Adopt AI agents without breaking
your model-risk framework.
Community banks, credit unions, and lenders must build custom software with IT teams they can’t grow: lending workflows, reporting pipelines, the glue around the core. Agents are the way out of the backlog, and every board knows it. But every examination rests on three questions: who changed the system, who reviewed it, who approved it. And the supervisors have added a fourth: why did the system decide that?
When agents join the team,
the attestation chain breaks.
“A qualified person wrote it, a second person reviewed it” stops being true the day an agent writes the code. Haltere repairs the chain: every change carries a human name, a recorded review, and a durable approval. By construction, not by policy memo.
Three regimes. One record.
The examiners aren’t hypothetical. These frameworks already govern how your software changes, and the supervisory questions about AI are already being asked in writing.
The exam already covers change management.
The FFIEC IT handbooks and OCC supervisory expectations reach how changes are authorized, tested, and approved. Today that evidence is assembled by hand, after the fact. On Haltere it’s generated by the system, not compiled by a team.
Your AI decisions look like models now.
The Federal Reserve’s model-risk guidance asks for inventory, validation, and documentation. Supervisors increasingly read AI-driven decisioning through that lens. The receipt records what ran, on what inputs, under whose approval: the documentation exists because the change happened.
Access and change controls, in writing.
IT general controls for financial reporting, and safeguards programs for systems holding customer data, both expect documented change control. The exhibits map to records that already exist, because the system can’t run without producing them.
One receipt per change. One query per audit.
Every step the AI took,
and who signed off.
A single loan-pricing change: retrieved, drafted, checked, approved, sealed. Each step on the record, and every pricing change tied to its approved band. We show the highest-stakes example on purpose; your pilot starts wherever you choose: a portal, a report, a reconciliation. The receipt’s shape is identical.
Fixture data. A margin outside the approved band fails at the gate, and no human is ever asked. The receipt is the difference between answering the examiner and reconstructing for them. Download the sample evidence pack (PDF, fixture data)
Bad changes don’t get reviewed. They get blocked: merged 0 lines, logged, receipted.
The exam won’t only ask
how it was built.
It will ask what it did. Your software prices loans, routes applications, flags accounts. And when the question arrives about one applicant declined in March, from the fair-lending review or the applicant’s adverse-action letter, reconstruction is either a query or a project. On Haltere the operation already wrote its decision receipt: the actor and their authority, the model and prompt that ran, the inputs verified back to source, the checks that passed, the trace.
ECOA expects specific reasons on the adverse-action letter. The receipt is where the reconstruction starts: what the system saw, which checks ran, who approved. A query, not a project.
Institutions, and the vendors
who serve hundreds at a time.
The governed first agentic project.
Community banks, credit unions, mid-size lenders, asset managers. One backlog item you already want, delivered governed in 4–8 weeks, with the evidence pack that lets you show the board AI progress and the examiners the receipts. We’re filling a first cohort of five founding design partners, sequenced.
The proof layer under your AI hub.
Every core processor and lending platform is shipping an AI hub. The hub decides. Increasingly, it builds. And your institutions will be asked to prove both: how the software changed, and why it decided. Your agents, your models, your hub; our map, gates, and receipts. The hub ships faster, the evidence writes itself, and delivery hours stop eating margin.
The governed first agentic project.
One backlog item. Fully governed. Evidence included.
The portal rebuild, the reporting pipeline, the reconciliation that should have become real software years ago. Scoped together before anything starts.
Scoped to the project and quoted in the first conversation, agreed before anything starts. No time-and-materials meter running.
From scoping to production-acceptable delivery, live in your environment.
Two deliverables, always paired: the working software, and the evidence pack. Show the board AI progress; show the examiners the receipts.
The process guarantee: your team reviews and approves every change. Nothing merges ungoverned. Zero unauthorized code reaches production.
Two ways to run it: we operate the line for teams with no engineering bench, or your team operates it and we train your operator. The software, the map, and the receipts are yours in either mode.
You own it. The software and the logic built on top are yours. After the pilot, the line moves into governed operations: we operate it for you, or your own team takes the controls and builds the next ten projects the same way. Walk at renewal with everything. The pilot is how you adopt agents; the platform is how you keep them governed.
Mapped to the exams
you already sit.
Generated by the system, not compiled by a team. The technical file becomes an export.
Bring us an institution with a backlog —
or a core with an AI hub.
We’ll bring the receipts. Scoping is a conversation, the fee is fixed before anything starts, and the first deliverable review is on your calendar before we write a line.
No newsletter. No drip sequence. Every request gets a real reply within one business day — from someone who can answer it.
