Knowledge Systems
Retrieval-augmented knowledge with a citation behind every answer.
Status shown is the honest current state of this area: Partial. Nothing on this page implies a certification, licence, partnership, government approval or endorsement.
What this service is
Knowledge Systems are retrieval-augmented (RAG) systems built on reviewed sources: ingestion, chunking, embeddings and hybrid semantic search, with human review of what enters the knowledge base and a citation attached to what comes out.
Core capabilities
- Source ingestion with publisher, URL and publication date recorded
- Chunking, embeddings and hybrid keyword + semantic retrieval
- Human review before a source becomes answerable
- Provenance labelling: official source, BD AI summary, or needs verification
- Row-level access control so answers respect who is asking
Typical use cases
- A public body's information must be answerable without distortion
- Internal documentation is large and staff cannot find the right page
- Answers must be traceable to the exact source that justified them
Benefits & outcomes
- Answers grounded in sources you approved
- A visible distinction between verified and unverified material
- Retrieval quality you can inspect rather than trust blindly
Delivery & process
- Step 1
Requirement review
A written review of the requirement, data involved and who needs access.
- Step 2
Scope & proposal
A scoped proposal issued as a referenced BD AI document that can be verified publicly.
- Step 3
Build & review
Implementation under BD AI standards for provenance, access control and audit logging.
- Step 4
Handover
Documentation, access handover and an agreed support arrangement.
Talk to BD AI
Engagements start with a written requirement review. Quotations are issued as referenced BD AI documents that can be verified publicly.