Knowledge management that gives one answer, with its sources
Connect the wiki, the shared drive, the ticket history and the chat archive, keep every permission, flag what is duplicated or stale, and let people ask a question instead of opening five tabs.
The knowledge exists five times, and three of them are wrong
Every organisation past a certain size has the same pattern: a wiki that was tidy two years ago, a shared drive with "final_v3" files, process answers buried in resolved tickets, and decisions made in chat threads nobody can find. Search returns too much, people ask a colleague instead, and the colleague answers from memory.
The fix is not another wiki. It is a layer that reads all of it with the right permissions, understands what is current, answers the question and tells you where the answer came from. Then the stale pages can be retired on evidence rather than on a tidy-up day that never comes.
How it works
From five silos to one cited answer
Answers reach the asker directly, with sources and dates. Content changes, retiring a stale page or merging duplicates, wait for the owner. The index is the single search surface across every source.
1. Connect and clean
Connectors read each source with its permissions. Near-duplicates are grouped, pages untouched for too long are flagged, and contradictions between sources are surfaced to their owners.
2. Index for both kinds of question
Content is chunked and embedded into a hybrid index, so "ERR-4021" and "why do refunds fail on weekends" both find the right passage. See vector search for the mechanics.
3. Answer and improve
Each answer cites its passages with dates. Questions that return weak or conflicting sources become suggested fixes: merge these, update that, retire this. Owners approve with one click.
What you get
A knowledge layer, and the evidence to tidy underneath it
Permission-aware connectors
Wiki, document store, ticketing and chat archive, synced on a schedule with group membership mirrored, built on our enterprise RAG architecture.
One hybrid index
Keyword and semantic retrieval with reranking, on pgvector or Milvus depending on scale, replacing per-tool search boxes with a single surface.
Cited answers in chat and search
Every answer shows its passages, their dates and owners, and says plainly when sources disagree or nothing relevant exists.
Content health reports
Duplicates, stale pages, contradictions and the questions nobody can answer, ranked by how often they are asked, so owners fix what matters first.
Eval set and usage dashboard
A question set built from real staff queries, scored for groundedness before launch and every sync after, plus usage by team and source.
Guardrails we build in
Accuracy thresholds: an answer is given only when retrieval confidence clears the level agreed on your eval set; below it, the system shows the closest sources and says it is unsure. Human-in-the-loop: the system never edits, merges or retires content itself; owners approve each suggested change. Audit trail: question, retrieved passages, answer and any content action are logged, with the permission check recorded for each. PII handling: personal data in tickets and chat is detected and masked at ingestion, retention follows your existing policies per source, and access is enforced at query time. The design follows our secure RAG architecture and the wider AI security and governance practice.
Typical rollout
Two sources first, then the rest
Audit (2 weeks)
Inventory sources, owners and permission models. Collect real staff questions for the eval set. Pick the two sources with the most value and the cleanest permissions.
Connect and index (3–4 weeks)
Connectors, cleaning, hybrid index and the answer layer. First content health report delivered to owners.
Pilot (3–4 weeks)
One or two teams use it daily. Groundedness tracked against the eval set, suggested fixes flow to owners, remaining sources connected.
Launch and hand over (2 weeks)
Rolled out in chat and search, dashboards live, content owners trained to work the health report. Optional ongoing tuning.
Illustrative example
What a first release usually looks like
Illustrative scenario, not a client case
A technology company with a few hundred staff has a wiki, a document drive and years of support tickets. The audit finds the wiki and drive overlap heavily, and that the most accurate process answers are in resolved tickets nobody searches. The pilot connects all three with permissions and gives the support and operations teams a question box in chat. The first health report lists a few dozen duplicate page groups and a set of pages contradicting newer tickets; owners merge and retire them over a few weeks using the report rather than a tidy-up day. By launch, most questions return a cited answer from a single current source, and the questions that still fail have become the content team's backlog, ranked by demand.
The same layer, pointed at customers instead of staff, is our customer support AI solution. New to the pattern? Start with the glossary entry for RAG.
Do we have to migrate everything into one wiki first?
No. The system connects to the sources where they are, with their permissions, and builds one index across them. Consolidation becomes a data-driven choice later, when the usage and staleness reports show which sources matter. If you do decide to move, our knowledge base migration service handles it.
How does it respect who is allowed to see what?
Permissions are read from each source at ingestion and checked again at query time, so an answer never includes a passage the person asking could not open themselves. Group changes propagate on the next sync. The design is described under secure RAG architecture.
What happens when two documents contradict each other?
The system notices, because both passages are retrieved for the same question. It shows both with their dates and owners, and raises a conflict for the content owners to resolve. Until they do, the answer says the sources disagree rather than picking one silently.
How is this different from a search box?
Search returns ten links; this returns one answer with the passages it came from, their dates and a confidence signal. Behind that sits hybrid retrieval, keyword plus semantic, so exact product codes and vague questions both work. Our RAG explained guide covers the mechanics.
What does it cost to run?
Mostly the model calls for questions and the periodic re-indexing of changed content. For most organisations the inference cost is modest; the RAG cost estimator gives a transparent figure from your document count and question volume.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.