1.10 L / ₹2.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹60,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute fixed-asset/collateral note (frontage, fit-out) for rent
Balance-sheet build
inventory = rack + back-stock unit worksheet (Observed) overrides benchmark DIO, style/season ageing → markdown risk on carried stock; near-zero receivables (cash/UPI); payables = supplier credit 30–45d; fixed assets = display fixtures, mannequins, trial rooms, AC, POS
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 footfall_timed 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
POS/Z-report day-total Observed
Bill count on a normal day constrains transactions; note festival multiplier separately.
Garment MRP tag sample Observed
Rack tag prices across segments constrain blended average ticket and discount depth.
Rack + back-stock unit worksheet Observed
Hanging + shelved unit count sets inventory value and flags ageing/off-season stock.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked retail turnover vs photo-derived; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived; seasonal filing pattern (concordance) | strong |
| Electricity bill | AC/lighting load → floor size sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) — large opex line | strong |
Activity signals
| Footfall | 3 timed captures (weekday evening, weekend, festival week) → footfall curve capturing seasonality; cross-check vs POS + UPI count and trial-room turnover |
| B2B / counterparties | n/a — B2C retail; wholesale offtake rare |
Variance path: Photographs alone put annual turnover within ±150% at 90% coverage in the metro band and ±165% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR + card settlement (banked retail turnover vs photo-derived); GST 3B/GSTR-1 (declared turnover vs photo-derived); Rental agreement (rent expense + deposit (BS) — large opex line).
Mixed counter-retail to walk-ins and credit trade sales to contractors/plumbers; earns on hardware and sanitaryware margin plus paint-tinting service, carries bulky slow-moving stock and contractor receivables.
Turnover · metro₹3.51 Cr
Turnover · non-metro₹1.69 Cr
EBITDA · metro₹24.60 L
Photographs alone±150%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 1200 | 2200 | 4000 | ₹ |
| Transactions / day | Observed | 25 | 45 | 75 | count |
| Operating days / yr | Claim | 345 | 355 | 362 | days |
Registry: gm [0.14, 0.17, 0.21] · opex [0.08, 0.1, 0.13] · DIO 65d · DSO 30d · DPO 35d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned shop/godown; substitute fixed-asset/collateral note (premises + racking) for rent
Balance-sheet build
inventory = bulky godown stock worksheet (Observed) overrides benchmark DIO, high value tied in slow-moving tiles/sanitaryware; receivables material = contractor credit 20–40d (DSO worksheet); payables = brand/distributor credit 25–40d; fixed assets = racking, tinting machine, delivery tempo, godown
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 credit_ledger 12 udyam
Next-photo evidence · Eq 7
Counter bill/challan sequence sample Observed
Serial bill numbers across a day fix true transaction count including credit challans a UPI feed misses.
Paint/sanitaryware price-list sample Observed
High-value sanitaryware vs low-value hardware mix constrains blended average ticket.
Contractor credit ledger / khata Claim
Trade receivables book bounds DSO and B2B share vs cash counter sales.
Godown bulky-stock worksheet Observed
Pipe/tile/cement stacks and slow movers set inventory value and days-on-hand.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-way bills | declared turnover & bulky inbound movement (concordance) | strong |
| UPI/QR settlement | cash-counter banked turnover; credit sales excluded (residual via ledger) | medium |
| AA bank feed | contractor cheque/RTGS receipts → receivables realisation | medium |
| Electricity bill | connected load (tinting machine) → power cost; owned/rented | medium |
| Rental agreement | shop + godown rent + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (morning trade rush, evening retail) → footfall curve; cross-check vs bill sequence, not UPI (much sold on credit) |
| B2B / counterparties | count contractor counterparties from GSTR-1 + e-way bill consignees; credit ledger names bound B2B throughput |
Variance path: Photographs alone put annual turnover within ±150% at 90% coverage in the metro band and ±157% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 + e-way bills (declared turnover & bulky inbound movement (concordance)).
Sells gold/silver ornaments where metal is near-pass-through and real income is making/wastage charges, hallmarking and old-gold exchange spread; very high ticket, low footfall, and enormous capital locked in metal inventory.
Turnover · metro₹40.85 L
Turnover · non-metro₹28.59 L
EBITDA · metro₹8.17 L
Photographs alone±129%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 5590.1699 | 10062.3059 | 17888.5438 | ₹ |
| Transactions / day | Observed | 0.6506 | 1.3012 | 2.6023 | count |
| Operating days / yr | Claim | 300 | 312 | 360 | days |
Registry: gm [0.23, 0.26, 0.3] · opex [0.051, 0.06, 0.069] · DIO 28.5d · DSO 6d · DPO 15d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.40 L / ₹3.00 L |
| Rent/mo · non-metro | ₹4,250 / ₹6,000 / ₹7,000 |
| Deposit → BS asset | 10 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned (common for established jewellers); substitute fixed-asset/collateral note (premises, safe, gold stock) for rent
Balance-sheet build
inventory dominates the entire balance sheet = vault gold/silver weight worksheet (grams × karat × live rate, Observed) far overrides benchmark DIO; portion may be gold-loan financed (metal payable) — separate owned vs borrowed metal; receivables small (booking/scheme); scheme advances are a liability; fixed assets = safe/vault, CCTV, secured display, hallmark/assay kit
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 assay_kit 7 safe_vault 8 qr_code 9 gst_board 10 utility_meter 11 licence 12 udyam
Next-photo evidence · Eq 7
Making/wastage-charge bill sample Observed
Bill shows metal value + making charge split — constrains ticket and true gross margin (making charge, not metal, is income).
Hallmark/HUID sales register External
BIS HUID per sold piece gives an auditable ornament count/day that footfall cannot (very low txns).
Vault gold-stock weight worksheet Observed
Grams by karat × rate sets the dominant inventory value and days-on-hand — the balance-sheet driver.
Advance/monthly-scheme deposit book Claim
Customer scheme advances (a liability) and booking receivables adjust the trade cycle.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-invoice | declared turnover vs photo-derived; metal vs making split (concordance) | strong |
| BIS hallmark/HUID records | hallmarked-piece count → txns floor & authenticity | strong |
| UPI/card + AA bank feed | banked high-ticket receipts; scheme advances; cash share = residual | strong |
| Gold-loan / metal-account statement | metal borrowed vs owned → true inventory financing & collateral | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (weekend, wedding/festival window) → footfall curve; cross-check vs HUID sales log, not walk-ins (browsing far exceeds buying) |
| B2B / counterparties | old-gold exchange and bullion counterparties in GSTR-1; wholesale/karigar job-work flows on challan |
Variance path: Photographs alone put annual turnover within ±129% at 90% coverage, from this trade's own assessed quartile spread on 33 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 + e-invoice (declared turnover vs photo-derived); BIS hallmark/HUID records (hallmarked-piece count → txns floor & authenticity); UPI/card + AA bank feed (banked high-ticket receipts).
Thin per-item markup on fast-moving staples (atta, oil, pulses, packaged FMCG) earned back through very high daily transaction frequency, mostly cash/UPI, on distributor credit.
Turnover · metro₹69.07 L
Turnover · non-metro₹48.35 L
EBITDA · metro₹6.91 L
Photographs alone±116%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average basket | Claim | 131.1094 | 182.0963 | 247.651 | ₹ |
| Transactions / day | Observed | 68.1797 | 105.3686 | 161.152 | count |
| Operating days / yr | Claim | 304.8 | 360 | 365 | days |
Registry: gm [0.1, 0.13, 0.16] · opex [0.0255, 0.03, 0.0345] · DIO 10.8d · DSO 2d · DPO 20d · η 0.4
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹45,000 / ₹80,000 |
| Rent/mo · non-metro | ₹3,000 / ₹3,000 / ₹5,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note (shop-cum-godown) for rent
Balance-sheet build
inventory = shelf + back-stock worksheet (Observed) overrides benchmark DIO; near-zero receivables (cash/UPI, small khata book); payables = distributor credit 15–30d; fixed assets = shelving, one/two refrigerators, weighing scale, POS/QR
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 qr_code 7 utility_meter 8 gst_board 9 udyam 10 rental_agreement 11 pukka_invoice 12 kacha_bill 13 footfall_timed
Next-photo evidence · Eq 7
POS / day-book Z-total Observed
Daily bill count pins transactions/day directly.
Shelf-price & basket photo Observed
Sampled shelf prices constrain the average basket.
Weekly-off / festival board Claim
Confirms weekly-off and closures → operating days.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | txns & ticket → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Electricity bill | connected load → floor/refrigeration sanity; owned vs rented; power cost | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (morning re-stock lull, evening 6–9pm peak, weekend) → footfall curve; cross-check vs UPI txn count and Z-report |
| B2B / counterparties | n/a — pure B2C; occasional local tea-stall/tiffin re-seller is immaterial |
Variance path: Photographs alone put annual turnover within ±116% at 90% coverage, from this trade's own assessed quartile spread on 78 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 20.9% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: UPI/QR settlement (txns & ticket → banked turnover); GST 3B/GSTR-1 (declared turnover vs photo-derived (concordance)).
Sells high-value handsets and electronics on thin metal-margin, earns real profit on accessories, extended warranty, activation and financier/EMI commissions; footfall low but ticket high.
Turnover · metro₹7.73 Cr
Turnover · non-metro₹3.83 Cr
EBITDA · metro₹23.20 L
Photographs alone±117%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 8000 | 12000 | 18000 | ₹ |
| Transactions / day | Observed | 10 | 18 | 30 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.08, 0.11, 0.14] · opex [0.06, 0.08, 0.1] · DIO 38d · DSO 5d · DPO 24d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹10,000 / ₹25,000 / ₹50,000 |
| Deposit → BS asset | 8 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute fixed-asset/collateral note (frontage, glass display) for rent
Balance-sheet build
inventory = serialised handset + accessory stock worksheet (Observed) overrides benchmark DIO, high unit value, obsolescence write-down risk on old models; receivables small (EMI settled by financier, some corporate credit); payables = distributor/brand credit 15–30d; fixed assets = display counters, security shutters, CCTV, POS/EMI terminal
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 footfall_timed 12 udyam
Next-photo evidence · Eq 7
Financier/EMI activation register External
Financier console + activation slips fix true handset units/day (footfall alone undercounts high-ticket sales).
Sealed-box price / IMEI display Observed
Model mix on display constrains average ticket across handset tiers vs accessories.
GST purchase-register stock worksheet External
Serialised inbound e-invoices bound live inventory value and days-on-hand for fast-obsolescing SKUs.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked turnover vs photo-derived; EMI down-payments; cash share = residual | strong |
| GST 3B/GSTR-1 + e-invoice | declared turnover & serialised handset purchases (concordance) | strong |
| Financier/EMI statement | financed-unit count → txns floor; commission income | strong |
| Electricity bill | connected load → floor size & display power; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday evening, weekend afternoon, festival) → footfall curve; cross-check vs POS + EMI activation count, not walk-ins (many browse, few buy) |
| B2B / counterparties | minor B2B (bulk/corporate handset orders) visible in GSTR-1 counterparty list |
Variance path: Photographs alone put annual turnover within ±117% at 90% coverage in the metro band and ±124% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR + card settlement (banked turnover vs photo-derived); GST 3B/GSTR-1 + e-invoice (declared turnover & serialised handset purchases (concordance)); Financier/EMI statement (financed-unit count → txns floor).
Grows saplings, ornamentals and flowering plants on-site and sells them retail alongside pots, soil, seeds and garden services; own-production keeps COGS low so gross margin is high, but land, water, labour and plant mortality carry the cost, and demand is strongly seasonal.
Turnover · metro₹42.88 L
Turnover · non-metro₹17.06 L
EBITDA · metro₹8.58 L
Photographs alone±179%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 180 | 320 | 600 | ₹ |
| Sales / day | Observed | 20 | 40 | 75 | count |
| Operating days / yr | Claim | 300 | 335 | 355 | days |
Registry: gm [0.38, 0.48, 0.58] · opex [0.22, 0.28, 0.35] · DIO 75d · DSO 10d · DPO 15d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹20,000 / ₹40,000 / ₹80,000 |
| Rent/mo · non-metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Deposit → BS asset | 4 months |
Owned signal: land record / no lease deed + proprietor-name electricity (pump) bill → owned land; substitute a land fixed-asset/collateral note for rent — land is the principal security here
Balance-sheet build
inventory = living-plant worksheet by stage (seedling/growing/ready) with mortality haircut overrides benchmark DIO — long grow cycle inflates DIO; receivables = landscaping/institutional credit (DSO 10–20d); payables = seed/pot/fertiliser credit, short; fixed assets = land (or lease), polyhouse/shadenet, irrigation & pump, potting shed — land dominates collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 grow_area 4 display 5 storage 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
Grow-area / polyhouse extent photo Observed
Bed/polyhouse area and plant count set saleable throughput → sales/day.
Plant & pot price-tag sample Observed
Spread from seedling to specimen plant constrains the average sale.
Season / event-order note Claim
Monsoon-planting & festival peaks vs summer lean set effective operating days.
Timed footfall / vehicle capture Observed
Weekend car footfall bounds walk-in sales/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | retail plant sales → banked turnover; event/landscaping jobs partly cash/cheque | medium |
| GST 3B/GSTR-1 (if registered) | declared turnover; many nurseries are unregistered (nursery produce partly exempt) → concordance weaker | medium |
| Electricity/water (pump) bill | irrigation load & area sanity; owned-vs-leased land | medium |
| Land record / lease deed | tenure, area, and collateral value of land | strong |
Activity signals
| Footfall | weekend and evening timed captures (plus car/parking counts, as buyers arrive by vehicle) → footfall curve; UPI covers retail, so footfall + grow-area carry event/bulk volume |
| B2B / counterparties | partial B2B — landscaping contracts, corporate/event floral supply, municipal plantation orders on credit; count institutional payers from GSTR-1 & bank credits → bounds the DSO tail and lumpier revenue |
Variance path: Photographs alone put annual turnover within ±179% at 90% coverage in the metro band and ±192% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Land record / lease deed (tenure, area, and collateral value of land).
