AI for the institutions that hold deposits and grant loans.

Your teams already run agents in KYC, surveillance, and support. The board question is whether that work moves the number, and whether it survives the next exam.

We do two things. We put AI on the workflows that move the number and make the output reliable enough to act on. And we are the independent read your model-risk team and your examiner can sign.

CTA: Book a call

Agents are already in production. The control hasn't caught up.

Banks have AI activity at scale: alert triage, document ingestion, customer copilots, internal tooling. What is missing is the read your model-risk function can defend, and the value attribution your board can act on.

• Value captureAI upsideWhich bank workflows AI is actually moving: throughput, false-positive reduction, cycle time, cost per outcome. Ranked by what the business can defend in a budget review.

• Risk controlAI riskShadow AI and agent paths your CISO has not inventoried, vendor model drift, and the outputs that became the record before anyone reviewed them.

• Operating readinessWorkforce fluencyWhether the people running KYC, surveillance, and support can actually operate the AI, and where role redesign is the bottleneck, not the model.

• Board evidenceDefensible decisionsThe audit memorandum, the materiality threshold, the working papers your CRO, audit committee, and examiner already know how to read.

Where AI lands first inside a bank.

One workflow at a time, instrumented end to end, so the value and the evidence come from the same pipeline.

• KYC and onboarding. Document ingestion, automated risk scoring, exception triage.

• AML and transaction surveillance. Agent-driven alert triage, false-positive reduction.

• Trade surveillance. Pattern detection, regulatory query response.

• Customer support. Chatbot deflection with strict policy boundaries.

• Vendor and AI-model risk. Continuous monitoring of third-party model behavior.

• Internal AI tooling. Vibe-coding governance, enterprise AI chatbot, search.

SR 11-7 is the load-bearing standard.

AI at a bank is not judged on productivity. It is judged on whether the model survives the next exam. The governance read routes evidence into the supervisory perimeters your CRO and compliance team already operate against, mapped to the three lines of defense: the business owns the workflow, risk and compliance oversee the controls, internal audit tests whether the evidence holds.

One read. A value case and an audit trail.

The same evidence base produces both doors. The discovery side ranks the workflows worth funding. The audit side produces the framework-mapped trail your examiner relies on.

The audit lands as a structured memorandum on the first three pages: opinion, materiality threshold, scope, exceptions, and remediation. Same shape the audit committee already reads from external auditors, and the working-paper package the external auditor can rely on. Frameworks covered: SR 11-7, FFIEC, OCC bulletins on AI/ML, NCUA model risk guidance, NYDFS Part 500, plus ISO 42001, NIST AI RMF, AIUC-1, and the EU AI Act for global subsidiaries.

What the buyer owns.

What the work produced elsewhere.

A PE-backed banking-software platform rolled up from seven merged entities believed AI was handled. The AI Audit surfaced the first unified count the board had ever seen.

• 75: shadow-AI cases surfaced across entities

• 20: unauthorized MCP paths found

• 12: tools brought under one reliable cadence

Two doors. Same read.

Pick the door that matches what you need first. Both run off one operating read of what AI is doing inside your bank.

Capture value from AI

Put AI on the workflows that move throughput, false-positive rates, and cost per outcome. Make the output reliable enough that the team acts on it instead of re-checking it.

CTA: Book a call

Prove control, independently

Production evals plus an independent audit. Show that what is running is behaving, and produce the memorandum your risk, compliance, and audit-committee teams can sign.

CTA: Book a call

Start the read.

Discovery call. Calendar link within 60 seconds.

Frequently asked.

Yes. The operating read and the memorandum scale down to credit-union institution size. NCUA model risk guidance is the primary anchor.

We do not run your model review. The evidence pipeline produces the artifacts your model-risk team needs, mapped to the frameworks you already answer to. We feed the auditor.

Different layer. Your existing tooling covers deterministic and statistical models. We add the LLM and agent evaluation layer alongside, and tie its output to the value and risk read.