AI knowledge base, shipped.
Not bought.

A regular knowledge base is a doc store with full-text search: Confluence, SharePoint, Notion. An AI knowledge base adds a retrieval-augmented generation layer on top. The system retrieves the top 3–5 relevant chunks from your corpus, the reply model (Sonnet 4.6 in our default stack) composes a grounded answer, and every claim is cited to a source doc. The honest difference is failure mode. Regular KB fails by returning 40 irrelevant pages. AI knowledge base fails by either refusing (good, that's what we tune for) or by hallucinating (bad, which is what RBAC, grounding, and the eval suite exist to prevent). When teams ask 'do we need an ai knowledge base or just better search', the answer depends on whether your queries are noun-shaped ('show me the password-reset page') or sentence-shaped ('how do I onboard a contractor in Germany'). Sentence-shaped queries need RAG.

Honest answer: it depends on your data shape. Buy Glean if your data already lives in its connector list (Okta-style IdP, 50+ stock connectors) and you want a no-engineering box; expect ~$40–60/seat/year list pricing. Buy Notion AI if 80%+ of your knowledge already lives in Notion. It's the cheapest option, and a notion ai alternative isn't needed when you're already on Notion. Buy Guru if your knowledge is card-shaped and your support team already curates Guru cards. Build a custom ai-powered knowledge base with us when retrieval has to span a private corpus, RBAC must mirror a non-standard identity provider, or your eval target needs custom scoring. We say so in the $3K audit if you should buy. We've recommended Glean to two of the last twenty audit clients; neither needed a custom build. Looking for a glean alternative usually means the price is the friction, not the product; we'll tell you whether a custom build is cheaper across 3 years.

Six stages, every query. Retrieve: hybrid search (BM25 + dense embeddings) over your corpus, top-k 20. Rerank: bge-reranker-v2 cuts to top-k 5 with cross-encoder scoring. Ground: the system prompt instructs the model to answer only from retrieved chunks or say it doesn't know. Cite: every answer span is tied to a source doc and offset. Answer: Sonnet 4.6 composes the reply, streamed. Log: Langfuse logs the query, retrieved chunks, citation accuracy, refusal rate, and grounding rate; we eval nightly against held-out questions. The same rag chatbot anatomy works whether the surface is a Slack bot, a web portal, or an embedded panel in Zendesk. Most knowledge base quality issues are retrieval issues, not generation issues, so we tune retrieval (chunking, top-k, reranker) before tuning prompts.

Three engagement tiers, same pricing as our other AI services. A 1–2 week ai knowledge management audit is $3,000: source catalog, eval-set design, architecture and model pick, RBAC mapping, 90-day roadmap. A pilot is $10,000–$25,000 fixed price, 4–6 weeks: one production knowledge base shipped end-to-end with retrieval eval and RBAC. A continuous knowledge management ai engagement is from $5,000 per month: embedded PM, engineer, and ops analyst shipping new sources and tuning the live system. Run cost (model calls + vector DB + monitoring) lands at $300–$3,000 per month depending on query volume and corpus size. Ai for knowledge management is cheaper to build than most teams expect once retrieval is right. Most of the cost ends up in connector engineering, not model calls.

Yes, but the shape changes enough that we usually ship it as a customer-service chatbot rather than a knowledge base. Same retrieval anatomy, different audience, different latency budget (sub-2-second instead of 5+), different review process (public-only sources, no RBAC). When clients ask about ai knowledge base software for customer self-serve, we route them to the chatbot pillar: partners, not duplicates. The KB pillar owns internal Q&A, agent-assist, and enterprise search; the chatbot pillar owns customer-facing single-turn. We've shipped both off the same RAG corpus more than once: internal version answers freely, customer-facing version is scoped to public docs only and gated behind a confidence threshold.

Most pilots ship in 4–6 weeks after a 1–2 week audit. Realistic distribution: simple knowledge bases (single source like Notion, 1,000–5,000 docs, English-only) in 3–4 weeks. Mid-complexity ai knowledge base software (3–5 sources, 10,000+ docs, RBAC against Okta or Azure AD) in 4–6 weeks. Complex (regulated industry with PHI or PII handling, multilingual across 5+ languages, custom IdP, 50,000+ docs) in 8–10 weeks. The audit phase tells us which bucket you're in before any pilot contract. We don't quote a 30-day knowledge base for work that takes 90 days. The walk-away point is week 2: if retrieval baseline won't hit 0.85+ recall@10 on your eval set, we stop and recommend either more corpus prep or a different approach.

Four layers. Identity-provider mirror: retrieval is scoped at query time against your existing IdP (Okta, Azure AD, Google Workspace, or custom). Users see only what they'd see in the source system; the AI knowledge base never escalates privilege. PII scrub at ingest and at query: we mask PII in the corpus before embedding and re-scrub at reply time, with allow-lists for fields the workflow legitimately needs. Audit log every query: who asked, what was retrieved, what was answered, what was cited. Stored in your warehouse, not ours. Refusal-by-default for compliance-sensitive sources: finance and legal corpora ship with a 0.95+ grounding-rate floor; sub-threshold queries refuse with a 'I don't have a grounded answer' message rather than guess. Regulated industries (healthcare, finance, legal) get a fifth layer: row-level encryption on the vector store and a separate audit-log retention policy.

Three cases. (1) Glean fits your connectors. If your data lives in their stock connectors, your IdP is Okta or Azure AD, and you want a no-engineering box, buy Glean. We'll tell you so in the audit. (2) Notion AI covers 80%+ of your knowledge. If your wiki, runbooks, and HR docs are already in Notion and adding cross-source retrieval isn't worth the engineering, stay with Notion AI at $10/seat/month. (3) You have a 10-person platform team and a one-week deadline. If you already have engineers fluent in LlamaIndex or LangChain, pgvector, and an eval framework, the build is faster in-house than the audit-to-pilot cycle. We'll point you at the open-source stack we'd use. The $3K audit exists partly to detect these three cases before anyone signs a pilot. If the answer is 'hire someone else' we'll say so, same as we do on chatbot, agent, and integration audits.