Context Layer
Your estate's knowledge, structured so an agent can use it safely.
A governed semantic layer over systems, documents, and tacit operational knowledge, with permissions preserved end to end.
Why it exists
Most enterprise AI failures are context failures, not model failures. The knowledge an agent needs is spread across schemas nobody documented, runbooks in a wiki, and expertise that lives in three people's heads. Retrieval over a pile of PDFs does not solve this, and naive retrieval quietly ignores who is allowed to see what.
How it works
Structured sources
Databases, ticketing systems, ERP and EHR records, and observability streams are mapped into a semantic model with explicit entities, relationships, and business definitions.
Unstructured sources
Contracts, runbooks, policies, and correspondence are chunked with structural awareness and indexed with citations that survive into the final answer.
Tacit knowledge capture
Operator corrections and escalation outcomes are captured as reusable context, so the expertise applied once is available to every subsequent run.
Permission preservation
Access control is evaluated at retrieval time against the identity on whose behalf the agent is acting. An agent can never surface a record its principal could not open directly.
What it gives you
- Semantic model over structured systems of record
- Structure-aware indexing with citation-preserving retrieval
- Capture of operator corrections as durable context
- Retrieval-time permission enforcement per principal
- Freshness policies and staleness detection per source