Internal AI assistants for the team

Internal AI assistants are internal AI helpers for your team’s tasks: an assistant that answers questions about your knowledge base (policies, instructions, contracts, price lists), helps support and sales staff, drafts emails and finds what is needed in documents in seconds. We build it for your data: we connect documents and systems, set up source-grounded search (RAG), define what the assistant may and may not do, and embed it where the team works — chat, CRM, a panel. The point is to take the routine of searching and repetitive answers off people, so they can do what needs a brain. Unlike plain ChatGPT, where you paste chunks of documents by hand, the assistant searches your knowledge base itself, cites the source and is built into your work tools; and it is a helper for your team, not a public chatbot for customers on your website. Importantly and honestly, without empty promises: an AI assistant makes mistakes. It can confidently give a wrong answer (this is called a hallucination) — so we make answers cite their sources and stay within limits, but a person must double-check on important decisions; no model gives 100% reliability. Also honestly: the quality of the assistant is the quality of your documents. If the knowledge base is outdated or contradictory, the assistant will repeat its mistakes — garbage in stays garbage out, so part of the work is putting the sources in order. And about money without surprises: this is not a one-time payment — every request to the model (OpenAI, Anthropic, etc.) is charged by tokens, and the cost grows with usage; we calculate an approximate monthly cost in advance. And about data: your documents are sent to the model, so we decide upfront where that is acceptable — a cloud API (usually under an NDA and with no provider training on your data) or a local model on your server for sensitive data: personal, HR, contracts; plus we set up access control over which team members can ask what. And most importantly, honestly: this is a helper, not a replacement for an employee. It speeds the team up, it does not fire them; we will not promise that “AI will replace support”. Picture this: a new employee asks the assistant “how do I process a return” — and gets an answer from your policy with a link to the clause, instead of waiting for a colleague. The base price starts from 120,000 ₽ for the implementation; plus, separately, the cost of requests to the model.

• The team spends hours searching for answers in documents, chats and policies.
• New employees take a long time to get up to speed — there is no one to quickly answer routine questions.
• Support and sales answer the same thing by hand a hundred times over.
• The knowledge exists but is scattered — by the time you find it, it is easier to ask a person.

• An AI assistant over your knowledge base with answers that cite their sources
• Connecting your documents and systems (policies, CRM, databases, chats)
• Source-grounded search (RAG) so there is less making things up
• Limits and constraints: what the assistant may and may not answer
• Integration where the team works (chat, CRM, a panel, a widget)
• Choice of model and where the data lives (cloud or local for confidentiality)
• An estimate of the monthly request cost and cost control
• Team training and tuning to real questions

• Answers from your documents in seconds — with a link to the source
• Newcomers get up to speed faster — the assistant handles the routine questions
• Support and sales are relieved of repetitive questions
• The company’s knowledge works instead of lying dead weight

• We work through the tasks, documents and where AI really helps (and where it does not)
• We prepare the data, set up the assistant, the limits and integrations
• We test on real questions, calculate the cost, train the team, and launch

• Won’t it make things up and lie?

  There is a risk — it is a property of any language model (hallucinations). We reduce it: the assistant answers grounded in your documents and gives links to the source, and on topics outside the knowledge base it honestly says “I don’t know” instead of inventing. But it is impossible to fully rule out mistakes, so on important decisions the answer must be checked by a person. We do not promise an error-free AI — we promise a useful and honestly limited one.

• How much does it cost — is it a one-time payment?

  No. The implementation is from 120,000 ₽, and after that there is an ongoing cost: every request to the model (OpenAI, Anthropic, etc.) is charged by tokens, and the amount depends on how much the team uses it. As a rough guide, at moderate load this is usually from a few thousand to tens of thousands of roubles a month, but the exact amount depends on the model, answer length and the number of requests. We will estimate an approximate monthly bill in advance and help keep it under control; if the volumes are large, we will also cost out a local-model option.

• Will AI replace our support or managers?

  We do not promise that and do not advise counting on it. An AI assistant is good at taking off the routine — search, routine answers, drafts — but it does not understand context and responsibility the way a person does, and it makes mistakes. The right goal is to free people’s time for the complex and the human side, not to cut the team in the hope of “a robot that does it all”.