Illustrative example engagement · not a client result
An underwriting agent that routes routine loans and flags the rest
Illustrative example engagement: how we would scope a multi-agent underwriting pipeline for a digital lender, inside its own AWS account and with a full audit trail.
01 · Challenge
A typical digital lender has an analyst read bank statements, verify identity and apply credit policy on every application. As volume grows, manual review becomes the bottleneck and slow approvals cost deals.
02 · Approach
A two-week discovery sprint turns the written credit policy into explicit decision rules and finds which applications are routine.- Specialist agents handle document extraction, identity checks and policy scoring, with human review for edge cases.
- Everything runs in the lender’s own AWS account, with zero-retention model endpoints and an immutable decision log.
03 · Goals
- Routine applications decided by the pipeline, with every decision logged and explainable.
- Analysts focus on complex, high-value cases.
- Results are measured against the business metric agreed in discovery, not a demo.
Illustrative example
The stack we would use for a build like this.
- Anthropic
- LangGraph
- AWS Bedrock
- Postgres
- Python
- Related service
- AI Agent Development
- Related industry
- Fintech
Planning a build like this?
Start with a free AI audit. You get a fixed-price plan after a 2-week discovery sprint.
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