Micro-margin, ultra-high-frequency counter sales of pan, cigarettes, gutka, snacks and cold drinks from a tiny footprint; almost entirely cash with a small UPI tail, negligible stock depth.
Turnover · metro₹19.66 L
Turnover · non-metro₹13.76 L
EBITDA · metro₹5.90 L
Photographs alone±125%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 15 | 25 | 45 | ₹ |
| Sales / day | Observed | 158.4 | 252 | 374.4 | count |
| Operating days / yr | Claim | 300 | 312 | 340.8 | days |
Registry: gm [0.33, 0.36, 0.4] · opex [0.051, 0.06, 0.069] · DIO 5.7d · DSO 0d · DPO 6d · η 0.12
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Rent/mo · non-metro | ₹4,000 / ₹6,000 / ₹6,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + own gumti/attached-to-shop → treat as owned/nil-rent; collateral negligible
Balance-sheet build
inventory = display + one shelf of cartons (DIO ~2 weeks on shelf-stable tobacco; betel leaf daily) worksheet overrides benchmark; nil receivables; payables = distributor credit ~1 week; fixed assets = kiosk, glass display, one fridge, hanging racks
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 price_board 5 qr_code 6 utility_meter 7 udyam 8 rental_agreement 9 kacha_bill 10 footfall_timed
Next-photo evidence · Eq 7
Cigarette/pan-masala restock slip Observed
Packets/cartons restocked per week bound sticks/pouches sold → sales/day.
Counter rate-card photo Observed
Item price mix constrains the tiny average sale.
Timed footfall capture Observed
Peak-hour counter queue bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | small banked share; cash dominates and must be residual | weak |
| Restock / purchase slips | cigarette & pan-masala inflow → volume floor, gm sanity | strong |
| Electricity bill | tiny load (light + one fridge) → premises sanity | weak |
| Udyam | registration & vintage | medium |
Activity signals
| Footfall | 2–3 timed captures (office in/out, late-evening) → footfall curve; UPI badly under-counts so restock slips + footfall carry volume |
| B2B / counterparties | n/a — B2C counter only |
Variance path: Photographs alone put annual turnover within ±125% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Restock / purchase slips (cigarette & pan-masala inflow → volume floor, gm sanity).
Sells prescription and OTC medicines, and FMCG/wellness, at regulated margins under a drug licence; higher ticket and deep, expiry-managed inventory, cold-chain for biologics, and a slice of credit sales to regulars and institutions.
Turnover · metro₹98.29 L
Turnover · non-metro₹68.80 L
EBITDA · metro₹14.74 L
Photographs alone±101%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average bill | Claim | 171.0736 | 272.1625 | 404.3558 | ₹ |
| Bills / day | Observed | 62.2646 | 103.7742 | 159.1205 | count |
| Operating days / yr | Claim | 328.8 | 348 | 360 | days |
Registry: gm [0.16, 0.2, 0.24] · opex [0.0425, 0.05, 0.0575] · DIO 18.6d · DSO 8d · DPO 35d · η 0.55
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹95,000 |
| Rent/mo · non-metro | ₹9,000 / ₹16,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset/collateral note (shop + cold-chain kit) for rent
Balance-sheet build
inventory = shelf + rack + fridge worksheet with expiry ageing (returns to stockist) overrides DIO; receivables = institutional & khata credit (DSO 5–15d); payables = stockist credit 30–45d giving a favourable trade cycle; fixed assets = racking, refrigerator/cold-chain, billing PC, AC, CCTV
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 storage 6 cold_room 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 licence 12 udyam 13 rental_agreement 14 pukka_invoice 15 footfall_timed
Next-photo evidence · Eq 7
Billing-software Z-report Observed
Chemist billing software gives an exact daily bill count and value.
Sample bills / GST invoices Observed
Prescription bill values constrain the average ticket and margin mix.
Refrigerator / cold-chain photo Observed
Fridge depth (insulin/vaccine stock) proxies chronic-refill footfall → bills/day.
Timed footfall capture Observed
Post-OPD evening rush bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | txns & ticket → banked turnover; low cash share expected | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Distributor invoices / e-way | drug purchases → COGS, regulated gm, DIO depth | strong |
| Drug licence (Form 20/21) + pharmacist reg. | legitimacy, scope, and continuity of operation | strong |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed captures near clinic/OPD close and evening → footfall curve; strong UPI/card coverage means POS reconciles footfall well |
| B2B / counterparties | partial B2B — supplies to nearby clinics/nursing homes on credit; count institutional payers from GSTR-1 & bank credits → bounds the DSO tail |
Variance path: Photographs alone put annual turnover within ±101% at 90% coverage, from this trade's own assessed quartile spread on 13 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR + card settlement (txns & ticket → banked turnover); GST 3B/GSTR-1 (declared turnover vs photo-derived (concordance)); Distributor invoices / e-way (drug purchases → COGS, regulated gm, DIO depth).
Buys general merchandise (apparel, footwear, household, gifting) at trade discount and sells at MRP-linked markup; fewer but larger tickets than a kirana, slower stock turns, some seasonal peaks.
Turnover · metro₹60.48 L
Turnover · non-metro₹42.34 L
EBITDA · metro₹7.26 L
Photographs alone±96%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average bill | Claim | 182.9271 | 274.3906 | 396.342 | ₹ |
| Bills / day | Observed | 39.3599 | 65.5999 | 104.9598 | count |
| Operating days / yr | Claim | 288 | 336 | 364 | days |
Registry: gm [0.16, 0.2, 0.25] · opex [0.068, 0.08, 0.092] · DIO 10.1d · DSO 4d · DPO 28d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.00 L |
| Rent/mo · non-metro | ₹3,400 / ₹5,000 / ₹7,000 |
| Deposit → BS asset | 6 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset/collateral note (shop) for rent
Balance-sheet build
inventory = rack + back-stock worksheet with ageing (seasonal dead-stock haircut) overrides DIO; modest receivables (card float, small credit); payables = supplier credit 30–45d; fixed assets = fixtures, trial rooms, signage, POS, AC
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 storage 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
POS Z-report / bill book Observed
Daily bill count pins transactions/day.
Price-tag & MRP sample Observed
Range of tagged prices constrains average bill and markup.
Timed footfall capture Observed
Weekend-vs-weekday footfall bounds conversion to bills.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | txns & ticket → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Purchase invoices / e-way | COGS & trade discount → gm sanity; stock inflow | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed exterior captures (weekday evening, Saturday, Sunday) → footfall curve; conversion-to-bill ratio cross-checks POS/UPI count |
| B2B / counterparties | n/a — B2C; small institutional/bulk gifting orders visible in GSTR-1 if present |
Variance path: Photographs alone put annual turnover within ±96% at 90% coverage, from this trade's own assessed quartile spread on 85 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 26.6% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: UPI/QR + card settlement (txns & ticket → banked turnover); GST 3B/GSTR-1 (declared turnover vs photo-derived (concordance)).
Blends low-margin stationery retail (books, paper, pens) with high-margin photocopy, print, lamination and DTP service income; strongly seasonal around exams and the school reopening cycle.
Turnover · metro₹33.66 L
Turnover · non-metro₹16.09 L
EBITDA · metro₹4.04 L
Photographs alone±137%
Full instrument stack±11% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 50 | 85 | 150 | ₹ |
| Transactions / day | Observed | 70 | 120 | 190 | count |
| Operating days / yr | Claim | 300 | 330 | 350 | days |
Registry: gm [0.24, 0.3, 0.38] · opex [0.14, 0.18, 0.23] · DIO 55d · DSO 5d · DPO 22d · η 1.1
Occupancy — owned vs rented
| Rent/mo · metro | ₹15,000 / ₹30,000 / ₹55,000 |
| Rent/mo · non-metro | ₹5,000 / ₹10,000 / ₹18,000 |
| Deposit → BS asset | 4 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset note (copiers + shop) for rent
Balance-sheet build
inventory = stationery shelves + paper/toner stock worksheet (seasonal build-up before school reopen) overrides DIO; small receivables (institutional print credit); payables = distributor credit 15–30d; fixed assets = photocopiers, printers, laminator, binding & DTP PC — high depreciation, consumables (toner/paper) a live opex line; elevated eta reflects copier power draw
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 machinery 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
Photocopier page-counter reading Observed
Machine lifetime/period page count converts to copy jobs → service transactions/day.
Service & item rate board Observed
Copy/print/lamination rates plus stationery prices constrain average sale.
Exam / school-reopen calendar note Claim
Peak months vs lean months set effective operating-day weighting.
Timed footfall capture Observed
After-school / office-hour rush bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | service & retail txns → banked turnover; small-value cash residual | strong |
| Electricity bill | copier/printer load is high → service intensity proxy; owned vs rented | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed captures after school hours and around exam season → footfall curve; copier page-counter is the strongest single activity signal, cross-checked vs UPI |
| B2B / counterparties | partial B2B — bulk DTP/print & institutional copy jobs on credit; count recurring office/coaching payers from bank credits → bounds service revenue |
Variance path: Photographs alone put annual turnover within ±137% at 90% coverage in the metro band and ±130% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (service & retail txns → banked turnover); Electricity bill (copier/printer load is high → service intensity proxy).
Buys perishable produce daily at the mandi on cash and sells same-day at a volume markup; margin is real but eroded by spoilage and weight loss, so near-zero inventory days and very high transaction frequency at a tiny ticket.
Turnover · metro₹20.58 L
Turnover · non-metro₹14.40 L
EBITDA · metro₹5.14 L
Photographs alone±68%
Full instrument stack±10% · tier A
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 21.6119 | 37.049 | 58.661 | ₹ |
| Sales / day | Observed | 99.1756 | 165.2927 | 251.245 | count |
| Operating days / yr | Claim | 300 | 336 | 360 | days |
Registry: gm [0.28, 0.31, 0.35] · opex [0.051, 0.06, 0.069] · DIO 8.6d · DSO 0d · DPO 2d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹35,000 |
| Rent/mo · non-metro | ₹3,500 / ₹4,000 / ₹5,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + municipal hawking licence or own frontage → treat as owned/nil-rent; collateral is negligible (stock is perishable)
Balance-sheet build
inventory ≈ one day's stock only (perishable) → DIO near-zero; nil receivables; payables ≈ nil (mandi is cash) so no trade-cycle cushion; fixed assets = weighing scale, crates, thela/stall, tarpaulin — spoilage carried as an opex line, not inventory
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 price_board 5 qr_code 6 utility_meter 7 weighbridge 8 udyam 9 rental_agreement 10 kacha_bill 11 footfall_timed
Next-photo evidence · Eq 7
Mandi purchase slip (kacha) Observed
Daily kg bought less spoilage bounds sellable volume → sales/day.
Chalkboard rate photo Observed
Per-kg rates × typical 0.5–1kg buy constrain average sale.
Timed footfall capture Observed
Morning and evening rush counts bound sales/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | banked share of sales; heavy cash residual expected | medium |
| Mandi purchase slips | daily COGS & volume → turnover floor; spoilage estimate | strong |
| Electricity bill | minimal load (lights/fan) → premises sanity; owned/rented | weak |
| Municipal hawking licence | legitimacy of pitch + fixed pitch fee | medium |
Activity signals
| Footfall | 2 timed captures (7–9am, 6–9pm) capture the twin produce rushes → footfall curve; UPI mostly under-counts, so footfall + mandi slip carry the volume estimate |
| B2B / counterparties | n/a — B2C walk-in; small supply to local eateries may show as recurring UPI payers |
Variance path: Photographs alone put annual turnover within ±68% at 90% coverage, from this trade's own assessed quartile spread on 48 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 30.8% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: Mandi purchase slips (daily COGS & volume → turnover floor).
Buys mixed scrap (metal, paper, plastic) by weight from pickers/households mostly in cash and sells sorted material to larger recyclers on a thin per-kg spread; profit rides on weighed tonnage and the blended rate, and cash intensity is inherent.
Turnover · metro₹36.48 L
Turnover · non-metro₹25.54 L
EBITDA · metro₹9.12 L
Photographs alone±164%
Full instrument stack±11% · tier A
Revenue drivers · metro tonnage/day × blended ₹/kg × operating days
| Driver | Class | lo | base | hi | |
|---|
| Tonnage / day | Observed | 0.3477 | 0.7646 | 1.3903 | t |
| Blended price | Benchmark | 9.5711 | 13.2523 | 16.9335 | ₹/kg |
| Operating days / yr | Claim | 264 | 360 | 365 | days |
Registry: gm [0.28, 0.31, 0.35] · opex [0.051, 0.06, 0.069] · DIO 8d · DSO 12d · DPO 5d · η 0.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Rent/mo · non-metro | ₹4,000 / ₹9,000 / ₹18,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity in dealer's name + no rental agreement → owned yard/shop; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory low, fast-clearing (DIO ~8d) = measured pile worksheet; receivables = short recycler credit (DSO ~12d); payables tiny — pickers paid cash (DPO ~5d); CASH-HEAVY so reconcile bank sales vs cash buys carefully; fixed assets = platform scale/kanta, cutter/baler, small tempo
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 weighbridge 5 storage 6 price_board 7 machinery 8 gst_board 9 utility_meter 10 qr_code 11 kacha_bill 12 pukka_invoice
Next-photo evidence · Eq 7
Weighing-scale / kanta slips Observed
Removes the dominant tonnage spread — daily weighed intake.
Rate board + material-mix photo Observed
Material mix fixes the blended ₹/kg.
Onward sale invoice to recycler External
Realised sale rate cross-checks blended price + banked share.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Onward-sale invoices (to recyclers) | outward sold weight × rate → turnover; realised blended price | strong |
| AA bank feed | banked receipts from recyclers vs cash purchases from pickers — cash intensity gap | strong |
| GST 3B/GSTR-1 | declared turnover vs weighed-throughput estimate (concordance) | medium |
| Electricity bill | cutter/baler load → processing sanity; owned/rented | medium |
Activity signals
| Footfall | timed exterior captures of picker/handcart drop-offs at peak morning hours give a soft intake curve |
| B2B / counterparties | buy-side is fragmented cash pickers (hard to enumerate); sell-side = a few recyclers from onward invoices + GSTR-1 → those bound sold tonnage |
Variance path: Photographs alone put annual turnover within ±164% at 90% coverage, from this trade's own assessed quartile spread on 13 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Onward-sale invoices (to recyclers) (outward sold weight × rate → turnover); AA bank feed (banked receipts from recyclers vs cash purchases from pickers — cash intensity gap).
