I built a UK-filed, patent-pending system that sits between enterprise data and an LLM to compile the smallest structurally complete context packet for a given query.

Most context tooling is built around semantic similarity. This is built first around governance: in regulated environments, teams need outputs they can explain, audit, and defend.

Core properties: - deterministic, budget-bounded context assembly - graph-aware structural completeness (not just similarity retrieval) - provenance metadata for traceability and explainability

Sectors where this matters most: building safety/BIM, healthcare records, ERP/finance audit trails.

I’d value critical feedback on: - whether this governance framing resonates or feels like a marketing angle - evaluation design for determinism and structural completeness - failure modes and adversarial cases you’d want tested

If there’s interest, I can share anonymised examples, benchmark methodology, and current limitations.