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// Fintech × LLM Integration & Evals

LLM integration for fintech

LLM integration for fintech — eval-gated accuracy, a provider-agnostic interface, and audited structured output for products that have to hold up under regulation.

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// The problem

Why this is hard

Wiring an LLM into a fintech product raises the stakes: an extraction that's wrong is a liability, a provider outage takes a regulated feature down, and 'is it accurate?' has to be a number you can defend, not an opinion. The integration that matters is eval-gated, provider-agnostic, and audited end to end.

// How we do it

The approach for this fit

Eval-gated accuracy

Every prompt and model change scored against a labelled set, so accuracy is a measured, reportable bar — and low-confidence cases route to human review.

Provider-agnostic by design

The model sits behind an interface you control, so a single vendor's outage or price change can't hold a regulated feature hostage.

Structured, audited output

Validated structured output with inputs, sources, and model version traced per call — an audit trail a compliance team will accept.

// Proof

Shipped in production

Fintech — Document intelligence

manual review

-82%

vs. the fully-manual baseline

throughput per analyst

3.5×

vs. the pre-automation baseline

Read the case study

// FAQ

Common questions

We hold out a labelled eval set and score every change against it, so accuracy is a number you can report and regression-check — not a claim — with low-confidence outputs escalated to a human.

The model is behind a provider-agnostic interface, so we fail over or switch providers without a rewrite — a single vendor isn't a single point of failure for a regulated feature.

Yes — inputs, retrieved sources, prompt/model version, and output are traced per call, so a reviewer or regulator can reconstruct exactly what happened.

// Part of

// Let's build

Building LLM integration for fintech?

Tell us where you are. We reply within a day with a concrete next step.