Earns a professional fee (≈ 6–12% of project cost) on a pipeline of projects billed across milestones; billable-staff capacity caps throughput and receivables run long.
Turnover · metro₹1.10 Cr
Turnover · non-metro₹37.50 L
EBITDA · metro₹14.30 L
Photographs alone±162%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Projects billed / month | Claim | 1 | 2 | 3.5 | projects |
| Avg fee / project (₹, ~6–12% of cost) | Claim | 300000 | 500000 | 900000 | ₹ |
| Billable months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.7, 0.78, 0.85] · opex [0.56, 0.65, 0.72] · DIO 1d · DSO 85d · DPO 25d · η 0.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in principal's name + no rental agreement → owned studio; substitute a fixed-asset/collateral note (office + workstations) for rent
Balance-sheet build
near-zero inventory (DIO≈1); receivables run long (milestone billing, retention) → high DSO drives WCR; payables = outsourced structural/MEP consultants 20–30d; fixed assets = workstations, plotters, software licences, models
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 portfolio_board 5 engagement_letter 6 fee_schedule 7 receivables_ageing 8 licence 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Signed engagement letters / proposals External
Counts live projects and contracted fee; tightens throughput.
Fee schedule / percentage-of-cost slab Observed
Fixes avg fee per project against project-cost band.
Receivables ageing / invoice ledger External
Confirms billed value and long DSO for working-capital sizing.
Billable-staff seating count Observed
Headcount caps concurrent projects (capacity ceiling).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | milestone collections vs billed fee; smooths lumpy inflows | strong |
| GST 3B/GSTR-1 | declared professional receipts vs photo-derived; GSTR-1 counterparty count = client count | strong |
| Municipal/sanctioned-drawing submissions | independent project count (external register) | medium |
| Rental agreement | office rent + deposit (BS) | medium |
| Electricity bill | office load sanity; owned/rented | weak |
Activity signals
| Footfall | n/a — appointment-based studio, no walk-in footfall to curve |
| B2B / counterparties | estimate client count from GSTR-1 counterparties, engagement-letter file and sanctioned-drawing register; billable headcount bounds concurrent projects |
Variance path: Photographs alone put annual turnover within ±162% at 90% coverage in the metro band and ±180% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (milestone collections vs billed fee); GST 3B/GSTR-1 (declared professional receipts vs photo-derived).
Sells stylist time on a fixed number of chairs — service tickets (cut/colour/facial) plus retail product upsell — through booked appointments and walk-ins.
Turnover · metro₹18.72 L
Turnover · non-metro₹13.10 L
EBITDA · metro₹5.62 L
Photographs alone±77%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients served / day (chairs × turns) | Observed | 11.0422 | 17.6676 | 26.5014 | count |
| Average service ticket | Claim | 203.7625 | 339.6042 | 577.3271 | ₹ |
| Operating days / yr | Claim | 300 | 312 | 360 | days |
Registry: gm [0.5, 0.58, 0.66] · opex [0.238, 0.28, 0.33] · DIO 30d · DSO 2d · DPO 18d · η 1.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹3,000 / ₹3,750 / ₹5,750 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory = retail products + colour/consumable stock worksheet (Observed) overrides DIO; near-zero receivables (cash/UPI); payables = product distributor credit 15–30d; fixed assets = chairs, mirrors, geysers, AC, dryers, product cabinet
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 chair_station 5 price_board 6 display 7 appointment_register 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Chair/station photo (installed capacity) Observed
Fixes seating capacity, capping clients/day.
Appointment/booking register (7-day) Observed
Actual bookings + walk-ins tighten daily throughput.
Service menu / rate card Observed
Menu mix constrains average service ticket.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | clients & fee → banked service revenue; cash tips/walk-ins = residual | strong |
| GST 3B/GSTR-1 | declared service turnover vs photo-derived (concordance) | medium |
| Electricity bill | connected load (dryers/AC/geysers) → chair count sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS asset) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday evening, weekend peak, mid-morning) → chair-utilisation curve; cross-check vs UPI txn count |
| B2B / counterparties | minor — occasional bridal/event packages; otherwise B2C |
Variance path: Photographs alone put annual turnover within ±77% at 90% coverage, from this trade's own assessed quartile spread on 63 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 74.0% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: UPI/QR settlement (clients & fee → banked service revenue).
Charges recurring compliance retainers plus one-time audit/tax/filing fees across a client book; revenue is seasonal (audit and tax peaks) and leverages articled/junior staff.
Turnover · metro₹61.60 L
Turnover · non-metro₹22.27 L
EBITDA · metro₹9.86 L
Photographs alone±158%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients billed / month | Claim | 40 | 70 | 120 | clients |
| Avg fee / client-month (retainer + filing) | Claim | 4500 | 8000 | 15000 | ₹ |
| Effective billing months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.72, 0.8, 0.88] · opex [0.55, 0.64, 0.72] · DIO 0d · DSO 55d · DPO 20d · η 0.16
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹65,000 / ₹1.30 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in proprietor/firm name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables moderate (retainers prompt, audit/tax fees billed after work → 30–75d) → seasonal DSO; payables = subcontracted audits/DSC/portal fees 15–25d; fixed assets = computers, software, library, office fit-out; rent deposit is a BS asset
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 engagement_letter 5 fee_schedule 6 licence 7 receivables_ageing 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Client / filing register (portal login count) External
GST/ITR/ROC filing count on portal fixes active client book.
Fee schedule / engagement letters External
Fixes avg retainer + filing fee per client.
Articled/junior-staff seating count Observed
Staff leverage caps clients serviceable in peak season.
Receivables ageing schedule External
Confirms billed value and seasonal DSO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | retainer + fee collections vs billed; monthly retainer credits confirm recurring base | strong |
| GST 3B/GSTR-1 | declared professional receipts vs photo-derived | strong |
| GST/Income-tax e-filing portal | independent count of filings handled = client-book proxy | strong |
| Rental agreement | office rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — appointment/office based; seasonal peaks (Jul tax, Sep–Nov audit) shape the monthly band |
| B2B / counterparties | estimate client book from portal filing counts, GSTR-1 counterparties and engagement-letter file; staff headcount bounds peak-season throughput |
Variance path: Photographs alone put annual turnover within ±158% at 90% coverage in the metro band and ±160% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (retainer + fee collections vs billed); GST 3B/GSTR-1 (declared professional receipts vs photo-derived); GST/Income-tax e-filing portal (independent count of filings handled = client-book proxy).
Large batches enrolled into term/annual programmes across classrooms; revenue = enrolled students × average monthly-equivalent fee × session months, with faculty salaries the heaviest cost.
Turnover · metro₹1.65 Cr
Turnover · non-metro₹55.00 L
EBITDA · metro₹26.40 L
Photographs alone±162%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Enrolled students (classrooms × batch size) | Claim | 150 | 300 | 550 | students |
| Avg monthly-equivalent fee / student | Claim | 3000 | 5000 | 8500 | ₹ |
| Programme months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.44, 0.54, 0.62] · opex [0.3, 0.38, 0.45] · DIO 3d · DSO 12d · DPO 8d · η 1.5
Occupancy — owned vs rented
| Rent/mo · metro | ₹80,000 / ₹1.80 L / ₹4.00 L |
| Rent/mo · non-metro | ₹25,000 / ₹55,000 / ₹1.20 L |
| Deposit → BS asset | 6 months |
Owned signal: commercial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
near-zero inventory (study material only, DIO ~3d); instalment/term fees give moderate receivables (DSO ~10–12d) while advance term fees sit as DEFERRED INCOME (liability); payables small; fixed assets = classroom furniture, projectors, AC, servers/LMS
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 classroom 5 enrolment_register 6 batch_timetable 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Admission / enrolment register (term) Observed
Term admissions tighten enrolled-student count.
Classroom count & seating photo Observed
Classrooms × seats × shifts cap enrolment.
Course fee structure board / brochure Observed
Programme-wise fees constrain blended monthly-equivalent fee.
Faculty roster / timetable Claim
Faculty count × batches cross-checks batch throughput and cost base.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| AA bank feed | lump-sum term-fee receipts → admissions count; advance fees → deferred income | strong |
| GST 3B/GSTR-1 | declared coaching turnover vs students×fee estimate (concordance) | strong |
| UPI/QR settlement | instalment fee inflows → collection cadence | medium |
| Electricity bill | multi-classroom load (AC/projectors) → capacity sanity; owned/rented | medium |
Activity signals
| Footfall | 3 timed exterior captures at shift changeovers (morning/evening batches, weekend) → batch-occupancy curve; cross-check vs admission register |
| B2B / counterparties | school/college tie-ups & bulk programmes — estimate from bulk GSTR-1 invoices |
Variance path: Photographs alone put annual turnover within ±162% at 90% coverage in the metro band and ±182% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: AA bank feed (lump-sum term-fee receipts → admissions count); GST 3B/GSTR-1 (declared coaching turnover vs students×fee estimate (concordance)).
Bills billable people at a rate across a mix of monthly retainers and fixed-fee projects; almost no inventory, but corporate clients pay slowly so receivables dominate the balance sheet.
Turnover · metro₹52.10 L
Turnover · non-metro₹36.47 L
EBITDA · metro₹13.03 L
Photographs alone±262%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Active engagements / month | Claim | 0.0793 | 0.1585 | 0.2774 | engagements |
| Avg monthly billing / engagement | Claim | 55904.9286 | 97833.625 | 174702.9018 | ₹ |
| Billable months / yr | Benchmark | 308.4 | 336 | 343.2 | months |
Registry: gm [0.72, 0.8, 0.88] · opex [0.4675, 0.55, 0.6325] · DIO 0d · DSO 80d · DPO 25d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹1.00 L / ₹2.00 L |
| Rent/mo · non-metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in firm/partner's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables are the dominant asset (30–90d corporate terms) → high DSO drives WCR; payables = subcontractors/associates 20–30d; fixed assets = laptops, software, office fit-out; deposits (rent) held as BS asset
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 engagement_letter 5 fee_schedule 6 receivables_ageing 7 qr_code 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Retainer agreements / SOWs on file External
Counts active engagements and contracted monthly value.
Rate card / MSA billing rates Observed
Fixes blended billing rate per engagement.
Timesheet / utilisation report Claim
Billable headcount × utilisation caps concurrent engagements.
Receivables ageing schedule External
Confirms billed value and long DSO for WCR.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | retainer + project collections vs billed; steady retainer credits confirm recurring base | strong |
| GST 3B/GSTR-1 | declared service receipts vs photo-derived; GSTR-1 counterparty count = client count | strong |
| TDS 26AS / Form 16A | client-side TDS credits corroborate billed fees | medium |
| Rental agreement | office rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — B2B office, no walk-in footfall |
| B2B / counterparties | estimate client count from GSTR-1 counterparties, TDS deductor list and SOW file; billable headcount × utilisation bounds throughput |
Variance path: Photographs alone put annual turnover within ±262% at 90% coverage, from this trade's own assessed quartile spread on 9 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (retainer + project collections vs billed); GST 3B/GSTR-1 (declared service receipts vs photo-derived).
Runs assays on samples — tests/day × average test price — driven by walk-ins plus doctor/hospital referrals; reagent-and-equipment heavy with NABL/quality overhead.
Turnover · metro₹84.48 L
Turnover · non-metro₹34.80 L
EBITDA · metro₹15.21 L
Photographs alone±192%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Tests / day | Observed | 40 | 80 | 150 | tests |
| Average test price | Claim | 150 | 300 | 600 | ₹ |
| Operating days / yr | Claim | 340 | 352 | 362 | days |
Registry: gm [0.46, 0.56, 0.64] · opex [0.3, 0.38, 0.45] · DIO 30d · DSO 35d · DPO 35d · η 3.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹35,000 / ₹70,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Deposit → BS asset | 6 months |
Owned signal: commercial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (analyzers + premises) for rent
Balance-sheet build
reagent/kit inventory + cold-chain consumables (DIO ~25–30d) via kit worksheet; HIGH receivables from doctor/hospital/TPA referrals & insurance (DSO ~28–35d) unlike cash-only services; reagent-supplier payables 30–35d; fixed assets = analyzers, centrifuges, refrigeration — equipment-heavy collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 assay_kit 6 sample_collection 7 test_register 8 price_board 9 nabl_certificate 10 qr_code 11 utility_meter 12 gst_board 13 pukka_invoice 14 udyam
Next-photo evidence · Eq 7
Test register / LIS day-count Observed
Logged tests/day directly constrain throughput.
Analyzer/equipment photo (capacity & menu) Observed
Installed analyzers cap daily test capacity and test menu.
Test rate list / price board Observed
Test-mix rate card constrains average test price.
Reagent/kit purchase invoice Observed
Reagent cost per test bounds margin and cross-checks volume.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared diagnostic turnover vs tests×price estimate; B2B referral invoices (concordance) | strong |
| AA bank feed | TPA/insurance & hospital settlements → receivables ageing (DSO) | strong |
| e-way / reagent purchase invoices | reagent/kit inbound → test-volume floor & COGS | medium |
| Electricity bill | analyzer + cold-chain load → capacity sanity; owned/rented | medium |
Activity signals
| Footfall | 2 timed exterior captures at collection hours (morning fasting-sample peak, evening) → sample-intake curve; cross-check vs test register |
| B2B / counterparties | doctor/hospital referrals & TPA — estimate counterparty count from GSTR-1 B2B invoices; drives the higher DSO |
Variance path: Photographs alone put annual turnover within ±192% at 90% coverage in the metro band and ±188% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared diagnostic turnover vs tests×price estimate); AA bank feed (TPA/insurance & hospital settlements → receivables ageing (DSO)).
Wins project-based events, quotes a lump-sum event value, takes 50–70% advance, and delivers via subcontracted labour and bought/rented material (flowers, fabric, lighting, catering pass-through); income is the retained margin over subcontract and consumable cost.
