AI for the firms that ship regulated-finance software.

Your customer's procurement team asks for AI-feature evidence before they renew. The reliability they demand is the thing that decides whether the deal closes.

We do two things. We make the AI features you ship reliable enough that the team acts on the output, not re-checks it. And we produce the procurement evidence as a byproduct of how the platform already runs, so it never becomes a separate compliance project.

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Reliability in production is what your customer is buying.

A demo that works is not a feature that ships under a bank's model-risk policy. The gate between the two is evidence, and it lands on your sales cycle whether or not you built for it.

• Value captureAI upsideWhich in-product AI features actually earn the renewal: fraud, FX, dispute, underwriting, collections. Made reliable enough to ship into the customer's own review.

• Procurement gateProcurement evidenceThe AIUC-1 readiness, model-risk documentation, and AI-policy disclosure your enterprise buyer requires before signing, produced from the same pipeline that runs the feature.

• Customer-driven riskInherited model riskYour customers' SR 11-7 and NYDFS exposure flowing back to you as feature requirements you have to answer in a procurement questionnaire.

• Operating readinessWorkforce fluencyWhether engineering, product, and customer success can ship AI features under enterprise procurement scrutiny without pulling product engineers off the roadmap.

One regulatory perimeter. Five buyer shapes.

We map the engagement to whichever shape your platform sits in.

Where AI moves the needle.

Five recurring fintech workstreams. Each shows up across payments, lending, and banking-ops under the same procurement pressure.

• In-product AI feature evals. Standard and Custom evals on the AI features your product ships.

• Customer-facing attestations. AIUC-1 readiness, model-risk documentation, customer-facing AI-policy disclosure.

• Customer-driven model risk. Your customers' SR 11-7 and NYDFS exposure flowing back into your AI-feature requirements.

• Internal AI tooling. Engineering, support, and sales: vibe-coding governance, enterprise AI chatbot, search.

• Vendor model governance. Third-party model evaluation embedded in your feature stack.

Your compliance is customer-driven.

The bank you sell to is under SR 11-7, NYDFS, and FFIEC. That flows down to you as procurement requirements on every renewal. We produce the evidence pipeline that closes those loops without forcing you to stand up a compliance team. AIUC-1 readiness on AI features is the high-priority pattern in fintech, and the same pipeline produces customer-specific evals, attestations, and AI-policy disclosure off one infrastructure.

What the work produced.

An AI-native finance platform was stalled at brittle eval coverage and a sales cycle that stalled on procurement. Making the eval harness the deploy gate turned "ships fast" into "ships reliably."

• ~60% → 95%: stated accuracy after the deploy-gate work

• 144%: net revenue retention, alongside the reliability work

• 100+: customers each clearing their own compliance review

Two doors. Same pipeline.

Pick the door that matches what you need first. Both run off the same evidence pipeline that watches your AI features in production.

Capture value from AI

Ship the in-product features that earn the renewal, and make the output reliable enough that your customer trusts it instead of re-checking it.

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Prove control, independently

Production evals plus an independent read your customer's procurement team can accept. The attestation is a byproduct of the pipeline, not a separate project.

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Start the read.

Discovery call. Calendar link within 60 seconds.

Frequently asked.

The first read and a Standard Evals engagement are sized for a single-feature footprint. The governance and AI-policy work scales when your enterprise customers start asking for attestations.

That is the AI Transformation shape. The read covers the AI inventory, then the transformation work runs the integration-priority features one at a time.

Observability tells you what happened. Evals tell you whether what happened was correct for your customer's use case. We sit on top of your observability stack, not in place of it.