Enterprise Knowledge Agent
Every answer your company already owns, behind one question.
The answer is almost never missing. It is in a Jira ticket from last March, a Confluence page nobody linked, a commit message, and the head of an engineer who left. This platform indexes all of it and puts one question box in front — and returns an answer with citations, a confidence score, and each source marked as primary, supporting or obsolete. It is not a chatbot bolted onto a wiki. It is a knowledge layer with permissions enforced before anything reaches a model.
- Connectors
- 7 sources
- Agents
- 8, orchestrated
- Answers carry
- Citations + confidence
- Audit log
- Append-only
- Connector-based indexing
- Cited answers
- Knowledge graph
- Agentic RAG
What Enterprise Knowledge Agent does
Seven sources, one index
GitHub, GitHub Enterprise, GitLab, Bitbucket, Jira, Confluence and SharePoint. Code is chunked along its syntax tree and documents along their headings, so a retrieved fragment is a whole thought rather than 512 tokens ending mid-sentence.
Permissions enforced before retrieval
Every vector search carries the user's identity and groups as a server-side filter, applied before results are returned. No content a person cannot open ever reaches the model that writes their answer.
Answers with their sources attached
Each answer arrives with citations, a confidence score, and every source classified as primary, supporting or obsolete — so a stale wiki page is visibly stale rather than quietly authoritative.
Eight agents, one state machine
Query understanding, source selection, retrieval, code analysis, document analysis, relationship mapping and response generation run as an orchestrated graph with shared state, not as one prompt asked to do everything.
The trace from requirement to release
A knowledge graph links requirement to Jira story to the code that implemented it to the API it calls to the test that covers it to the release that shipped it. Answering “why is this here” stops being an archaeology project.
Nothing is searched by default
Scope is always chosen. The platform will not search every connected source unless asked to — an accident of breadth is how confidential material ends up in an answer.
An audit log that cannot be edited
Every query and every answer is written to an append-only log with chained hashes, enforced at the database level, with personal data masked before storage and a two-year retention floor.
Feedback that tunes retrieval, not the model
Helpful, partly helpful and incorrect ratings improve ranking and prompt heuristics. They never fine-tune a model on your content — a boundary worth keeping explicit.
How a question gets answered
- 1
Connect
Sources are attached with scoped credentials held in a secrets vault, never in config.
- 2
Index
Content is extracted, normalised and chunked — by syntax tree for code, by heading for documents.
- 3
Ask
A question in plain language, scoped to the sources you choose. Never all of them by default.
- 4
Retrieve
Semantic search filtered by your permissions server-side, before a single result is returned.
- 5
Answer
Agents draft, cite and score the answer, classifying each source as primary, supporting or obsolete.
- Engineering organisations where the context left with the people
- Teams onboarding into a codebase nobody fully remembers
- Regulated businesses that must show who saw what, and when
Questions people ask
The architecture is built to make that hard rather than to promise it cannot happen. Permission filtering runs inside the search itself, server-side, before results leave the store — so retrieval cannot return a document the user lacks access to, and the model only ever sees what retrieval returned.
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