Turnover · metro₹1.65 Cr
Turnover · non-metro₹60.00 L
EBITDA · metro₹23.10 L
Photographs alone±98%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Events / month | Claim | 4.443 | 6 | 8.076 | count |
| Avg event value | Claim | 161758 | 250000 | 431674 | ₹ |
| Active months / yr | Claim | 9.962 | 11 | 11.5 | months |
Registry: gm [0.3, 0.38, 0.46] · opex [0.18, 0.24, 0.3] · DIO 10d · DSO 25d · DPO 30d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹50,000 / ₹1.00 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 5 months |
Owned signal: electricity bill in proprietor's name + owned godown, no rental agreement → owned; substitute fixed-asset/collateral note for rent
Balance-sheet build
owned reusable decor assets (lighting, drapes, props, structures) = fixed assets and collateral; consumables (flowers, disposables) small dio ~10d; receivables = balance-on-completion (dso ~25d) offset by 50–70% customer advances (liability, often negative net WC pre-event); payables = subcontracted labour & material vendors 25–30d
Guided capture — photo order
1 exterior 2 interior 3 storage 4 display 5 portfolio_album 6 qr_code 7 gst_board 8 utility_meter 9 rental_agreement 10 advance_ledger 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Signed event contracts (last 6) Observed
Fixes avg event value from actual quotes/orders.
Forward booking calendar Claim
Counts events/month incl. wedding-season peaks.
Advance-receipt ledger External
Confirms active months & advance-funded working capital.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared event turnover vs photo-derived events×value; input credits on subcontract/material | strong |
| Account-Aggregator bank feed | lumpy advance inflows + vendor/labour outflows → project cash cycle & margin | strong |
| UPI/QR settlement | advance & balance collections vs booking count | medium |
| Rental agreement | office/godown rent + deposit (BS) | medium |
| Electricity bill | godown load; owned/rented | weak |
Activity signals
| Footfall | n/a — no walk-in footfall; scene is godown/prop-stock condition, not counter traffic |
| B2B / counterparties | deal-count driven — count events from booking calendar + GSTR-1 B2B invoices (corporate/venue clients); average deal size × frequency bounds throughput; wedding/festival months carry 2–3× the off-season rate |
Variance path: Photographs alone put annual turnover within ±98% at 90% coverage in the metro band and ±85% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared event turnover vs photo-derived events×value); Account-Aggregator bank feed (lumpy advance inflows + vendor/labour outflows → project cash cycle & margin).
Recurring membership subscriptions — active members × average monthly fee — against a fixed floor and equipment set; low direct cost, high fixed rent and trainer payroll.
Turnover · metro₹57.50 L
Turnover · non-metro₹14.72 L
EBITDA · metro₹11.50 L
Photographs alone±114%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Active members | Claim | 150 | 250 | 420 | members |
| Average monthly membership fee | Claim | 1400 | 2000 | 3200 | ₹ |
| Billing months / yr | Benchmark | 11 | 11.5 | 12 | months |
Registry: gm [0.75, 0.82, 0.88] · opex [0.55, 0.62, 0.7] · DIO 6d · DSO 2d · DPO 12d · η 2.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹15,000 / ₹35,000 / ₹70,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (equipment + premises) for rent
Balance-sheet build
subscription model — near-zero inventory (only supplement stock); advance/annual fees create DEFERRED INCOME (liability) so DSO is low (2–5d) not high; payables minimal; fixed assets = cardio/strength equipment (major), AC, flooring, sound — equipment doubles as collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 member_register 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Active-member register / app dashboard Observed
Active (non-lapsed) member count is the core driver.
Membership plan/rate board (monthly/quarterly/annual) Observed
Plan mix constrains blended monthly fee.
Equipment-floor photo (station count & area) Observed
Floor + equipment cap sustainable active membership.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | recurring fee inflows → active-member count & renewal cadence | strong |
| AA bank feed | monthly subscription pattern; advance (quarterly/annual) receipts → deferred income | strong |
| GST 3B/GSTR-1 | declared subscription turnover vs member×fee estimate | medium |
| Electricity bill | high connected load (AC + machines) → floor-size sanity; owned/rented | medium |
Activity signals
| Footfall | 3 timed exterior captures (early-morning peak, evening peak, weekend) → check-in curve; cross-check vs member register & UPI renewals |
| B2B / counterparties | corporate wellness tie-ups — estimate from bulk-invoice count in GSTR-1 |
Variance path: Photographs alone put annual turnover within ±114% at 90% coverage in the metro band and ±140% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (recurring fee inflows → active-member count & renewal cadence); AA bank feed (monthly subscription pattern).
Sells professional time/expertise as billable engagements at a per-client fee from a modest office; income is fee less low direct cost and office overhead — used as a catch-all when no specific profile fits.
Turnover · metro₹16.50 L
Turnover · non-metro₹6.93 L
EBITDA · metro₹3.30 L
Photographs alone±83%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients / month | Claim | 19.6 | 25 | 36.1 | count |
| Avg fee / engagement | Claim | 4576 | 6000 | 10431 | ₹ |
| Active months / yr | Claim | 10.7 | 11 | 11.3 | months |
Registry: gm [0.55, 0.7, 0.85] · opex [0.35, 0.5, 0.65] · DIO 0d · DSO 45d · DPO 8d · η 0.06
Occupancy — owned vs rented
| Rent/mo · metro | ₹20,000 / ₹45,000 / ₹90,000 |
| Rent/mo · non-metro | ₹6,000 / ₹14,000 / ₹28,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/home-office; substitute fixed-asset/collateral note for rent
Balance-sheet build
no inventory (dio 0); receivables material (dso ~45d — professionals invoice and wait); payables minimal; fixed assets = office fit-out, computers, books/software; goodwill/personal-brand not on BS but supports durability note
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 appointment_register 6 qr_code 7 gst_board 8 utility_meter 9 rental_agreement 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
Appointment/client register (3 mo) Claim
Bounds clients/month; wide fallback prior needs this first.
Issued fee invoices sample Observed
Fixes avg engagement fee across service types.
Active-months + 26AS/GST cross-check External
Bounds active months/yr; corroborate with receipts.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | professional-receipt credits → fee realisation & client frequency (primary anchor for a thin catch-all) | strong |
| GST 3B/GSTR-1 or 26AS/TDS | declared professional turnover vs photo-derived clients×fee (concordance) | strong |
| UPI/QR settlement | small-fee collections vs client count; cash residual | medium |
| Rental agreement | office rent + deposit (BS) | medium |
| Professional licence/registration | vintage, credential → income durability | medium |
Activity signals
| Footfall | low-frequency, high-value visits; appointment register + 2 timed captures preferred over exterior footfall |
| B2B / counterparties | mixed B2C/B2B; where clients are firms, count counterparties from GSTR-1/TDS to bound engagement volume |
Variance path: Photographs alone put annual turnover within ±83% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (professional-receipt credits → fee realisation & client frequency (primary anchor for a thin catch-all)); GST 3B/GSTR-1 or 26AS/TDS (declared professional turnover vs photo-derived clients×fee (concordance)).
Performs small paid jobs/services at a per-job charge with minimal premises and mostly cash collection; income is charge less basic material/consumable and labour — the most conservative catch-all when nothing else fits.
Turnover · metro₹24.90 L
Turnover · non-metro₹17.43 L
EBITDA · metro₹6.23 L
Photographs alone±84%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day | Claim | 16.4069 | 20.7529 | 29.3999 | count |
| Charge / job | Claim | 316 | 400 | 707 | ₹ |
| Operating days / yr | Claim | 292.8 | 300 | 340.8 | days |
Registry: gm [0.4, 0.55, 0.72] · opex [0.255, 0.3, 0.45] · DIO 3d · DSO 3d · DPO 5d · η 0.12
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹45,000 |
| Rent/mo · non-metro | ₹4,000 / ₹5,000 / ₹12,750 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + electricity bill in own name (or no dedicated meter for roadside) → owned/home-based; substitute fixed-asset/collateral note, or flag premises informality
Balance-sheet build
minimal owned inventory (dio ~3d consumables); near-zero receivables (cash on completion); negligible payables; fixed assets = basic tools/equipment & premises fit-out (thin, low collateral value); high informality → treat GST/bank gaps as Gap nodes, not zeros
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 qr_code 7 utility_meter 8 rental_agreement 9 kacha_bill 10 udyam
Next-photo evidence · Eq 7
Cash day-book / job tally (2 wk) Claim
Very wide prior — counts jobs/day; corroborate with UPI.
Charge/rate sample per job Observed
Fixes charge per job across the service mix.
30-day UPI settlement (count + active days) External
Bounds operating days & seasonality; anchors cash residual.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | job count & charge → banked collections (primary anchor); large cash residual expected | strong |
| Electricity bill | connected load/premises existence; owned/rented; often thin | medium |
| Udyam | existence & self-declared activity/vintage | weak |
| Rental agreement | rent + deposit if formal premises (often absent) | weak |
Activity signals
| Footfall | 3+ timed exterior captures (weekday, weekend, peak hour) → job-arrival curve; cross-check vs UPI count; expect high cash share the curve alone can't bank |
| B2B / counterparties | predominantly B2C cash; rare institutional jobs counted from any kacha-bill/challan stub if present |
Variance path: Photographs alone put annual turnover within ±84% at 90% coverage, from this trade's own assessed quartile spread on 12 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (job count & charge → banked collections (primary anchor)).
Earns commission = policies sold × avg premium × commission rate, plus a renewal-trail income on the existing book; near-zero COGS/inventory, revenue lands as insurer payouts into the bank.
Turnover · metro₹34.65 L
Turnover · non-metro₹12.10 L
EBITDA · metro₹7.62 L
Photographs alone±89%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Policies issued / month | Claim | 65.9 | 90 | 133 | policies |
| Avg commission / policy (premium × rate) | Claim | 2535 | 3500 | 5671 | ₹ |
| Active months / yr | Benchmark | 10.5 | 11 | 11.5 | months |
Registry: gm [0.82, 0.9, 0.96] · opex [0.58, 0.68, 0.78] · DIO 0d · DSO 35d · DPO 15d · η 0.14
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹6,000 / ₹14,000 / ₹28,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in agent's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables = commission accrued but not yet paid by insurer (15–45d) → DSO; payables = sub-agent commission payouts 10–20d; renewal trail is an off-balance recurring-income annuity noted for cash-flow stability; fixed assets minimal (office fit-out, computers)
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 policy_register 5 commission_statement 6 fee_schedule 7 renewal_register 8 licence 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Policy issuance register / portal count External
Counts policies issued/month from insurer portal or register.
Insurer commission statement External
Fixes avg commission per policy and confirms banked income.
Renewal-book / trail register External
Sizes recurring renewal-trail income (persistency).
AA bank inflows tagged to insurers External
Insurer credits reconcile total commission income independently.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | insurer commission credits → total income; the primary ground truth for a cashless-inventory model | strong |
| Insurer commission statements | commission per policy, rate mix and renewal trail | strong |
| GST 3B/GSTR-1 | commission is a taxable service; declared vs bank-derived income | strong |
| IRDAI agency licence / portal | authorised lines and active-agent status | medium |
| Rental agreement | office rent + deposit (BS) | weak |
Activity signals
| Footfall | n/a — advisory/commission model, no product footfall |
| B2B / counterparties | estimate volume from insurer-portal policy counts, commission statements and AA insurer-tagged inflows; sub-agent headcount indicates sourcing capacity |
Variance path: Photographs alone put annual turnover within ±89% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (insurer commission credits → total income); Insurer commission statements (commission per policy, rate mix and renewal trail); GST 3B/GSTR-1 (commission is a taxable service).
Earns a design fee plus a margin on materials and execution across turnkey projects; collects advances up front but carries project work-in-progress and material stock until handover.
Turnover · metro₹1.32 Cr
Turnover · non-metro₹48.00 L
EBITDA · metro₹15.84 L
Photographs alone±85%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Projects handed over / month | Claim | 1.15 | 1.5 | 2.25 | projects |
| Avg project value (fee + material margin) | Claim | 599951 | 800000 | 1300123 | ₹ |
| Active months / yr | Benchmark | 10.5 | 11 | 11.5 | months |
Registry: gm [0.3, 0.38, 0.46] · opex [0.2, 0.26, 0.32] · DIO 30d · DSO 40d · DPO 30d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹85,000 / ₹1.70 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹42,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in owner's name + no rental agreement → owned studio; substitute a fixed-asset/collateral note (studio + workshop) for rent
Balance-sheet build
inventory = material stock + project WIP worksheet (Observed) overrides benchmark DIO; receivables moderate (advances collected up front, retention/final at handover) → net DSO; payables = material vendors + contractors 25–40d; fixed assets = studio fit-out, samples, tools; customer advances sit as a BS liability offsetting WCR
Guided capture — photo order
1 exterior 2 interior 3 portfolio_board 4 project_wip 5 engagement_letter 6 fee_schedule 7 storage 8 pukka_invoice 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Signed project contracts + advance receipts External
Counts live projects and contracted value; advances confirm pipeline.
BOQ / rate card / fee schedule Observed
Fixes avg project value split (design fee vs material margin).
Material purchase invoices External
Sizes material margin (gm) and payables to vendors.
Project WIP / site-works register Observed
Confirms in-progress projects and WIP days for DIO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | advance + milestone inflows vs contracted value; advances net down receivables | strong |
| GST 3B/GSTR-1 | declared turnover (works-contract/service) vs photo-derived; input-credit on materials confirms COGS | strong |
| Material purchase invoices / e-way bills | material COGS, margin and DIO | medium |
| Rental agreement | studio rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — project-based; portfolio board and site photos evidence recent completions |
| B2B / counterparties | estimate active projects from contract file, e-way bills to site addresses and GSTR-1 counterparties; WIP register bounds concurrent execution |
Variance path: Photographs alone put annual turnover within ±85% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (advance + milestone inflows vs contracted value); GST 3B/GSTR-1 (declared turnover (works-contract/service) vs photo-derived).
A small counter turns over repair jobs — jobs/day × average repair value (parts + labour) — with a warranty/out-of-warranty mix; labour is high-margin, parts pass-through.
