Solutions · Regulated AI
Govern the model, and keep a defensible release trail.
Governance-first private AI, mapped to the NIST AI RMF and ISO/IEC 42001. Separation of duties is enforced in the data layer — the approver can never be the trainer — and the evidence is produced by the pipeline as a model moves, not assembled after the fact. It is built so the model you ship is the model you can defend, months later, to a regulator who never trusted your dashboard.
Framework mapping
The controls a framework asks for are properties of the record
The AI RMF organizes governance into four functions — Govern, Map, Measure, Manage. ISO/IEC 42001 asks for a management system around AI. LeanLogix does not paper over either with a policy document. Each function maps to a capability the studio already enforces on every build.
A control you can only describe is a control you cannot prove. Here the mapping is mechanical: the reviewer being independent of the trainer is a state on the record, the leakage score is a number the gate reads, and the lineage is what the signature covers.
This is a mapping to those frameworks, not a certification under them. The point is that the artifacts an auditor would ask for already exist by the time a model reaches a channel — they are not reconstructed after a request.
AI RMF function → LeanLogix capability
The governance gate
Four steps from a dataset to a sealed release
Promotion is not a button someone clicks at the end. It is a sequence of gates a build has to earn — and each gate leaves the evidence behind it on the record.
Why it holds up
Governance that survives a question you cannot anticipate
The hard part of regulated AI is not the model. It is proving, after the fact, exactly what shipped and why it was allowed to. The studio closes that gap by construction rather than by paperwork.
Separation of duties
The approver is never the trainer. A release that has not cleared a reviewer independent of the build cannot enter the production channel — the constraint is enforced, not requested.
Evidence by construction
The passport is produced by the pipeline as the model moves, not assembled into a binder after a regulator asks. The record that drives the gate is the same record an auditor reads.
Defensible release trail
On approval, the release is sealed with an Ed25519 signature over its lineage, and an offline endpoint re-derives the passport and checks the signature. A regulator or customer can re-verify it later without trusting your dashboard, your word, or your uptime.
Framework-aligned
The mapping to the AI RMF and ISO/IEC 42001 is a property of the record, not a slide. The functions a framework names — Govern, Map, Measure, Manage — line up with capabilities the studio already runs.
LeanLogix maps its capabilities to the NIST AI RMF and ISO/IEC 42001 to make governance evidence concrete. This is a framework mapping for evaluation, not a claim of certification, accreditation, or deployed customer compliance.
Walk a model through the gate yourself
Open the studio and follow one build from a registered dataset to a signed passport — then look at the evidence model that makes the release trail re-verifiable.