Prepare the knowledge.
Parse documents, retain structure and version information, and define ownership of each source. Plan updates and deletion.
Enterprise knowledge & RAG
Your information.Answers with evidence.
Turn fragmented information into a useful knowledge system. Preserve access rules, document versions and the evidence behind an answer.
Explore the capabilityENGINEERING FOCUS
01Prepare data. Apply access rules.
02Retrieve and rerank
03Answer with sources
A vector database is only the start.
Retrieval-augmented generation, or RAG, gives a model relevant source material at the time of a request. The quality depends on how that material is prepared, selected and checked.
Find relevant evidence.
Combine metadata, keyword and semantic retrieval where useful. Test chunking, reranking and access filters on real questions.
Show the limits.
Attach source references, handle missing information and surface contradictions. Test retrieval separately from the generated answer.
A possible workflowIllustrative example.
Find the current procedure rather than an obsolete copy, then link the answer to the relevant passage.
A citation is not proof that a conclusion is correct. Verify that the cited source actually supports the answer.
A closer look
Good questions.
Straight answers.
Can access follow existing permissions?
That should be a design requirement. The integration must map source access rules and handle changes to those permissions.
Can it search recordings and images?
Yes, with an appropriate multimodal ingestion and retrieval design. Source locations and access rules still need preserving.
Technical reference: Google: multimodal embeddings (opens in a new tab)
Keep exploring