Turnover · metro₹36.23 L
Turnover · non-metro₹15.30 L
EBITDA · metro₹6.52 L
Photographs alone±167%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day | Observed | 8 | 15 | 25 | jobs |
| Average repair value (parts + labour) | Claim | 400 | 700 | 1500 | ₹ |
| Operating days / yr | Claim | 330 | 345 | 358 | days |
Registry: gm [0.4, 0.48, 0.56] · opex [0.24, 0.3, 0.36] · DIO 25d · DSO 4d · DPO 15d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹15,000 / ₹30,000 / ₹60,000 |
| Rent/mo · non-metro | ₹5,000 / ₹10,000 / ₹22,000 |
| Deposit → BS asset | 5 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
modest spare-parts + accessory inventory (DIO ~20–25d); mostly cash/UPI so low receivables except warranty-claim reimbursements (DSO ~3–4d, longer for ASC claims); parts-supplier payables 12–15d; fixed assets = soldering/testing benches, tools, display counter
Guided capture — photo order
1 exterior 2 neighbourhood 3 repair_counter 4 display 5 storage 6 job_card_register 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Repair job-ticket register (7-day) Observed
Intake tickets tighten daily job count.
Counter + accessory display photo Observed
Accessory/retail mix lifts and constrains average job value.
Spare-part purchase invoice sample Observed
Part cost vs charge fixes parts/labour split within repair value.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | repair payments → banked turnover; cash jobs = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs jobs×fee estimate (concordance) | medium |
| e-way / parts purchase invoices | spare-parts inbound → parts COGS & inventory | medium |
| Brand ASC agreement | authorised-service-centre warranty jobs → B2B receivable stream | medium |
Activity signals
| Footfall | 2 timed exterior captures (evening peak, weekend) → walk-in count; cross-check vs job-ticket register & UPI |
| B2B / counterparties | brand/ASC warranty reimbursements — estimate from claim invoices / GSTR-1 B2B lines |
Variance path: Photographs alone put annual turnover within ±167% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (repair payments → banked turnover).
Earns brokerage = deals closed × avg deal value × brokerage % (≈1–2% sale, ~1 month rent); income is lumpy and high-variance, landing as large irregular payments at closing.
Turnover · metro₹69.30 L
Turnover · non-metro₹19.25 L
EBITDA · metro₹15.25 L
Photographs alone±87%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Deals closed / month | Claim | 2.598 | 3.5 | 5.079 | deals |
| Avg brokerage / deal (value × %) | Claim | 134877 | 180000 | 301831 | ₹ |
| Active months / yr | Benchmark | 10.5 | 11 | 11.5 | months |
Registry: gm [0.82, 0.9, 0.96] · opex [0.55, 0.68, 0.8] · DIO 0d · DSO 25d · DPO 10d · η 0.14
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹7,000 / ₹15,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in broker's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables short (brokerage usually collected at/near closing, occasional 30d tail) → low DSO; payables = sub-broker splits 5–15d; income highly seasonal/lumpy so use a trailing-12-month average, not a spot month; fixed assets minimal (office, vehicles for site visits)
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 staff_seating 5 deal_register 6 commission_statement 7 fee_schedule 8 licence 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Deal register / agreement-to-sell records External
Counts closed deals/month; anchors the highest-variance driver.
Brokerage agreement / commission slab Observed
Fixes brokerage % and avg deal value band.
AA bank inflows (large irregular credits) External
Closing-brokerage credits reconcile realised income and smooth lumpiness.
Active-listing / mandate board Observed
Live mandates proxy pipeline that converts to deals.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | large irregular brokerage credits → realised income; the key smoother for a lumpy model | strong |
| GST 3B/GSTR-1 | brokerage is a taxable service; declared vs bank-derived income | strong |
| RERA agent portal | registered-agent status and (where filed) transaction linkage | medium |
| Rental agreement | office rent + deposit (BS) | weak |
Activity signals
| Footfall | n/a — deal-driven, not footfall-driven; walk-in enquiries are weak signal |
| B2B / counterparties | estimate deal flow from deal register, RERA linkage, GSTR-1 counterparties and AA large-credit clustering; sub-broker headcount indicates coverage |
Variance path: Photographs alone put annual turnover within ±87% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Account-Aggregator bank feed (large irregular brokerage credits → realised income); GST 3B/GSTR-1 (brokerage is a taxable service).
Doctor sells time as consultations (consults/day × fee) with a thin pharmacy/procedure/injectables add-on layered on top; revenue is mostly cash/UPI at point of care.
Turnover · metro₹17.68 L
Turnover · non-metro₹12.38 L
EBITDA · metro₹7.07 L
Photographs alone±19%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Consults / day | Observed | 10.7828 | 17.2525 | 23.7222 | patients |
| Avg consultation + add-on fee | Claim | 181.1512 | 284.6661 | 439.9385 | ₹ |
| Operating days / yr | Claim | 240 | 360 | 365 | days |
Registry: gm [0.55, 0.62, 0.68] · opex [0.187, 0.22, 0.253] · DIO 12d · DSO 8d · DPO 22d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹35,000 / ₹70,000 / ₹1.40 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in doctor's name + no rental agreement in Mitra pack → owned premises; substitute a fixed-asset/collateral note (clinic + equipment) for rent
Balance-sheet build
inventory = small drug/consumables stock worksheet (Observed) overrides benchmark DIO; low receivables (mostly cash/UPI; TPA/insurance panel adds a short tail → DSO); payables = pharma distributor credit 15–30d; fixed assets = diagnostic/procedure equipment, furniture, refrigeration for vaccines
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 licence 5 appointment_register 6 fee_schedule 7 display 8 storage 9 qr_code 10 gst_board 11 utility_meter 12 pukka_invoice 13 udyam
Next-photo evidence · Eq 7
Appointment/OP register day-count Observed
Counts patients seen/day directly; tightens the widest driver.
Consultation fee board / rate card Observed
Fixes base consultation fee; add-on inferred from pharmacy invoices.
UPI/QR settlement day-total External
Banked collections cross-check consult count × fee; cash share = residual.
Pharmacy/consumables purchase bill External
Sizes the dispensing add-on and pharmacy DIO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | consult count × fee → banked turnover; cash consults = residual | strong |
| GST 3B/GSTR-1 | declared receipts (pharmacy/procedures are taxable; pure consultation often exempt) vs photo-derived | medium |
| Pharmacy/distributor invoices | add-on COGS and dispensing DIO | medium |
| Electricity bill | connected load → equipment (lights, AC, steriliser) sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior captures (morning OP, evening OP, weekend) → patient-arrival curve; cross-check vs appointment register and UPI txn count |
| B2B / counterparties | n/a — B2C; TPA/insurance panel receipts (if empanelled) explain the small receivables tail |
Variance path: Photographs alone put annual turnover within ±19% at 90% coverage, from this trade's own assessed quartile spread on 9 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (consult count × fee → banked turnover).
Charges a per-garment stitching/alteration fee on customer-supplied fabric; income is labour value less thread/consumables and helper wages, held-fabric belongs to customers and is not owned inventory.
Turnover · metro₹17.16 L
Turnover · non-metro₹12.01 L
EBITDA · metro₹5.15 L
Photographs alone±100%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Garments / day | Observed | 5.9043 | 7.7576 | 10.7054 | count |
| Stitching charge / garment | Claim | 462.2021 | 635.6414 | 1113.2805 | ₹ |
| Operating days / yr | Claim | 289.2 | 348 | 363.6 | days |
Registry: gm [0.55, 0.65, 0.75] · opex [0.2975, 0.35, 0.4025] · DIO 3d · DSO 4d · DPO 5d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Rent/mo · non-metro | ₹4,000 / ₹5,000 / ₹5,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/home-based; substitute fixed-asset/collateral note for rent
Balance-sheet build
customer fabric held is NOT owned inventory (exclude from BS); owned dio ~3d = thread/lining/buttons only; near-zero receivables (paid on delivery, small advance common); payables minimal; fixed assets = sewing/overlock/interlock machines, iron & table, cabinets (primary collateral)
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 machinery 6 job_ticket 7 qr_code 8 utility_meter 9 rental_agreement 10 gst_board 11 udyam
Next-photo evidence · Eq 7
Order/job-ticket book tally Observed
Counts garments/day from pending job slips.
Stitching rate card / price board Observed
Fixes charge per garment by type (blouse/suit/alteration).
Delivery-date register Claim
Bounds working days incl. festive/wedding peaks.
Machine & workstation count Observed
Installed machines cap plausible daily throughput.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | garment count & charge → banked collections; cash residual | strong |
| Electricity bill | machine load → number of active machines/throughput sanity; owned/rented | medium |
| GST 3B / Udyam | declared turnover (often below-threshold/composition) & vintage | medium |
| Rental agreement | shop rent + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday, weekend, festive week) → job-intake curve; cross-check pending-rack count vs UPI collections |
| B2B / counterparties | mostly B2C; if job-work for boutiques/exporters, count institutional counterparties from delivery challans/GSTR-1 |
Variance path: Photographs alone put annual turnover within ±100% at 90% coverage, from this trade's own assessed quartile spread on 49 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 100.0% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: UPI/QR settlement (garment count & charge → banked collections).
Earns a commission/margin on gross bookings (air, hotel, holiday packages); collects customer advances before travel and remits to suppliers, so turnover recognised is gross transaction value while the retained margin is the true income.
Turnover · metro₹28.28 L
Turnover · non-metro₹19.80 L
EBITDA · metro₹5.66 L
Photographs alone±73%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Bookings / day | Observed | 1.9221 | 2.507 | 3.6852 | count |
| Avg gross ticket value | Claim | 2631.8269 | 3616.1403 | 6006.6501 | ₹ |
| Operating days / yr | Claim | 244.8 | 312 | 360 | days |
Registry: gm [0.23, 0.26, 0.3] · opex [0.051, 0.06, 0.069] · DIO 0d · DSO 7d · DPO 15d · η 0.05
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,500 / ₹7,500 / ₹9,500 |
| Rent/mo · non-metro | ₹3,500 / ₹5,000 / ₹8,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
no owned inventory (dio 0); receivables = corporate-account credit (dso ~7d) net against large customer-advance liability (negative operating WC in season); payables = supplier/consolidator credit 10–20d; fixed assets = office fit-out, computers, GDS terminal; float held for travel dates is a liability, not equity
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 qr_code 6 booking_register 7 gst_board 8 utility_meter 9 rental_agreement 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
GDS/PNR issuance summary (monthly) External
Airline/GDS PNR count bounds bookings/day.
Booking invoice sample (fare + commission) Observed
Fixes avg gross ticket value & retained margin.
12-month booking ledger (seasonality) Claim
Bounds effective operating days across peak/lean.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared commission/GTV turnover vs photo-derived bookings×ticket (concordance) | strong |
| Account-Aggregator bank feed | customer advances in / supplier remittances out → true retained margin & float | strong |
| UPI/QR settlement | card/UPI collections vs booking count; cash residual | medium |
| Rental agreement | office rent expense + deposit (BS) | medium |
| Udyam / IATA-agency licence | vintage, accreditation → supplier-credit access | medium |
Activity signals
| Footfall | walk-in bookings low; 2 timed interior captures (peak season week, lean week) cross-check counter activity vs PNR count |
| B2B / counterparties | corporate travel accounts material — count counterparties from GSTR-1 B2B invoices; large-value monthly settlements indicate corporate desk vs retail leisure mix |
Variance path: Photographs alone put annual turnover within ±73% at 90% coverage, from this trade's own assessed quartile spread on 41 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 63.2% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared commission/GTV turnover vs photo-derived bookings×ticket (concordance)); Account-Aggregator bank feed (customer advances in / supplier remittances out → true retained margin & float).
Small batches of students pay a monthly fee across the academic year; revenue = enrolled students × monthly fee × active months, tutor cost is the main variable expense.
Turnover · metro₹25.78 L
Turnover · non-metro₹18.04 L
EBITDA · metro₹10.31 L
Photographs alone±119%
Full instrument stack±11% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Enrolled students (batches × students) | Claim | 1.8885 | 3.3049 | 5.6655 | students |
| Average monthly fee / student | Claim | 1500 | 2500 | 4200 | ₹ |
| Active academic months / yr | Benchmark | 305.76 | 312 | 336 | months |
Registry: gm [0.52, 0.6, 0.68] · opex [0.17, 0.2, 0.23] · DIO 0d · DSO 6d · DPO 5d · η 0.8
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹55,000 |
| Rent/mo · non-metro | ₹3,000 / ₹7,000 / ₹15,000 |
| Deposit → BS asset | 4 months |
Owned signal: residential electricity tariff + no commercial rental agreement → home-run/owned; substitute a fixed-asset note (furniture only) for rent
Balance-sheet build
zero inventory; SEASONAL — revenue concentrated in ~10 academic months with a summer dip, so annualise on active months not 365d; small receivables from delayed monthly fees (DSO ~5–6d); negligible payables; fixed assets = benches, boards, fans
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 batch_timetable 5 enrolment_register 6 price_board 7 qr_code 8 utility_meter 9 udyam 10 pukka_invoice
Next-photo evidence · Eq 7
Enrolment / attendance register Observed
Names/heads across batches tighten enrolled-student count.
Batch timetable board (batches × slots) Observed
Number of batches × seat count caps enrolment.
Fee receipt book / fee slip Observed
Grade-wise slips constrain average monthly fee.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | monthly fee collections → enrolled-student count × fee | strong |
| AA bank feed | seasonal collection pattern (dip in summer break) → active months | medium |
| Electricity bill | residential vs commercial → premises type; small load | medium |
| Udyam | registered education/coaching activity; vintage | weak |
Activity signals
| Footfall | 2 timed exterior captures at batch changeover (after-school evening slots) → batch-size curve; cross-check vs enrolment register |
| B2B / counterparties | n/a (B2C parents) |
Variance path: Photographs alone put annual turnover within ±119% at 90% coverage, from this trade's own assessed quartile spread on 12 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: UPI/QR settlement (monthly fee collections → enrolled-student count × fee).
Workshop bays process repair/service jobs — bays × jobs/day × average job value (spares + labour) — constrained by bay count, equipment and mechanic availability.
