LatentAtlas tests when model answers move from semantic relevance into unsupported authority, action permission, identity claims, stale evidence, or publish-safe truth.
The point is not to claim a finished safety platform. The point is to expose a reviewable control surface: where the model used evidence, where it overreached, and what deterministic route should block or escalate the decision.
Does the system distinguish related evidence from decision-grade proof?
Does the answer ask for an operational action that the evidence does not authorize?
Does a similar entity, product, user, policy, or case get mistaken for the same one?
Are rehearsal, shadow review, and real outcome learning kept separate from production truth?
These are public-safe examples of the inspection logic. Full rows, source text, calibration tables, and customer-specific examples stay reserved for NDA, paid audit, or direct review.
LatentAtlas is strongest where the model is not obviously hallucinating. The risky output often looks plausible because the retrieved context is related, just not authorized for the decision.
Topical retrieval is treated as proof even when source authority, approval state, or audience permission is missing.
A true fact is used to justify refund, access, escalation, suppression, publish, or customer promise decisions.
Peer examples, product lookalikes, account similarities, and historical cases are promoted into same-identity decisions.
Outdated evidence remains semantically relevant while its decision authority has expired.
Human review outcomes are treated as training permission or policy override without governed separation.
Internal evidence, draft status, or team-only reasoning is converted into customer-facing authority.
LLM output can help parse, summarize, and propose signals. It does not become the final authority for truth mutation, canonical identity, policy override, customer publication, or production action.
Capture claim, evidence, requested action, audience, source class, and risk lane before scoring.
Build a minimal evidence packet with provenance, freshness, identity fields, and missing-proof flags.
Score semantic fit separately from authority, action permission, identity confidence, and publish-safe status.
Return allow, verify, hold, review, or block. Uncertain packets do not silently become production truth.
Record reason codes, reviewer outcome, and downstream effect without using learning as a policy bypass.
This is designed to be evaluable without asking for customer data or private logs.
I work on evidence-boundary diagnostics, identity decision reliability, and guardrail routes for AI, RAG, agentic, catalog, and marketplace workflows.
CV/portfolio packet is available on request. I do not publish phone, address, private application packets, or raw evidence rows on this public page.
The landing page gives the recipient a concrete criterion instead of a vague "if useful" ask.