AI knowledge-base assistant

An AI knowledge-base assistant is a system that, based on your documents (via RAG — 893: retrieving relevant fragments + the model answering from them), gives accurate, source-linked answers to questions in natural language. It suits customer support, onboarding and helping employees (internal assistant — 884). Honestly about the main limitation, this is key: the assistant knows only what is in your base. If information is absent from the documents, outdated or poorly written — the answer will be accordingly: 'garbage in — garbage out'. So value directly depends on the quality and freshness of your materials; part of the work is helping structure them. Honestly about hallucinations: even relying on your data the LLM may sometimes distort or make things up — we reduce this via RAG and source links (so the answer can be verified) and guardrails 'answer only from what was found, otherwise say it was not found'. Errors cannot be fully eliminated, so critical questions need human review. Honestly about 'not omniscient': it is not an expert but a fast search-and-answer over your documents; beyond the base it must not make things up. Honestly about the effect: it speeds up finding information and offloads people but does not replace expertise in complex cases. Honestly about cost and privacy: answers are paid per token; data goes to the model — for sensitive information we discuss private/local options. Honestly about access: the knowledge-base materials themselves and their updating are needed. An important boundary: this is a knowledge-base assistant; general-purpose chatbot — 867, RAG infrastructure — 893, internal employee assistant — 884. Picture this: instead of 'search a pile of documents yourself' — ask in natural language and get an answer with a link. The base price starts from 55,000 ₽ (depends on base volume and quality).

• Information is scattered across documents — slow to find.
• Employees/clients ask the same questions about policies.
• Keyword search does not understand the query's meaning.
• Answers without a source link — they cannot be trusted.

• Collecting and structuring your knowledge base
• RAG: semantic fragment retrieval + answer from them (893)
• Answers with a source link (verifiability)
• Guardrails: answer only from what was found, else 'not found'
• An honest assessment of your materials' quality/completeness
• Indicating boundaries (depends on data; verify the critical)
• Token cost and privacy options
• Handover and review with you

• Answers from your documents in natural language
• A source link — the answer can be verified
• Fast access to information, fewer repeat questions
• Honest boundaries (only from your data; not omniscient)

• We collect and assess the knowledge base; clarify critical topics
• We set up RAG, source links, guardrails
• We test answers, honestly show the data dependency with you

• Will the assistant know answers to any questions?

  No, and that is honest: it answers only from your knowledge base. If information is absent from the documents or outdated — there will be no answer (or it will be incomplete). It is not an all-knowing expert but a fast search-and-answer over your data. So value depends on the quality and freshness of materials, and part of the work is helping put them in order.

• Can it err even relying on my documents?

  Yes, it can, and we honestly reduce it. Even relying on your data the model sometimes distorts or makes things up. We use RAG and source links (so the answer can be verified) and guardrails 'answer only from what was found'. Errors cannot be fully eliminated, so critical questions need human review — we build this in rather than promise infallibility.

• How is this different from ordinary site search?

  Ordinary search looks by keywords and returns a list of pages. The AI assistant understands the question's meaning in natural language and gives a ready answer with a source link. It is more convenient but also more responsible — so we make it verifiable (links) and with honest boundaries. Semantic search as a separate service is 874.