Plan the request
The selected profile, filters, source boundaries, and caller identity determine which retrieval channels may run.
CogLake Evidence Fusion
CogLake does not rely on a single similarity score. It plans each request, combines complementary retrieval channels, verifies access, and returns the evidence needed to understand every result.
A staged decision system
Retrieval breadth and ranking quality matter, but enterprise answers also require identity, permission, provenance, and reproducibility.
The selected profile, filters, source boundaries, and caller identity determine which retrieval channels may run.
Lexical, semantic, metadata, and graph channels retrieve complementary evidence without flattening their signals.
Rank fusion, reranking, and bounded graph expansion turn independent candidates into one evidence set.
Permissions, stable file identity, citations, and original links are checked and attached to the response.
Hybrid retrieval pipeline
Exact identifiers, semantic meaning, metadata, and linked context are kept as distinct signals, then combined through a controlled ranking and verification path.
Explicit search profiles
Frontend, REST, and MCP callers can use the same named behavior. Administrators can inspect which channels ran instead of relying on an opaque search mode.
Evidence and validation
Every result can carry the matching signals, stable source identity, original location, permission decision, and graph path used to reach related context.
Keyword, semantic, metadata, rerank, and graph contributions stay distinguishable.
Caller identity and source ACL decisions are enforced before results leave the platform.
Stable file IDs, original links, citations, and bounded relation paths keep context traceable.
Retrieval benchmarks, ACL isolation, update consistency, and multimodal needles protect releases.
Start with your real knowledge
Use representative documents, permission boundaries, and known answers to see how Evidence Fusion behaves in your environment.
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