Turnover · metro₹32.30 L
Turnover · non-metro₹22.61 L
EBITDA · metro₹6.46 L
Photographs alone±89%
Full instrument stack±10% · tier A
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day (bays × throughput) | Observed | 5.7729 | 8.6593 | 14.4321 | jobs |
| Average job value (spares + labour) | Claim | 717.3084 | 1195.514 | 2092.1495 | ₹ |
| Operating days / yr | Claim | 300 | 312 | 364 | days |
Registry: gm [0.34, 0.42, 0.5] · opex [0.187, 0.22, 0.253] · DIO 13.9d · DSO 18d · DPO 30d · η 2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹80,000 / ₹1.60 L |
| Rent/mo · non-metro | ₹5,000 / ₹6,000 / ₹6,000 |
| Deposit → BS asset | 6 months |
Owned signal: commercial/industrial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (equipment + land) for rent
Balance-sheet build
spares inventory material (DIO ~30–35d) via parts-rack worksheet; receivables from fleet/corporate & insurance jobs (DSO ~12–18d); spares-supplier payables 26–30d; fixed assets = lifts, compressors, diagnostic tools, welding — equipment as collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 workshop_bay 4 machinery 5 spares_rack 6 job_card_register 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Workshop bay & lift photo (capacity) Observed
Bays × lifts cap jobs/day.
Job-card register (7-day) Observed
Booked job cards tighten daily throughput.
Job invoice sample (spares vs labour split) Observed
Invoice mix constrains average job value and margin split.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared turnover + input credit on spares vs photo estimate (concordance) | strong |
| UPI/QR settlement | job payments → banked turnover; cash jobs = residual | strong |
| e-way / spares purchase invoices | spares inbound → parts throughput & COGS floor | medium |
| Electricity bill | compressor/lift load → bay-count sanity; owned/rented | medium |
Activity signals
| Footfall | 2 timed exterior captures (morning drop-off, evening pickup) → vehicle-in/out count; cross-check vs job-card register & UPI |
| B2B / counterparties | fleet/dealer AMC contracts drive receivables — estimate counterparties from GSTR-1 B2B invoices |
Variance path: Photographs alone put annual turnover within ±89% at 90% coverage, from this trade's own assessed quartile spread on 40 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 22.2% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared turnover + input credit on spares vs photo estimate (concordance)); UPI/QR settlement (job payments → banked turnover).
Operator owns/runs a small fleet of cars driven on aggregator apps, earning fare per trip; revenue = cars × trips/day × avg fare × utilisation, eaten into by fuel (dominant COGS), 20–25% aggregator commission, driver wages and heavy vehicle-loan EMI.
Turnover · metro₹37.98 L
Turnover · non-metro₹14.37 L
EBITDA · metro₹5.32 L
Photographs alone±87%
Full instrument stack±30% · tier C
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Cars in fleet | Observed | 2.839 | 4 | 6.322 | count |
| Trips / vehicle / day | Claim | 16.1 | 19 | 21.9 | count |
| Average fare / trip | Claim | 181 | 210 | 251 | ₹ |
| Utilisation (on-shift days) | Claim | 0.605 | 0.68 | 0.75 | × |
| Operating days / yr | Claim | 338 | 350 | 357 | days |
Registry: gm [0.55, 0.62, 0.68] · opex [0.42, 0.48, 0.53] · DIO 3d · DSO 7d · DPO 4d · η 0.05
Occupancy — owned vs rented
| Rent/mo · metro | ₹4,000 / ₹12,000 / ₹30,000 |
| Rent/mo · non-metro | ₹1,500 / ₹5,000 / ₹12,000 |
| Deposit → BS asset | 3 months |
Owned signal: electricity bill in proprietor's name for a residence/plot + no yard rental agreement → parked at owned premises; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Cars are the principal fixed asset AND collateral; matched by vehicle-loan liabilities (EMI a major fixed obligation, tracked via AA feed, not inside opex). Near-zero inventory (only spares/tyres, dio ≈ 3). Receivables short (aggregator settles T+1 to weekly, dso ≈ 6–7). Fuel payables small (dpo ≈ 4). Parking-yard deposit a minor BS asset.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 qr_code 6 licence 7 gst_board 8 udyam
Next-photo evidence · Eq 7
Aggregator earnings dashboard (weekly trips) External
Constrains trips/vehicle/day directly from platform data.
Fuel bills / FASTag km log External
Km run vs idle days → utilisation.
RC + taxi-permit count for fleet External
Fixes fleet size from registry.
Trip-receipt / fare sample Observed
Constrains average fare per trip.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Aggregator settlement statement | trips & fare → banked fare income; commission % visible | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed cars; net cash after EMI | strong |
| RC / permit registry | vehicles (fleet count) — collateral identification | strong |
| FASTag / fuel bills | util → km run vs idle; energy proxy (fuel, not grid) | medium |
| GST 3B/GSTR-1 | declared turnover vs fare-derived (concordance) | medium |
Activity signals
| Footfall | n/a — asset-utilisation business, not footfall; fleet count established via RC/permits, utilisation via trip logs + FASTag/fuel km |
| B2B / counterparties | largely B2C via aggregator; any corporate-tie-up trips visible in GSTR-1 counterparties |
Variance path: Photographs alone put annual turnover within ±87% at 90% coverage in the metro band and ±79% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Aggregator settlement statement (trips & fare → banked fare income); AA bank feed (EMI outflows) (vehicle-loan EMI count → number of financed cars); RC / permit registry (vehicles (fleet count) — collateral identification).
Operator runs commercial vehicles on hire or fixed contract (e.g. staff/school transport, project logistics, bulk carriage); revenue = vehicles × trips/day × realisation × utilisation, with fuel the dominant COGS plus toll, maintenance, driver wages and vehicle-loan EMI. Note: if the borrower is instead a commercial-vehicle DEALERSHIP, this is a retail archetype (unit sales × margin) — the fleet-operator model here does not apply.
Turnover · metro₹32.12 L
Turnover · non-metro₹22.49 L
EBITDA · metro₹6.42 L
Photographs alone±233%
Full instrument stack±30% · tier C
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Commercial vehicles in fleet | Observed | 2.7099 | 3.517 | 5.3324 | count |
| Trips / vehicle / day | Claim | 0.9017 | 1.0643 | 1.3085 | count |
| Avg realisation / trip | Claim | 3039.0713 | 3547.5541 | 4361.7177 | ₹ |
| Utilisation (deployed vs idle) | Claim | 0.661 | 0.72 | 0.765 | × |
| Operating days / yr | Claim | 300 | 336 | 360 | days |
Registry: gm [0.44, 0.5, 0.56] · opex [0.255, 0.3, 0.345] · DIO 6d · DSO 45d · DPO 12d · η 0.04
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹55,000 |
| Rent/mo · non-metro | ₹3,500 / ₹4,500 / ₹6,000 |
| Deposit → BS asset | 3 months |
Owned signal: no yard rental agreement + plot property tax / electricity bill in proprietor's name → owned depot; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Commercial vehicles are the principal fixed asset AND collateral, matched by vehicle-loan liabilities (EMI a major fixed obligation via AA feed, outside opex). Low inventory (spares/tyres, dio ≈ 5–6). Contract/hire receivables billed monthly drive WCR (dso ≈ 38–45). Fuel/toll payables modest (dpo ≈ 10–12). Depot deposit a minor BS asset if rented. (If a dealership instead: inventory of vehicles, floor-plan financing and DSO change entirely.)
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 licence 6 gst_board 7 udyam
Next-photo evidence · Eq 7
Trip sheet / contract log Observed
Constrains trips/vehicle/day.
FASTag crossings / odometer reading External
Km run vs idle → utilisation.
RC + permit count for fleet External
Fixes fleet size from registry.
Hire / contract & rate sample Observed
Constrains realisation per trip.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| FASTag / toll data | util & trips → deployment frequency and routes | strong |
| RC / permit registry | vehicles (fleet count) — collateral identification | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed vehicles | strong |
| Hire / contract agreements + GSTR-1 | fare realisation; contract counterparty count | strong |
| Fuel bills | fuel = dominant COGS; km run; energy proxy (diesel, not grid) | medium |
Activity signals
| Footfall | n/a — asset-utilisation; fleet count via RC/permits, utilisation via FASTag + odometer/fuel logs |
| B2B / counterparties | count hire/contract counterparties from agreements + GSTR-1 to bound deployment and receivable concentration |
Variance path: Photographs alone put annual turnover within ±233% at 90% coverage, from this trade's own assessed quartile spread on 48 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 13.2% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: FASTag / toll data (util & trips → deployment frequency and routes); RC / permit registry (vehicles (fleet count) — collateral identification); AA bank feed (EMI outflows) (vehicle-loan EMI count → number of financed vehicles).
A hub/franchise runs a fleet of bikes/vans with riders delivering parcels; revenue = riders × parcels/day × rate per parcel × utilisation — high utilisation but thin per-parcel margin, with rider wages, franchise/hub fee, fuel and maintenance the main costs.
Turnover · metro₹57.06 L
Turnover · non-metro₹23.12 L
EBITDA · metro₹4.56 L
Photographs alone±83%
Full instrument stack±30% · tier C
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Riders / delivery vehicles | Observed | 7.602 | 10 | 16 | count |
| Parcels / rider / day | Claim | 58 | 70 | 86 | count |
| Rate per parcel | Claim | 24 | 28 | 33.6 | ₹ |
| Utilisation (working riders/day) | Claim | 0.772 | 0.82 | 0.86 | × |
| Operating days / yr | Claim | 349 | 355 | 359 | days |
Registry: gm [0.4, 0.46, 0.52] · opex [0.33, 0.38, 0.43] · DIO 2d · DSO 22d · DPO 8d · η 0.08
Occupancy — owned vs rented
| Rent/mo · metro | ₹10,000 / ₹25,000 / ₹60,000 |
| Rent/mo · non-metro | ₹4,000 / ₹10,000 / ₹22,000 |
| Deposit → BS asset | 4 months |
Owned signal: hub premises usually rented — rental agreement + DISCOM bill in landlord/company name confirm rent; owned hub is rare (then use fixed-asset note)
Balance-sheet build
Bikes/vans are fixed assets and collateral where financed (van EMI via AA feed, outside opex); many bikes are rider-owned so fleet asset base can be light. Negligible inventory (dio ≈ 2). Receivables = COD float + corporate/franchise billing (dso ≈ 18–22). Payables short (dpo ≈ 7–8). Hub deposit a BS asset.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 qr_code 6 licence 7 gst_board 8 udyam
Next-photo evidence · Eq 7
Hub manifest / parcels-scanned report External
Constrains parcels/rider/day from scan data.
Rider roster / attendance sheet Observed
Active riders = delivery capacity.
Client rate card / franchise slab External
Constrains per-parcel rate.
Fuel bills / route-km log External
Route km vs idle → utilisation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Hub / franchise scan dashboard | trips (parcels) → daily volume; SLA and success rate | strong |
| Client / franchise rate card | fare → per-parcel realisation | strong |
| AA bank feed / franchise settlement | COD remittance, franchise fee, EMI on vans | strong |
| RC count (vans/bikes) | vehicles → fleet size / collateral | medium |
| Fuel bills | util & energy proxy (petrol, not grid); route km | medium |
| GST 3B/GSTR-1 | declared turnover vs volume-derived (concordance) | medium |
Activity signals
| Footfall | n/a — throughput business; capacity from rider roster, volume from hub scan manifests, utilisation from fuel/route km |
| B2B / counterparties | e-commerce / corporate client count from GSTR-1 counterparties bounds parcel volume and revenue concentration |
Variance path: Photographs alone put annual turnover within ±83% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Hub / franchise scan dashboard (trips (parcels) → daily volume); Client / franchise rate card (fare → per-parcel realisation); AA bank feed / franchise settlement (COD remittance, franchise fee, EMI on vans).
Operator owns trucks/tempos moving freight for businesses; revenue = vehicles × loads/day × avg freight realisation × utilisation, with fuel the dominant COGS and toll, maintenance, driver wages and vehicle-loan EMI as the main costs; long B2B freight receivables strain working capital.
Turnover · metro₹1.40 Cr
Turnover · non-metro₹51.33 L
EBITDA · metro₹27.95 L
Photographs alone±82%
Full instrument stack±30% · tier C
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Trucks / tempos in fleet | Observed | 3.899 | 5 | 7.569 | count |
| Loads / vehicle / day | Claim | 1.833 | 2.2 | 2.677 | count |
| Avg freight realisation / load | Claim | 4583 | 5500 | 6784 | ₹ |
| Utilisation (loaded, not empty-return) | Claim | 0.645 | 0.7 | 0.744 | × |
| Operating days / yr | Claim | 319 | 330 | 339 | days |
Registry: gm [0.48, 0.55, 0.6] · opex [0.3, 0.35, 0.4] · DIO 6d · DSO 55d · DPO 12d · η 0.04
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹50,000 |
| Rent/mo · non-metro | ₹3,000 / ₹8,000 / ₹18,000 |
| Deposit → BS asset | 3 months |
Owned signal: no yard rental agreement in pack + property tax / electricity bill for the plot in proprietor's name → owned yard; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Trucks/tempos are the principal fixed asset AND collateral, matched by vehicle-loan liabilities (EMI a major fixed obligation via AA feed, outside opex). Low inventory (spares/tyres, dio ≈ 5–6). Long B2B freight receivables dominate WCR (dso ≈ 45–55). Fuel/toll payables modest (dpo ≈ 10–12). Yard deposit a minor BS asset if rented.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 licence 6 gst_board 7 udyam
Next-photo evidence · Eq 7
Trip register / lorry receipts (LR) Observed
Constrains loads/vehicle/day.
FASTag toll-crossings log External
Trip frequency + empty-return → utilisation.
RC + national-permit count External
Fixes fleet size from registry.
