The platform
Eight levels of lending technology. We operate at six to eight.
Most lenders digitised the old model — apps, copilots and paperless workflows layered on top of branches and relationship managers. Levels six to eight replace it: an army of AI agents runs every function, improves daily, and a human signs the credit.
The agents
Four twins of the jobs that made this segment unbankable
Origination engine
Agent Odin™
Odin walks the street in imagery — reading every name board, sizing every shop, scoring lend probability — so a pre-qualified offer exists before anyone leaves a desk. Other lenders angle for one customer at a time; Odin enumerates the whole pond.
Maps and scores every urban micro-business, PIN code by PIN code.
Credit intelligence
Agent Janus™
Janus reads bureau data, account-aggregator bank statements, the shop itself through a camera, and the borrower through an AI voice call — assembling a credit memo that reaches a human officer in under nine minutes.
Underwrites 4,000+ signals through four progressive gates.
Field management
Nemesis AI™
Algorithmic task allocation and route optimisation turn a field officer from a territory owner into a dispatched resource — so one officer looks after six hundred loans rather than one hundred and fifty.
Routes and tracks the field force the way a ride-hailing app runs a fleet.
Portfolio & collections
Saras AI™
Continuous machine learning on repayment conduct triggers early-warning signals at three days past due, not thirty — and settles collections digitally on daily instalments matched to the merchant’s cash flow.
Watches repayment health daily and nudges before stress becomes default.
The autonomous lifecycle
Hyper-local sourcing to instant disbursement, with a human at the one point that matters
A green rule marks the steps an agent runs on its own. Step 04 has none by design: disbursement is an act of the regulated balance sheet, not of the software.
Underwriting
Four gates, one memo, a human signature
Janus spends the next rupee of underwriting effort only on applicants the previous gate qualified. Socrates — the machine-learning engine trained on every FriendLoan applicant, loan and repayment since 2022 — scores each stage against what actually got repaid.
The customer consents in the app; two bureaus are pulled; hard rules plus the Socrates score decide whether more underwriting effort is worth spending.
Account Aggregator statements: UPI income set against every bureau obligation. One shop photo gives a first read of daily revenue.
The field officer films; Janus decodes the shop live — stock, footfall, premises — every frame geotagged, agent-stamped and time-stamped.
A 28-question personal discussion. Deviations across shop, bureau and bank are clarified and scored.
The credit memo, the draft offer and a video personal discussion go to a head-office credit officer, who approves, modifies or declines — then the repayment calendar is built around the borrower’s own trading week.
Every automated recommendation is logged and auditable. On partner rails the partner’s board-approved credit policy binds the decision.
Product design
Repayment shaped to the borrower’s cash flow, not a calendar
A shop earns every day. A monthly EMI landing at the wrong moment creates artificial default. FriendLoan collects in daily or weekly micro-instalments calibrated to each merchant’s liquidity cycle — and iLCS fires the auto-debit at the hour the balance is highest.
Traditional NBFC
- One large monthly debit at peak liquidity drag
- Standard NACH drives 30%+ bounce rates
- Binary schedules fail in festival seasons and cash squeezes
- Cash still collected at the doorstep
FriendLoan engine
- Daily / weekly micro-instalments matched to real cash flow
- Auto-debit timed to the hour of peak balance
- Dynamic amortisation with holiday toggles from 52-week cash-flow modelling
- 100% digital capture — zero cash, zero doorstep risk
See Odin walk a street.
A live walkthrough of the origination engine, on request, in a first meeting with our partnerships team.