AI in production, not in a demo.
We embed AI into a real product: LLM features, assistants, Retrieval (RAG), personalized flows — with safety and cost control, for a specific measurable outcome. Not hype for the sake of hype.
AI assistants & interfaces
LLM features and assistants embedded into the product so people actually use them, not just open them once.
RAG & data
Retrieval over your data, indexing, answer-quality evaluation and hallucination control.
Safety & cost control
Guardrails, access control, logging and token-cost monitoring — so AI is predictable in production.
Integration into the product
AI is not a separate prototype but part of the product: wired to backend, analytics and real user flows.
Can you embed AI into an existing product?
Yes — that's the common case: we add an LLM feature or assistant to a live product and wire it to your data and flows.
Whose models do you use?
Chosen for the task and data constraints: external LLM providers or self-hosted models, weighing privacy and cost.
How do you control answer quality?
Through evaluation on your data, guardrails, logging and monitoring — so quality and cost are visible in production, not guessed.
What if AI errs in a critical domain?
In sensitive domains the product supports the decision and does not replace the specialist; responsibility and regulation remain with the client.
How to build an AI agent: what you need beyond the prompt
The prompt and the model are the smaller part of the job. An agent becomes usable when it has tools, explicit authority boundaries, state, a log and cost control. A step-by-step breakdown of what it is made of, how to get it to production and where it breaks.
What RAG actually is and when a business needs it
RAG means the model answers from your documents rather than from the internet, with a visible source under every claim. What it is made of, where quality is lost in chunking and updates, how to measure that it works and when you do not need it.
AI agent platforms in 2026: what a business should choose
Eleven platforms, what each of them charges for, where its ceiling is and what actually works from Russia. Every figure comes from the vendor's own public page, checked on 6 September 2026, with the source named. No rankings and no top-ten lists: the choice depends on what you are building, not on a position in a table.
Checklist: are your documents ready for RAG
PDF, 2 pages. No sign-up, no email — just the file.