GreenBox Loans

The problem

Mortgage eligibility, pricing, and policy answers lived across thousands of internal product documents. Finding the right one meant knowing it existed first, and the people who knew were the bottleneck.

Separately, reviewing a borrower's bank statements was a three-day manual process. Statements arrived as PDFs across multiple banks and date ranges, and someone had to read every page to classify credits against debits before underwriting could move.

What we built

• MIA, a retrieval-grounded conversational agent indexing the full corpus — internal mortgage product documents plus competitor product knowledge — behind a single agentic interface for internal staff.
• Two specialised desks share that corpus: a Greenbox Desk for guideline and policy questions, and a Market Desk for market queries. Per-document scoping keeps an answer inside the sources it should be reading.
• Regenerate, copy, and feedback controls, plus a standing-by status indicator, so a broker can see the agent is grounded rather than guessing — and flag a bad answer in flight.
• A separate agentic OCR and extraction pipeline ingests multiple bank statement PDFs in one upload, merges them across date ranges, classifies credits versus debits, and exposes filters by bank, account, statement range, and description.
• Extractions are named and linked to a client and loan number, so the output feeds downstream underwriting instead of dead-ending in a spreadsheet.