Freight invoice / rate sample Observed
Constrains freight realisation per load.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| FASTag / toll data | util & trips → trip frequency, routes, empty-return share | strong |
| RC / national-permit registry | vehicles (fleet count) — collateral identification | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed trucks | strong |
| GST e-way bills / GSTR-1 | fare & throughput; number of B2B freight counterparties | strong |
| Fuel bills | fuel = dominant COGS; km run; energy proxy (diesel, not grid) | medium |
Activity signals
| Footfall | n/a — asset-utilisation; fleet count via RC/national permits, utilisation via FASTag crossings + fuel/km logs |
| B2B / counterparties | count B2B freight counterparties from e-way bills + GSTR-1 to bound throughput and receivable concentration |
Variance path: Photographs alone put annual turnover within ±82% at 90% coverage in the metro band and ±79% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: FASTag / toll data (util & trips → trip frequency, routes, empty-return share); RC / national-permit registry (vehicles (fleet count) — collateral identification); AA bank feed (EMI outflows) (vehicle-loan EMI count → number of financed trucks).
Rents refrigerated capacity (per MT or sq ft) to farmers/traders at a seasonal rate scaled by occupancy; revenue is capacity × rate × occupancy, but a very large power bill and seasonality make the electricity signal decisive.
Turnover · metro₹1.05 Cr
Turnover · non-metro₹65.20 L
EBITDA · metro₹25.20 L
Photographs alone±122%
Full instrument stack±11% · tier A
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Effective rent yield / sq ft / day (net of vacancy) | Claim | 1.2 | 1.5 | 1.85 | ₹ |
| Rentable cold capacity (chamber floor) | Observed | 10000 | 20000 | 40000 | sq ft |
| Operating days / yr | Claim | 320 | 350 | 365 | days |
Registry: gm [0.45, 0.58, 0.68] · opex [0.24, 0.34, 0.44] · DIO 0d · DSO 32d · DPO 15d · η 3.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹24 / ₹40 / ₹60 |
| Rent/mo · non-metro | ₹15 / ₹27 / ₹42 |
| Deposit → BS asset | 2 months |
Owned signal: high sanctioned load + property-tax receipt in owner's name and no lease-as-tenant → building + plant are owned; treat as fixed asset / collateral (major security), rent received is revenue
Balance-sheet build
no own inventory (DIO 0) — goods are depositors' (held on behalf, off-book); receivables = seasonal storage dues (DSO ~32d); the building + refrigeration plant are the dominant fixed assets and usual COLLATERAL (value plant separately, note refrigerant type/age); depositor advances are a liability
Guided capture — photo order
1 exterior 2 neighbourhood 3 cold_room 4 occupancy_gauge 5 utility_meter 6 machinery 7 rental_agreement 8 interior 9 gst_board 10 pukka_invoice 11 udyam 12 licence
Next-photo evidence · Eq 7
DISCOM bill (12-month kWh profile) External
Compressor load tracks occupied capacity — strongest signal; seasonality visible.
Chamber occupancy read (bags/pallets vs capacity) Observed
Occupancy % is the dominant yield driver.
Storage ledger / rental register Claim
Rate/MT × stored quantity → revenue directly.
Chamber capacity measurement (MT / sq ft) Observed
Fixes rentable cold capacity.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Electricity/DISCOM bill | kWh vs occupied capacity (eta ~3+) — decisive triangulation; reveals seasonality | strong |
| Storage rental ledger | rate/MT × stored qty × tenure → revenue | strong |
| GST 3B/GSTR-1 | rental/service turnover vs derived (concordance) | strong |
| AA bank feed | seasonal rent inflows; advance bookings vs capacity | medium |
Activity signals
| Footfall | n/a; inbound/outbound truck timing marks the fill (post-harvest) and draw-down cycle |
| B2B / counterparties | count depositors from storage ledger + GSTR-1; occupancy % = stored MT ÷ capacity is the key utilisation measure — peak-season fill vs annual average matters for a seasonal facility |
Variance path: Photographs alone put annual turnover within ±122% at 90% coverage in the metro band and ±136% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Electricity/DISCOM bill (kWh vs occupied capacity (eta ~3+) — decisive triangulation); Storage rental ledger (rate/MT × stored qty × tenure → revenue); GST 3B/GSTR-1 (rental/service turnover vs derived (concordance)).
Buys fast-moving branded stock on short company credit and pushes it to retailers via van/beat sales on a very thin distributor margin plus scheme income; profit is made on high inventory turn and disciplined retailer collections.
Turnover · metro₹4.04 Cr
Turnover · non-metro₹2.42 Cr
EBITDA · metro₹12.13 L
Photographs alone±87%
Full instrument stack±10% · tier A
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Dispatch value / sq ft / day | Claim | 27 | 33 | 41 | ₹ |
| Godown usable area | Observed | 2000 | 3500 | 5500 | sq ft |
| Operating days / yr | Claim | 330 | 350 | 362 | days |
Registry: gm [0.05, 0.07, 0.09] · opex [0.025, 0.04, 0.05] · DIO 15d · DSO 12d · DPO 14d · η 0.32
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹75,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity in proprietor's name + no lease → owned godown; else lease deposit is a BS asset
Balance-sheet build
inventory fast (DIO ~15d) = measured stock worksheet; receivables = retailer credit (DSO ~12d, mostly cash/UPI at drop); payables = company credit (DPO ~14d) → tight trade cycle, modest WC; fixed assets = delivery vans/tempo (financeable), racking, DMS handhelds
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 dispatch 5 vehicle_fleet 6 display 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 udyam
Next-photo evidence · Eq 7
Daily beat / van load-out sheet Observed
Per-van dispatch value → throughput per sqft.
Company primary-purchase invoices External
Inward purchases + turn confirm dispatch volume.
Active retailer count (GSTR-1/DMS) External
Outlets served bound daily secondary sales.
Godown area measurement Observed
Fixes usable stacked area.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Company DMS / primary invoices | primary purchases + scheme income → revenue base | strong |
| e-way bills | inward primary + outward secondary consignments → throughput | strong |
| GST 3B/GSTR-1 | declared turnover + active retailer count vs derived | strong |
| AA bank feed | retailer collections vs sales; distributor-credit discipline | medium |
Activity signals
| Footfall | n/a (B2B); van load-out and return timing at the bay is a throughput check |
| B2B / counterparties | active outlets from GSTR-1 + DMS + beat register × drop size → bounds daily secondary sales; number of vans caps reach |
Variance path: Photographs alone put annual turnover within ±87% at 90% coverage in the metro band and ±96% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Company DMS / primary invoices (primary purchases + scheme income → revenue base); e-way bills (inward primary + outward secondary consignments → throughput); GST 3B/GSTR-1 (declared turnover + active retailer count vs derived).
Owns warehouse space and earns rent per sq ft per month scaled by occupancy; near-zero COGS and the building itself is the asset, so revenue is effectively rentable area × rent × occupancy.
Turnover · metro₹27.30 L
Turnover · non-metro₹9.58 L
EBITDA · metro₹15.02 L
Photographs alone±144%
Full instrument stack±11% · tier A
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Effective rent yield / sq ft / day (net of vacancy) | Claim | 0.3 | 0.5 | 0.75 | ₹ |
| Rentable area | Observed | 8000 | 15000 | 30000 | sq ft |
| Operating days / yr | Claim | 360 | 364 | 365 | days |
Registry: gm [0.75, 0.85, 0.92] · opex [0.2, 0.3, 0.42] · DIO 0d · DSO 22d · DPO 10d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹15 / ₹22 / ₹32 |
| Rent/mo · non-metro | ₹6 / ₹10 / ₹16 |
| Deposit → BS asset | 3 months |
Owned signal: electricity + property-tax receipt in owner's name and NO lease-as-tenant → building is owned; treat as fixed asset / collateral (often the main security), and rent received is revenue not opex
Balance-sheet build
no inventory (DIO 0); receivables = rent arrears (DSO ~22d); minimal payables; the building is the dominant fixed asset and usual COLLATERAL — value it and note tenure/title; security deposits held are a liability
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 occupancy_gauge 5 rental_agreement 6 interior 7 utility_meter 8 gst_board 9 pukka_invoice 10 udyam 11 licence
Next-photo evidence · Eq 7
Occupancy read (bays/racks filled vs total) Observed
Occupancy % is the dominant yield driver — filled vs empty bays.
Tenant rent ledger / lease schedule Claim
Contracted rent/sqft × occupied area → revenue directly.
Rentable-area measurement Observed
Fixes lettable sq ft vs gross built-up.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Tenant rental agreements | contracted rent/sqft, tenure, escalation → revenue | strong |
| AA bank feed | monthly rent inflows vs contracted rent; arrears/vacancy | strong |
| GST 3B/GSTR-1 | rental turnover (18% GST on commercial rent) vs derived | strong |
| Property-tax receipt / electricity | ownership → collateral; connected-load floor-size sanity | medium |
Activity signals
| Footfall | n/a; vehicle in/out at gate is a soft occupancy signal only |
| B2B / counterparties | number of tenants from lease schedule + GSTR-1; occupancy % = occupied ÷ rentable area is the key throughput/utilisation measure and single-tenant reliance is a risk flag |
Variance path: Photographs alone put annual turnover within ±144% at 90% coverage in the metro band and ±169% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Tenant rental agreements (contracted rent/sqft, tenure, escalation → revenue); AA bank feed (monthly rent inflows vs contracted rent); GST 3B/GSTR-1 (rental turnover (18% GST on commercial rent) vs derived).
Buys goods in bulk on supplier credit and resells to B2B buyers on a thin buy-sell margin; profit rides on inventory turn and the receivables-vs-payables spread, not price.
Turnover · metro₹1.45 Cr
Turnover · non-metro₹67.20 L
EBITDA · metro₹3.63 L
Photographs alone±129%
Full instrument stack±11% · tier A
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Sales / sq ft / day | Claim | 12 | 20 | 30 | ₹ |
| Usable trading/storage area | Observed | 1200 | 2200 | 3500 | sq ft |
| Operating days / yr | Claim | 300 | 330 | 355 | days |
Registry: gm [0.05, 0.07, 0.1] · opex [0.03, 0.045, 0.06] · DIO 40d · DSO 38d · DPO 34d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned godown; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory = measured stock worksheet (Observed) overrides benchmark DIO (~40d); receivables = B2B buyer credit (DSO ~35d); payables = supplier credit (DPO ~32d) → positive trade cycle needs WC funding; fixed assets = racking, weighing, small handling gear
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 storage 5 dispatch 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 udyam
Next-photo evidence · Eq 7
Dispatch register / outward invoices Observed
Bounds daily throughput → the dominant psf spread.
Usable-area pace-out / plan Observed
Fixes usable area vs gross floor.
GSTR-1 B2B counterparty count External
Number of active buyers cross-checks throughput.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared turnover + B2B counterparty count vs photo-derived throughput (concordance) | strong |
| e-way bills | outward consignment volume → throughput (psf) sanity | strong |
| AA bank feed | banked collections vs GST turnover; working-capital swings | medium |
| Electricity bill | connected load → floor-size sanity; owned/rented | medium |
Activity signals
| Footfall | n/a (B2B); loading-bay activity photo at peak dispatch hour is a soft check only |
| B2B / counterparties | count distinct buyers from GSTR-1 + dispatch register + e-way bills → bounds daily throughput and concentration risk |
Variance path: Photographs alone put annual turnover within ±129% at 90% coverage in the metro band and ±144% in the non-metro band, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: GST 3B/GSTR-1 (declared turnover + B2B counterparty count vs photo-derived throughput (concordance)); e-way bills (outward consignment volume → throughput (psf) sanity).
Holds a principal's stock in a large godown and earns a fixed commission on throughput plus a small trading spread; large rupee flow but the agent's own margin is thin and receivables/payables settle against the principal.
Turnover · metro₹4.18 Cr
Turnover · non-metro₹2.93 Cr
EBITDA · metro₹12.55 L
Photographs alone±78%
Full instrument stack±10% · tier A
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Throughput value / sq ft / day | Claim | 16.9994 | 26.4435 | 39.6652 | ₹ |
| Godown usable area | Observed | 2636.474 | 4394.1233 | 7030.5972 | sq ft |
| Operating days / yr | Claim | 312 | 360 | 365 | days |
Registry: gm [0.04, 0.06, 0.09] · opex [0.0255, 0.03, 0.0345] · DIO 4d · DSO 35d · DPO 22d · η 0.28
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹18,000 / ₹40,000 / ₹80,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity + property-tax receipt in agent's name + no lease in Mitra pack → owned godown as collateral; else lease deposit is a BS asset
Balance-sheet build
principal stock is held-on-behalf (off-book or clearly segregated — do NOT count as own inventory); own inventory small (DIO ~28d); receivables = commission + trade dues (DSO ~35d); payables to principal (DPO ~22d); fixed assets = racking, handling equipment, godown if owned (collateral)
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 dispatch 5 interior 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 udyam 12 licence
Next-photo evidence · Eq 7
Dispatch / GRN register Observed
Daily inward/outward value → throughput per sqft.
Principal commission statement Claim
Commission-on-throughput ties revenue to volume directly.
Godown area measurement Observed
Fixes usable racked area.
e-way bill throughput External
Independent consignment-volume check.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Principal commission statement | commission income + throughput; strongest tie to revenue | strong |
| e-way bills | inward + outward consignment volume → psf throughput | strong |
| GST 3B/GSTR-1 | commission + trading turnover vs derived (concordance) | strong |
| Rental agreement | godown rent + deposit (BS); or owned-asset note | medium |
Activity signals
| Footfall | n/a (B2B); loading activity at dispatch bay is a soft throughput check |
| B2B / counterparties | distinct downstream stockists/retailers from dispatch register + e-way + GSTR-1 → bounds throughput; single-principal dependence is a concentration flag |
Variance path: Photographs alone put annual turnover within ±78% at 90% coverage, from this trade's own assessed quartile spread on 21 real cases. Stating ±10% needs about 90% of turnover under instrument, and this trade banks 2.1% on the real book, so a bank feed alone cannot reach it. Strongest corroboration for this trade: Principal commission statement (commission income + throughput); e-way bills (inward + outward consignment volume → psf throughput); GST 3B/GSTR-1 (commission + trading turnover vs derived (concordance)).
Salaried manager / executive. Income from a monthly wage; roughly 11% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹30,000
Turnover · non-metro₹30,000
EBITDA · metro₹30,000
Photographs alone±43%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 24000 | 30000 | 47114 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (11%) needs employer confirmation + workplace visit.
