2 week AI Audit
AI AuditVisibility, risk, outcomes.AI TransformationCapture AI value across workflows.AI GovernanceContinuous evidence, not paperware.AI FluencyWorkforce that compounds.AI engineeringBuild AI inside your product.EvalsContinuous evaluation in production.All servicesThe full service map.
Not sure where to start?Take the 5-min maturity assessment →Maturity modelOpen assessment
By industry
Private equityPortfolio-wide value capture.Regulated financeThe finance umbrella.Banks & capital marketsRegulator-defensible under SR 11-7.FintechShip trusted AI fast.Asset & wealthFor advisors and ops.REITs / real estateAI-driven NOI uplift.InsuranceUnderwriting + claims, instrumented.
Field notes, scorecards, compliance
12 LeversWhere value capture shows up.Baseline problemWhy measurement collapses.Adoption → assuranceThe sequence every AI team walks.Adoption scorecard9 proxies. 5 minutes.All scorecardsFour 8-minute scorecards.Compliance frameworksISO · NIST · EU AI Act · AIUC-1.Resources hubAll chapters and field notes.
Featured chapter
The 12 Levers of Enterprise AI Adoption.
Twelve places where AI value capture actually shows up, each mapped to who owns it and what good looks like.
Read more →
For finance

AI for the institutions that hold deposits and grant loans.

For banks and credit unions that need AI adoption and risk evidence their model-risk teams can defend.

Built for the model-risk conversation: adoption evidence, continuous controls, and workflow-level proof your risk team can actually use.

Definition

AI for banks starts with workflow evidence your risk team can defend: adoption, value, policy, and model-risk traceability from the AI Audit.

Sub-sectors

The buyer profile.

BuyerWhat they own
CIOOwns the AI rollout. Wants vendor evaluation discipline plus integration with the existing observability and IDP stack.
Chief Risk Officer / Chief Compliance OfficerOwns model risk and regulatory posture. Wants continuous evidence, not point-in-time attestation.
Head of Innovation / Head of DigitalOwns the AI thesis. Needs to defend the bet to the board and to the regulator.
CEO (credit unions)Smaller institutions, single-decision-maker buying motion. Same workflows, lower headcount, more direct route.
Workflows we transform

Concrete bank workflows.

Where AI Transformation lands first inside a bank or credit union. One workflow at a time, instrumented end to end.

  • 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 risk and AI vendor compliance. continuous monitoring of third-party model behavior.
  • Internal AI tooling rollout. vibe-coding governance, enterprise AI chatbot, search.
Regulator framing

SR 11-7 is the load-bearing standard.

AI investments at a bank or credit union aren't judged on productivity. They're judged on whether the model survives the next exam. AI Governance routes the AI Audit evidence into the supervisory perimeters your CRO and Compliance team already operate against.

Sub-sectors

PerimeterWhat it covers
SR 11-7Federal Reserve model risk management guidance. The load-bearing standard for any model that touches credit, AML, fraud, or pricing.
NYDFS Part 500New York cybersecurity regulation. Continuous attestation expectations for AI vendors and third-party model behavior.
FFIEC IT Examination HandbookJoint exam framework across the federal banking agencies. Where AI/ML governance lands during the next IT exam.
NCUA model risk guidanceNational Credit Union Administration expectations. Same model risk discipline, scaled for credit-union institution size.
Compliance posture

One pipeline. Audit-grade evidence.

The AI Audit produces the operating read. AI Governance produces the framework-mapped evidence. Same pipeline, two outputs.

AUDIT
SR 11-7 evidence pack
  • Standards and frameworks. ISO 42001, NIST AI RMF, AIUC-1.
  • Federal banking regulators. SR 11-7, FFIEC, OCC bulletins on AI/ML, NCUA model risk guidance.
  • State and global. NYDFS Part 500. EU AI Act for global subsidiaries.

Same evidence pipeline produces the operational view and the audit-grade trail.

How we ship

Anchored on the AI Audit.

Two-week visibility deliverable, then the three workstreams sequence per your priority. One named TrustEvals practitioner embeds; methodology transfers, the platform stays.

DISCOVERY CALL · 30 MINApproved AI inventoryShadow AI exposureAdoption outcomesEvidence gapsowners, risk, next workstream
How we sequence

Start with Audit. Sequence the workstreams.

One order, applied across the engagement. The AI Audit produces the operating read, then AI Transformation, AI Governance, and AI Fluency sequence per the customer's priority.

Sequence anchored to model risk discipline.
  1. AI Audit
    See use, value, and risk.
  2. AI Transformation
    Ship value workflows.
  3. AI Governance
    Produce audit evidence.
  4. AI Fluency
    Raise role-level capability.
AI Transformation typically starts with KYC, AML, or trade surveillance.
Next step

Start with an AI Audit baseline.

Discovery call. Calendar link within 60 seconds.

FAQ

Frequently asked.

Yes. The Adoption & Efficiency Gain Report Template was built to scale down to credit-union institution size. NCUA model risk guidance is the primary anchor.

Our evidence pipeline produces the artifacts your model risk team needs. We don't run the review; we feed the auditor.

Different layer. Existing tooling covers deterministic and statistical models. We add the LLM and agent evaluation layer alongside.