AI FAQ generation + auto-update

AI FAQ generation is creating and maintaining a frequently-asked-questions section with AI: the model analyzes your materials (descriptions, documentation) and real inquiries (from support/chat) and forms clear question-answer pairs, while an update mechanism suggests new/changed FAQs when data changes or new frequent topics appear. Honestly about the human's role, this is key: the AI generates a DRAFT, not the final truth. Before publishing, answers must be reviewed by a human — because the model can err, oversimplify or output something outdated. This is especially important for answers about prices, terms, legal and technical details. We build a 'AI suggests → human confirms' process, not 'AI publishes itself'. Honestly about 'auto-update': this is not a fully autonomous system that writes truth to the site by itself. It is a semi-automatic: the AI notices that data changed or a new frequent question appeared and prepares an update — but publishing happens after review. Promising 'the FAQ updates itself without your involvement' would be dishonest and risky. Honestly about quality: answers are as good as the source materials and inquiry history; incomplete data gives an incomplete FAQ. Honestly about the effect: it saves time on writing and maintaining the FAQ and reduces support load but does not guarantee clients will stop asking. Honestly about cost: generation is paid per token, usually small for a FAQ. Honestly about access: materials and, ideally, inquiry history are needed. An important boundary: this is a FAQ; a chatbot answering live — 867, knowledge base — 868. Picture this: instead of 'the FAQ was written once and forgotten, it is outdated' — a living section that the AI helps maintain and you control. The base price starts from 35,000 ₽ (depends on volume and integrations).

• No FAQ or it is outdated and does not answer real questions.
• Writing and maintaining a FAQ manually is slow and tedious.
• It is unclear which questions clients ask most often.
• Support answers the same things, the FAQ does not help.

• Analysis of materials and real inquiries for FAQ topics
• AI generation of question-answer draft pairs
• A human-review process before publishing
• A mechanism to suggest updates when data changes
• Honest boundaries (AI — a draft; auto-update with review)
• Structure and wording for clarity
• A link with the chatbot (867) / knowledge base (868)
• Handover of the process and review with you

• A ready FAQ based on your data and real questions
• A process to keep it current (AI suggests, you review)
• Time savings on writing and updating
• Quality control (a human confirms before publishing)

• We collect materials and inquiry history; define topics
• We set up AI draft generation and the review process
• We set up update suggestions, honestly set boundaries with you

• Will the FAQ be published fully automatically, without me?

  No, and that is honest for your own safety. The AI prepares a draft, but before publishing a human reviews the answers — the model can err or output something outdated, especially about prices, terms and legal details. 'Auto-update' means the AI notices changes and suggests edits, and publishing happens after your review. Publishing without control would be risky.

• Will the AI write the FAQ perfectly the first time?

  Not perfectly — it gives a good draft that needs reviewing and fixing. Quality depends on your source materials and inquiry history: incomplete data gives an incomplete FAQ. We set up generation and the review process to get an accurate and useful result, but honestly: it is 'AI + human', not magic without involvement.

• Will this remove all support questions?

  It will reduce the load but not remove it fully — honestly. A good FAQ answers routine things and offloads support, but some clients will still write (complex, personal, non-standard). We make a FAQ that genuinely closes frequent questions; for live answers there is a chatbot (867), and the complex still goes to a human.