Solutions · Healthcare

PHI safety by construction, not by promise.

Name: LeanLogix
Brand: LeanLogix

AI near patient data cannot be trusted on a vendor claim alone. Before a clinical or operational model is allowed close to PHI, it needs a data boundary, a leakage bar it has cleared, and evidence a reviewer can re-check. LeanLogix is built to produce exactly that — so the model you put near patient data is the model you can defend.

Open the studio See the deployment pattern

Leakage on the hard PHI/PII probe suite

Customer or patient rows in any training corpus

Ed25519

Passport seal · re-verifiable offline

AI RMF · ISO 42001

Framework controls mapped per model

Illustrative deployment pattern

Constraint first, then boundary, then proof

This is an example healthcare deployment pattern — an illustration of how a private clinical or operational AI workflow can be scoped and governed in LeanLogix, not a claim of a live customer deployment. It runs constraint to boundary to eval bar to a governed release.

Start with the clinical constraint

Name the data-boundary, latency, or trust constraint that makes a shared, third-party inference endpoint unworkable near clinical or operational data — before any model is selected.

Draw the PHI/PII data boundary

Define which systems, datasets, roles, and approval points sit inside the private workflow. Training data is registered and PII-scanned; no patient rows enter the corpus.

Set the PHI-leakage eval bar

List the leakage, behavioral, and safety probes a version must clear before it can advance. A model that leaks on the hard PHI/PII suite does not move forward.

Govern the release with signed evidence

Promotion is gated by separation of duties — the approver is never the trainer — and on approval the release is sealed with a re-verifiable signed passport over its lineage.

Why it holds up near PHI

Evidence near patient data, not assertions about it

A model is easy to demo. What is hard, months later, is proving exactly what it was trained on, that it does not leak, and why it was allowed near clinical data. LeanLogix closes that gap by construction.

PHI-safe by construction

Training happens inside the data boundary on a registered, PII-scanned corpus. The SprintLoop corpus is 2,800 verified examples with zero customer or patient rows — there is nothing to leak because it was never let in.

Leakage evidence, not assertions

Every version is run against PHI/PII leakage probes alongside behavioral and safety suites. The hard suite reads 90/100 with zero leakage — a measured verdict attached to the artifact, not a sentence in a sales deck.

Audit boundary design

The pattern makes explicit which systems, datasets, roles, and approval points sit inside the private workflow — so an auditor sees a defined boundary, not an undocumented path between a model and a chart.

Built for the audit a review asks for

Governance for each model maps to AI RMF and ISO 42001 controls — separation of duties, registered data lineage, and a signed release trail — so it produces the kind of evidence a HIPAA or SOC 2 review asks for. That is the architecture, not an external certification: LeanLogix is not HIPAA-certified or SOC 2-attested, and never prints a compliance badge the registry cannot back.

Scope a PHI-safe model the way you would defend it

The deployment pattern here is illustrative — a frame for how a private clinical or operational workflow gets a boundary, a leakage bar, and signed evidence, not a claimed live customer system. Open the studio to follow a governed build, or review the enterprise delivery path for implementation.

Open the studio Review enterprise delivery