Case Study - Multi-agent AI for commercial credit assessment

The problem

Commercial lending generates a mountain of unstructured work. Every submission brings financial statements, tax returns, property valuations, borrower profiles, and a 30-item compliance checklist. Analysts spend hours per file — extracting figures, cross-referencing lender requirements, and tracking missing documents.

The client's first instinct was a chatbot. Add an "Ask AI" button to the platform, let it handle the long tail.

That approach fails fast in commercial credit. A question about EBITDA trends needs different data, different formatting, and different reasoning than a question about which compliance documents are outstanding. One big prompt trying to do everything produces generic answers on everything.

We needed a structured architecture — not a bigger chatbot.

What we built

A production multi-agent AI platform with three architectural decisions that make it work:

1. A triage layer that never does real work. Every query first hits a lightweight classifier — low temperature, no tool access, no database calls. Its only job is to identify intent and route to the right specialist. Sub-second routing, by design.

2. Seven domain-specialist agents. Each owns a specific knowledge domain — financials, borrowers, documents, collateral, submissions, team, general. Each has scoped tools and can only query the data its domain allows. No agent has access to everything.

3. Database-layer security, not prompt-layer security. Row-Level Security is enforced in the database — not in the agent's prompt. Even if an agent misbehaves or gets prompt-injected, it physically cannot read data outside its scope.

Under the multi-agent layer, AI is woven through the full platform:

• Voice input — analysts narrate notes; the platform transcribes in real time.
• Financial document extraction — PDFs, spreadsheets, and scanned statements are parsed and validated automatically.
• Compliance checklist automation — the system tracks all 30 items per submission, flags what's missing, and signals what's ready for review.
• Slash commands — power users jump directly to specific agents (/submissions, /financials, /collateral) without typing a question.

Full technical deep-dive: How We Built a Multi-Agent AI System for Commercial Credit Assessment.

What we built

Why this matters

Most "AI in financial services" projects stall because they try to solve everything with one prompt. That approach can't hit the accuracy bar that credit, compliance, and financial reasoning demand.

The multi-agent pattern — one dumb router, many smart specialists — is now how we architect every complex AI build. If you're running into the limits of a single-agent or single-prompt approach, it's the right next step.

Building something similar for lending, insurance, or any document-heavy workflow? Book a consultation or explore AI for mortgage brokers.