Technology overview
How AtlasProof verifies AI
This is a high-level summary of the Proofence verification architecture — written for a general audience, not a technical specification. It describes the moving parts at a conceptual level only.
Multi-model consensus
Several AI models answer the same claim in parallel. AtlasProof compares how they vote, weights each response and surfaces disagreement instead of trusting a single model.
Trust scoring
Model confidence, source support, model agreement and policy posture combine into one 0–100 trust score, mapped to a clear governance action (approve, warn, review or block).
Evidence sources
Claims are cross-referenced against recommended trusted sources per domain, so a score is backed by references a reviewer can inspect — not an opaque verdict.
Expert review
Low-confidence or high-impact verifications can be escalated to expert and hybrid review, keeping a human accountable for sensitive decisions.
Proof-certificate preview
Each verification produces a report and a proof-certificate preview summarizing the score, components and sources — a preview surface, not a real cryptographic issuance in this demo.
Proofence engine boundary
The public experience is a thin buyer-facing surface. The deep verification engine lives behind a clean adapter boundary in a separate core repository and is never exposed here.
This overview is a summary only. It omits proprietary engine internals and contains no configuration, credentials or secrets.
AtlasProof Beta v1 — sandbox demo; informational only, not professional advice.