The Ethical ProofGraph Framework
Our proprietary six-element architecture doesn't just store documents. It parses unstructured, informal reality and builds a mathematically rigorous network of evidence designed for strict compliance review.
How the engine verifies claims
Claim-to-Evidence Verification Engine
Break broad sustainability/sourcing claims into testable claim components and map each component to relevant evidence classes.
SME Sourcing Evidence Ontology
Structure informal SME evidence such as invoices, WhatsApp messages, spreadsheets, supplier photos and PDFs into standardised evidence categories.
Batch-Level Ethical ProofGraph
Link claim ↔ product batch ↔ supplier ↔ evidence ↔ production stage ↔ contradiction ↔ confidence.
Ethical Evidence Score
Assess evidential strength using factors such as completeness, freshness, source reliability, traceability, contradiction severity and batch linkage.
Cross-Document Contradiction Detection
Detect inconsistent information across marketing claims, invoices, supplier records, shipping/origin evidence and packaging information.
Claim-Safe Recommendation Engine
Turn findings into missing-evidence actions and wording aligned with the evidence actually available.
End-to-End Verification Flow
From the moment a claim is registered, Tracekind AI automatically decomposes it, structures the unstructured evidence, links it via the ProofGraph, and outputs a confidence score with actionable recommendations.