The agent that hands your credit team a decision-ready file.
It observes every source — bureau, banking, KYC, GST, documents — triangulates them, and questions the customer like a careful credit officer, in their own language, to assemble the file a fair assessment needs. It runs the bank's own policy as a rules engine to match the right product and never sends anyone to a dead-end — the credit decision stays with the bank. Built for reach and financial inclusion across India's tier-2/3 diversity, not just a better form.
Every conversation below is a recorded, deterministic replay of the real engine — identical every time, no live-LLM variance.
It cross-examines — and it understands
The agent doesn't just collect a form. It reads the bureau and bank statements against what the customer said — surfacing the fuller story a form would miss, and questioning a figure that doesn't add up. The same skill serves the genuine customer and screens the fraudulent one. Switch between both below.
Try it live → /testStated ₹75,000 · bank statements observed ₹52,000 · the agent raised it in its own advisor voice, the applicant corrected down — a 31% income drift, recorded as a fraud-screening signal.
Every conversation becomes a decision-ready file
At close, the lender's risk team gets a file: the cross-source discrepancies the agent flagged, the file checked against the bank's own credit policy (version-stamped for audit), and the best-fit product across the shelf. The credit call stays with the bank.
The file clears the cut-offs — but the income drift above rides along on the record, so your credit team decides with eyes open. Thin-file / new-to-credit applicants may still be considered with stronger income proof or a co-applicant — do not hard-decline on a missing score alone; surface the path. FOIR may be relaxed toward 0.60 for salaried applicants above ₹1L take-home with a clean bureau and stable tenure.
Nobody is turned away
A customer who fails the product they applied for isn't rejected — the engine ranks them across the whole shelf and routes them to one they DO qualify for: a new-to-credit loan, a secured card, a gold loan.
No dead-end: a customer the standard product declines is routed to Personal loan — new-to-credit, which she qualifies for — the financial-inclusion thesis, as an engine.
And it goes further
It knows what they want
A customer who already knows they want a gold loan answers ~9 questions — the gold + KYC it actually needs — not the 21-field personal-loan interrogation. The questions adapt to the product.
Try the gold loan → /testThe 11th interaction is cheaper than the 1st
Months later, the next product is a 90-second start: the agent remembers the household, employer, and bureau identity, and only asks the delta — never a re-onboarding.
Train it on your policy, not an IT project
Paste a credit-policy document and it compiles to a structured, validated policy that applies in the next conversation. Edit a cut-off — score, FOIR, income floor — and the next file is re-checked against it, with no deploy.
Open the catalog console → /admin/catalog