Salaried worker / skilled labour. Income from a monthly wage; roughly 22% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹22,000
Turnover · non-metro₹22,000
EBITDA · metro₹22,000
Photographs alone±32%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 18000 | 22000 | 30000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (22%) needs employer confirmation + workplace visit.
Salaried office / clerical. Income from a monthly wage; roughly 17% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹25,000
Turnover · non-metro₹25,000
EBITDA · metro₹25,000
Photographs alone±41%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 18500 | 25000 | 35000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (17%) needs employer confirmation + workplace visit.
Salaried driver. Income from a monthly wage; roughly 20% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹25,000
Turnover · non-metro₹25,000
EBITDA · metro₹25,000
Photographs alone±36%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 20000 | 25000 | 35000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (20%) needs employer confirmation + workplace visit.
Salaried supervisor. Income from a monthly wage; roughly 16% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹25,000
Turnover · non-metro₹25,000
EBITDA · metro₹25,000
Photographs alone±36%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 20000 | 25000 | 35000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (16%) needs employer confirmation + workplace visit.
Salaried teacher / professional. Income from a monthly wage; roughly 5% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹35,000
Turnover · non-metro₹35,000
EBITDA · metro₹35,000
Photographs alone±51%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 25000 | 35000 | 55000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (5%) needs employer confirmation + workplace visit.
Salaried support / services staff. Income from a monthly wage; roughly 9% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹25,000
Turnover · non-metro₹25,000
EBITDA · metro₹25,000
Photographs alone±45%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 20000 | 25000 | 40000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (9%) needs employer confirmation + workplace visit.
Salaried sales / field. Income from a monthly wage; roughly 24% of this cohort is paid partly or wholly in cash and needs workplace or employer verification rather than a bank statement.
Turnover · metro₹25,000
Turnover · non-metro₹25,000
EBITDA · metro₹25,000
Photographs alone±26%
Full instrument stack—
Revenue drivers · metro
| Driver | Class | lo | base | hi | |
|---|
| Monthly salary | Claim | 20000 | 25000 | 30000 | ₹ |
Registry: gm [1, 1, 1] · opex [0, 0, 0] · DIO 0d · DSO 0d · DPO 0d · η 0
Occupancy — owned vs rented
| Rent/mo · metro | — |
| Rent/mo · non-metro | — |
| Deposit → BS asset | — months |
Owned signal:
Balance-sheet build
Guided capture — photo order
1 workplace 2 id_card 3 payslip 4 salary_bank_statement 5 residence 6 neighbourhood
Next-photo evidence · Eq 7
Mitra data pack — cross-checks
Activity signals
| Footfall | — |
| B2B / counterparties | n/a (B2C) |
Variance path: Bank-credited portion verified from statement; cash-paid portion (24%) needs employer confirmation + workplace visit.
Sells milk daily to households, a dairy society or a private collection centre; the herd is the capital and fodder is the main cost.
Turnover · metro₹18.93 L
Turnover · non-metro₹13.25 L
EBITDA · metro₹5.68 L
Photographs alone±50%
Full instrument stack±30% · tier C
Revenue drivers · metro animals × output per animal/day × price per unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Milch animals | Observed | 4 | 8 | 16 | count |
| Milk per animal per day | Claim | 8.0463 | 13.1424 | 23.4685 | litres |
| Price per litre | Claim | 35 | 50 | 60 | ₹ |
| Selling days / yr | Claim | 300 | 360 | 365 | days |
Registry: gm [0.25, 0.32, 0.4] · opex [0.017, 0.02, 0.023] · DIO 31.1d · DSO 10d · DPO 15d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹3,000 / ₹6,000 / ₹12,000 |
| Rent/mo · non-metro | ₹0 / ₹2,000 / ₹5,000 |
| Deposit → BS asset | 3 months |
Owned signal: shed on own or family land shown in the land record or electricity bill; record the shed and herd as fixed assets
Balance-sheet build
fixed assets = herd at market value and shed; inventory = fodder and feed (days of feed in store); receivables = society payment cycle of 10 to 15 days
Guided capture — photo order
1 exterior 2 neighbourhood 3 livestock_shed 4 livestock_count 5 storage 6 milk_collection_slip 7 qr_code 8 utility_meter 9 licence 10 udyam
Next-photo evidence · Eq 7
Herd count photo (each animal, tag visible) Observed
Counting the milch animals pins the volume driver directly.
Dairy society or collection-centre slips (3 months) External
Litres delivered per day, independent of the claim.
Collection-centre rate card or fat-SNF slip External
Fixes the price per litre.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Dairy society or collection-centre statement | litres per day and price per litre → turnover | strong |
| Bank or UPI credits | fortnightly society payments → banked turnover | medium |
| Electricity bill | chaff cutter and milking machine load; owned shed | weak |
Activity signals
| Footfall | morning and evening milking and dispatch captures confirm the two daily collection cycles |
| B2B / counterparties | count of buyers: society, private centre, households; one buyer above 70% is a concentration risk |
Variance path: Photographs alone put annual turnover within ±50% at 90% coverage, from this trade's own assessed quartile spread on 156 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Dairy society or collection-centre statement (litres per day and price per litre → turnover).
Sells eggs to traders and retailers from a layer flock; feed is about two thirds of cost, so margin turns on feed price and laying rate.
Turnover · metro₹78.10 L
Turnover · non-metro₹71.00 L
EBITDA · metro₹9.37 L
Photographs alone±149%
Full instrument stack±30% · tier C
Revenue drivers · metro animals × output per animal/day × price per unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Birds in lay | Observed | 2000 | 5000 | 10000 | count |
| Eggs per bird per day | Claim | 0.7 | 0.8 | 0.88 | eggs |
| Farm-gate price per egg | Claim | 4.5 | 5.5 | 6.5 | ₹ |
| Laying days / yr | Claim | 340 | 355 | 365 | days |
Registry: gm [0.16, 0.22, 0.28] · opex [0.07, 0.1, 0.13] · DIO 7d · DSO 7d · DPO 21d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,000 / ₹10,000 / ₹20,000 |
| Rent/mo · non-metro | ₹0 / ₹4,000 / ₹9,000 |
| Deposit → BS asset | 3 months |
Owned signal: shed on own land in land record → fixed asset; leased shed → rent agreement
Balance-sheet build
fixed assets = sheds and cages; biological asset = flock; inventory = feed and eggs in trays; receivables = trader credit of about a week
Guided capture — photo order
1 exterior 2 neighbourhood 3 poultry_shed 4 bird_count 5 storage 6 egg_tray_stock 7 kacha_bill 8 utility_meter 9 licence 10 udyam
Next-photo evidence · Eq 7
Shed photo with cage rows counted Observed
Cage rows × birds per cage bounds the flock.
Egg trader sale slips (3 months) External
Trays sold per day bound the laying rate.
Feed purchase bills External
Feed volume cross-checks flock size and margin.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Trader or wholesaler statements | trays per day and rate → turnover | strong |
| Feed supplier ledger | feed tonnes → flock size and cost | medium |
| Electricity bill | lighting and water pumps → shed size | weak |
Activity signals
| Footfall | n/a |
| B2B / counterparties | number of egg buyers from sale slips; one trader above 70% is a concentration risk |
Variance path: Photographs alone put annual turnover within ±149% at 90% coverage, from the driver intervals, with no real cases behind them. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Trader or wholesaler statements (trays per day and rate → turnover).
Earns from one to three crop cycles a year on owned or leased land, sold at the mandi or to traders; income is seasonal and weather-exposed.
Turnover · metro₹37.13 L
Turnover · non-metro₹25.99 L
EBITDA · metro₹27.47 L
Photographs alone±84%
Full instrument stack±30% · tier C
Revenue drivers · metro cultivated acres × gross value per acre per cycle × cycles per year
| Driver | Class | lo | base | hi | |
|---|
| Cultivated acres | External | 2 | 4 | 15 | acres |
| Gross value per acre per cycle | Claim | 190265.625 | 464062.5 | 1113750 | ₹ |
| Crop cycles per year | Claim | 1 | 2 | 3 | count |
Registry: gm [0.76, 0.8, 0.8333] · opex [0.03, 0.06, 0.09] · DIO 30d · DSO 15d · DPO 30d · η 0.05
Occupancy — owned vs rented
| Rent/mo · metro | ₹0 / ₹0 / ₹0 |
| Rent/mo · non-metro | ₹2,500 / ₹3,000 / ₹5,000 |
| Deposit → BS asset | 0 months |
Owned signal: name on the land record → owned; lease deed or share-cropping note → leased
Balance-sheet build
fixed assets = land, pump, tractor and tools; inventory = stored produce between harvest and sale; receivables = trader credit after mandi sale
Guided capture — photo order
1 neighbourhood 2 land_record 3 field_crop 4 irrigation 5 machinery 6 storage 7 mandi_slip 8 utility_meter
Next-photo evidence · Eq 7
7/12 extract or patta for the cultivated land External
Government land record fixes the acreage.
Mandi or trader sale slips for the last two cycles External
Quantity and rate sold per acre.
Borewell, canal or drip photo Observed
Water availability bounds the number of cycles.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Land record (7/12, patta) | acres owned and crop recorded | strong |
| Mandi or APMC receipts | quantity and rate per cycle | strong |
| PM-KISAN and crop insurance records | land holding and crop sown | medium |
| Bank credits | seasonal sale credits → banked share | medium |
Activity signals
| Footfall | n/a |
| B2B / counterparties | buyers per cycle from mandi slips (commission agent, trader, processor) |
Variance path: Photographs alone put annual turnover within ±84% at 90% coverage, from this trade's own assessed quartile spread on 43 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Land record (7/12, patta) (acres owned and crop recorded); Mandi or APMC receipts (quantity and rate per cycle).
Takes civil, labour-only or material-and-labour contracts from individuals and builders; billing is by stage, so receipts are lumpy and receivables long.
Turnover · metro₹39.65 L
Turnover · non-metro₹27.76 L
EBITDA · metro₹5.95 L
Photographs alone±77%
Full instrument stack±30% · tier C
Revenue drivers · metro running contracts per year × average contract value
| Driver | Class | lo | base | hi | |
|---|
| Running contracts per year | Claim | 2 | 8 | 24 | count |
| Average contract value | Claim | 212416.0714 | 495637.5 | 1062080.3571 | ₹ |
Registry: gm [0.14, 0.2, 0.28] · opex [0.0425, 0.05, 0.0575] · DIO 5d · DSO 45d · DPO 30d · η 0.02
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹15,000 / ₹30,000 |
| Rent/mo · non-metro | ₹3,000 / ₹4,000 / ₹5,500 |
| Deposit → BS asset | 3 months |
Owned signal: no office is common; record machinery and scaffolding as fixed assets
Balance-sheet build
receivables = retention and unpaid stage bills (often 30 to 60 days); payables = labour and material suppliers; fixed assets = mixer, scaffolding, vehicle
Guided capture — photo order
1 exterior 2 site_photo 3 work_order 4 labour_register 5 machinery 6 storage 7 pukka_invoice 8 gst_board 9 udyam
Next-photo evidence · Eq 7
Work orders or contracts for the last 12 months Claim
Counts the running contracts.
Stage bills and the matching bank credits External
Fixes billed value per contract.
Live site photo with board or owner confirmation Observed
Confirms at least one contract is real and running.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Bank credits | stage payments → banked turnover | strong |
| GST returns (where registered) | declared contract receipts | medium |
| Work orders | contract count and value | medium |
Activity signals
| Footfall | n/a |
| B2B / counterparties | number of principals (individuals, builders, government) from work orders; one builder above 60% is a concentration risk |
Variance path: Photographs alone put annual turnover within ±77% at 90% coverage, from this trade's own assessed quartile spread on 200 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Bank credits (stage payments → banked turnover).
Buys sand, aggregate and bricks by the truckload and sells to builders and individual house owners, often delivered; credit to builders drives working capital.
Turnover · metro₹50.99 L
Turnover · non-metro₹35.69 L
EBITDA · metro₹10.20 L
Photographs alone±63%
Full instrument stack±30% · tier C
Revenue drivers · metro tonnes per day × price per tonne × operating days
| Driver | Class | lo | base | hi | |
|---|
| Tonnes sold per day | Observed | 3.3129 | 7.4541 | 14.4941 | t |
| Blended price per tonne | Claim | 1400 | 1900 | 2600 | ₹ |
| Selling days / yr | Claim | 300 | 360 | 365 | days |
Registry: gm [0.23, 0.26, 0.3] · opex [0.051, 0.06, 0.069] · DIO 6d · DSO 30d · DPO 15d · η 0.03
Occupancy — owned vs rented
| Rent/mo · metro | ₹15,000 / ₹30,000 / ₹60,000 |
| Rent/mo · non-metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Deposit → BS asset | 3 months |
Owned signal: yard in own name on land record → owned; lease agreement → rent and deposit
Balance-sheet build
inventory = yard heaps and brick stacks; receivables = builder credit of 30 to 45 days; fixed assets = trucks, loader, weighbridge
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 vehicle_fleet 5 weighbridge 6 load_challan 7 price_board 8 qr_code 9 gst_board 10 pukka_invoice
Next-photo evidence · Eq 7
Load challans or royalty passes (3 months) External
Loads moved per day.
Yard stock photo with heap sizes estimated Observed
Stock turn bounds daily volume.
Rate board or quotation Observed
Fixes price per tonne.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Royalty or transit passes | tonnes moved | strong |
| GST returns and e-way bills | declared sales and delivery count | strong |
| Bank credits | builder payments → banked share | medium |
Activity signals
| Footfall | timed yard captures of trucks loading |
| B2B / counterparties | count of builder customers from GSTR-1 or the credit ledger |
Variance path: Photographs alone put annual turnover within ±63% at 90% coverage, from this trade's own assessed quartile spread on 10 real cases. Stating ±10% needs about 90% of turnover under instrument, and no banked share has been measured for this trade, so the requirement is untested here. Strongest corroboration for this trade: Royalty or transit passes (tonnes moved); GST returns and e-way bills (declared sales and delivery count).