Workflow context
The customer job determines the reliability checks. Industry labels alone cannot establish that a workflow is safe or complete.
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About this material
The material below is an earlier industry perspective. Historical examples, metrics, offers and readiness claims have not been revalidated for this review. They are not evidence of current product performance.
AI for the firms that move money.
We make AI trustable and reliable for the teams that run banks, credit unions, asset and wealth managers, payments, lending, and capital-markets infrastructure.
Capture value from the AI you put into production, prove control independently, and produce the evidence your risk, legal, and compliance teams can sign.
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Deployed is not the same as working.
Most AI in a regulated firm never moves the number. The output becomes the record an analyst, an underwriter, or a relationship manager acts on before anything proves it earned the trust. A reviewer cannot keep pace with how fast agents produce outputs, so sign-off after the fact is not a control. We make the output reliable enough to act on, and produce the record the exam already expects.
Serve the finance buyer map.
SUB-SECTOR | BUYER TITLES |
|---|---|
Banks (community + regional) | CIO, Chief Risk Officer, Head of Innovation |
Credit unions | CEO, CIO |
Asset / wealth managers | COO, Head of Technology |
Capital-markets infrastructure | CTO, Head of Engineering |
Payments / lending fintech | CTO, VP Engineering, CISO |
Banking-ops software | CIO (scaled platform integration shape) |
Three sub-sector pages drill in.
Banks and credit unions; fintech (payments, lending, banking-ops, capital-markets tech, treasury); asset and wealth and capital markets. Same regulatory perimeter, different buyer-specific workflows.
Banks & Credit Unions. /industries/banks → SR 11-7 anchored for community banks, regional banks, and credit unions.
Fintech. /industries/fintech → payments, lending, banking-ops, capital-markets tech, treasury and CFO software.
Asset & Wealth + Capital Markets. /industries/asset-wealth → asset managers, wealth managers, broker-dealers, capital-markets infrastructure.
Put AI on the workflows that move the number.
Where AI value capture lands inside a regulated firm. One workflow at a time, made reliable enough to act on instead of re-check.
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 across third-party model behavior.
Internal AI tooling rollout. vibe-coding governance, enterprise AI chatbot, search.
We Build It, and We Read It.
We build governable AI into the workflow, and we are the independent read on whether it holds. The same evidence base that makes the output reliable produces the audit trail your CRO and Compliance team already operate against. When you need an arm’s-length opinion, the audit runs as a separable service.
Standards and frameworks. ISO 42001, NIST AI RMF, AIUC-1.
Regulations. SR 11-7 (model risk, US banking), GDPR, CCPA, NYDFS Part 500, FFIEC, EU AI Act.
Guidelines. industry codes of practice and supervisory expectations.
One evidence base, read by the people who own the workflow and the people who have to sign off on it.
The Read Holds Up at the Next Exam.
A finance copilot went from roughly 60% accuracy to 95% stated accuracy (about 90% measured) once the eval harness became the deploy gate, and the customer ran 144% NRR (net revenue retention) alongside the work. Nothing shipped until the golden set passed, and every customer cleared its own compliance review. That is the difference between AI that is deployed and AI a regulated firm can act on.
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.
01AI AuditMap use, value, risk, and next move.
02AI TransformationShip the priority workflow with measures.
03AI GovernanceControl what ships and prove what changed.
04AI FluencyTrain owners on the workflows that changed.
Two Ways In.
Capture value from AI, or prove control independently. Tell us which one is pressing and we start there. The calendar link is within 60 seconds.
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Frequently asked.
Yes. We can scope work for community banks, regional banks, and credit unions. The Adoption & Efficiency Gain Report Template was built for this band of institution.
Existing tooling covers deterministic and statistical models. Tensile AI adds the LLM and agent evaluation layer alongside it, with framework-mapped evidence that survives the next exam.
Our evidence pipeline produces the artifacts your model risk team needs. Your team owns the review; Tensile AI gives the auditor the evidence package.
Related links and sources
From context to an observable check
Define the job
What exact customer result is expected, for whom and by when?
Capture the input, intended owner, authoritative result and completion condition.
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Find the affected work
Which jobs failed, and what portion of the source is visible?
Record job IDs, agent versions, time window, exclusions and source freshness.
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Explain the gap
Where did expected behavior diverge from observed behavior?
Link source evidence to the hypothesis; keep missing evidence and owner decisions explicit.
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Retain the expectation
What reusable check would catch this failure without rejecting correct work?
Include positive cases, counterexamples, policy scope and an independently reviewed expectation.
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Bring the workflow and the evidence gap.
We can start with an affected customer job and the result your team needs to establish.
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AI reliability, from production to customer outcome.