Branch officers manually collected land documents, read 8A records, traced linked 7/12 extracts and checked ownership and existing charges. It was slow and error-prone — and because a charge can take time to reflect on the 7/12 after lending, another branch could lend again against the same land in the gap.
Before building anything, we mapped the operation. These are the findings that shaped what we built.
Verification depended on borrower-submitted documents that were often outdated or incomplete.
Existing bank charges and joint ownership on a parcel were easy to miss in a manual read of the record.
After lending, no one tracked whether the charge actually reflected on the 7/12 — the gap that enables over-lending.
The data foundation, and the AI agents that turn it into decisions and action.
From basic land details, an agent fetches the 8A record and linked 7/12 extracts from the official source and autonomously verifies borrower and landholder name, area, survey/gat numbers, joint ownership, existing charges, application-vs-record mismatch and duplicate use of the same parcel — the read a human officer does, in minutes.
After disbursal, an agent keeps checking whether the charge has actually reflected on the 7/12 — ageing branch-wise pendency and alerting the moment reflection runs late.
When a charge is uploaded but not yet reflected, an agent warns a second lender that a charge appears in progress on the parcel — closing the window for duplicate lending against the same land.
Measured against the baseline the audit established — before and after, on the metrics that move the P&L.
Start with a Business Transformation Audit — a structured working session where we map where AI changes your P&L, prioritised by impact.