Audit-first AI governanceUnderwriting calls, claims decisions, credit determinations, treatment recommendations, code changes, and customer communications increasingly involve AI-assisted judgment.
For banks, credit unions & insurers
Use AI in your bank — without the risk.
KAiM puts guardrails around your AI so it can't approve, deny, or send anything it shouldn't — and you keep the proof to show an examiner.
The risk of doing nothing: if an AI tool inside your bank denies a loan it shouldn't, can you prove to an examiner why? KAiM stops the wrong AI action before it reaches your customer — and keeps the record.
Built for community banks & credit unionsRight-sized — not enterprise overkill.
Made for your examsAudit-ready records for every AI decision.
Led by a Fed veteranRobert Sellers — 25 years at the Federal Reserve & Wells Fargo.
Start small, prove it worksOne workflow, real results, before any big rollout.
The problem
AI is making real decisions now. Someone has to keep it in bounds.
The danger isn't the technology — it's an AI action going out the door that you can't defend later: a loan denied, a claim closed, a customer emailed, with no proof of who approved it or why.
Policy binders, PDF standards, and committee charters describe what should happen. They can't actually stop a risky AI action the moment it tries to happen.
When AI fails, examiners and auditors look for documented decisions, traceable logic, human authority, escalation paths, and defensible evidence.
How it works
A safety check between your AI and your customer.
No new system to learn. KAiM sits quietly in the middle and checks every important AI decision — in an instant, before anything happens.
Step 01
Your AI suggests something
Like approving a loan, denying a claim, or emailing a customer.
Step 02
KAiM checks it first
Is this allowed? Is there proof? Does a human need to look? Decided in an instant, before it reaches anyone.
Step 03
You get a clean record
Every decision is logged and ready to show an examiner, an auditor, or your board.
How KAiM works
We start by listening, not selling.
We learn how your bank actually works — who decides what, and what happens if the AI gets it wrong — before we recommend a thing. You end up with stronger controls and a clear next step, not a binder.
Listen before prescribing
We begin with the customer workflow: who acts, what decision is being made, where the evidence lives, and what happens if the AI gets it wrong.
Find the structural issue
AI governance failures usually expose deeper problems in data lineage, business vocabulary, authority boundaries, and compliance operating models.
Build for proof
Every recommendation should leave the organization with a clearer control, a stronger evidence path, and a defensible next action.
Our tools
Meet the tools that keep your AI safe.
Three simple jobs: check every AI decision, make sure your AI knows your business, and keep every AI helper accountable.
Incoming work
AI proposes an action
A model, assistant, or named agent recommends a consequential step: communicate, approve, deny, merge, route, or update a system of record.
ActorWho or what initiated it?
ActionWhat would change?
The check
KAiM checks it before it happens
Is it allowed? Is there proof? Does a human need to look? Each question is answered before the action reaches a customer, a system, or a record.
Authority
Policy
Evidence
Harm
Regulation
Escalation
What you get
A record you can defend
HELM allows it, blocks it, or sends it to a person — and keeps the proof your risk, compliance, audit, and board teams can read.
AllowWithin authority and evidence threshold.
DenyBlocked before execution.
EscalateHuman review required.
HELM
Checks every AI decision
HELM reviews what your AI wants to do — and allows the safe decisions, blocks the risky ones, and flags anything that needs a human, before it happens.
- Allow, block, or send to a person
- The right view for each role
- A clean record an examiner can read
Herb Brain
Makes sure AI knows your business
Herb Brain gives your AI the right definitions and trusted facts about your bank — so it isn't guessing, or working from a messy spreadsheet it half-understood.
- Your terms, defined consistently
- The right facts behind each decision
- A human checks anything sensitive
Bot Village
Keeps AI helpers accountable
Every AI worker has a name, a job, and clear limits — so no AI is ever doing things behind your back.
- Named, accountable AI roles
- Clear limits on what each can do
- Hands its proof to HELM
Consulting services
Hands-on help getting AI right.
We help you set clear rules for AI, get your information in order, build exam-ready proof, and test it on one real decision before you commit to anything bigger.
AI governance operating model
Set clear rules for AI
Define the operating model, decision rights, approval paths, review rituals, and evidence standards required to govern AI-assisted work.
Outcome: a control map tied to real workflows, not a governance binder.
Data structure and meaning
Fix the context AI depends on
Get your definitions, data, and evidence in order — the quiet gaps that make AI misread your business and get decisions wrong.
Outcome: a governed data and meaning foundation for automation.
Compliance and assurance
Build audit-ready evidence
Translate standards and obligations into control tests, escalation paths, artifacts, and decision records that risk, legal, and audit teams can inspect.
Outcome: evidence architecture aligned to compliance needs.
HELM workflow pilot
Prove one high-risk workflow
Start with one consequential AI-assisted action and demonstrate how HELM would evaluate, block, escalate, and evidence the decision.
Outcome: a board-ready demonstration and pilot path.
See it in action
See it on a real banking decision.
Watch how a risky AI decision — a loan denial, a claim denial, a risky code change — gets caught and documented before it does any harm. Built on examples your team will recognize.
Live · Demo data
Mortgage loan approval under HELM
An AI underwriting assistant proposes deny, send adverse-action notice, and close file. HELM evaluates six checks — fair-lending (ECOA / Reg B), adverse-action, ability-to-repay — before a denial reaches a borrower. Built for community banks and credit unions.
Open walkthroughBanking proof
Live · Demo data
Insurance claim triage under HELM
An AI claims assistant proposes deny, send letter, and close case. HELM evaluates six checks and prevents unauthorized customer-facing action.
Open walkthroughHELM proof
Live · Demo data
AI code governance under HELM
An AI coding agent opens a risky PR with a known-CVE dependency and missing human attestation. HELM blocks merge and generates remediation evidence.
Open walkthroughTechnical proof
Ready to use AI without the worry?
See a real banking decision get checked and documented in under three minutes — then let's talk about your highest-risk workflow. You get big-bank discipline, sized for your shop: start with one workflow and grow only when it's earning its keep.