The Science of Substantiation
Tracekind AI is built on rigorous research into regulatory compliance, ontological engineering, and the informal reality of SME supply chains.
Tracekind SME Evidence Survey 2026
Our foundational research focused on quantifying the burden placed on independent fashion and lifestyle brands when attempting to verify origin and sustainability claims.
The Core Finding
Enterprise solutions assume perfectly structured, EDI-compliant data. The reality for 90% of UK independent brands is that vital proof of origin exists entirely in fragmented WhatsApps, images, and non-standard PDFs. This gap forms the basis for Tracekind's SME Evidence Ontology.
The SME Evidence Ontology
Unstructured Parsing
Our models are specifically tuned to identify entities (materials, volumes, dates, certifications) within the noisy environment of supplier chat histories and informal receipts.
Batch Level Graph
Traditional ERPs track items. The Ethical ProofGraph is a novel architecture that maps the relational matrix between a specific marketing claim and a specific physical batch.
Contradiction Detection
By structuring the unstructured, we enable cross document consistency checks, identifying when a supplier's invoice contradicts a subsequent WhatsApp promise.
Bridging the gap to HMRC & CMA standards
The platform's architecture was directly informed by deep, practical exposure to HMRC regulatory compliance, audit frameworks, and the evidential rigour demanded by the CMA Green Claims Code.
CMA Mandate
"Businesses must hold the evidence to support their claims before they make them." Our graph guarantees this condition is mathematically met.
DMCC Enforcement
With fines up to 10% of global turnover, our Evidence Score provides a quantifiable metric of risk exposure before a claim goes public.