How we build AI products, without the hype.
Notes from engineering practice: what it takes to get an AI feature from a demo to production, where such systems break and when they are not needed at all. No round-ups, no invented numbers.
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.
→ AI agents10 min readn8n or custom development: where the line runs
n8n covers more than people assume, and we regularly recommend starting there. But a workflow builder has a boundary, and it runs not through scenario complexity but through the cost of a mistake, concurrency and access rights. How to work it out in advance and when writing code becomes the cheaper option.
→ Process automation9 min readOff-the-shelf chatbot or custom: counting the cost of ownership
An off-the-shelf bot launches in a week and looks ten times cheaper. The difference shows up not in month one but where the bot has to look inside your systems, answer from your documents and hand over to a human honestly. What the two-year bill is made of and how to choose without overpaying either way.
→ AI chatbots9 min readChunking documents for RAG: why chunks matter more than the model
A bad RAG answer almost always comes from chunking rather than from the model. How to split policies, contracts, scanned PDFs and support tickets, what breaks on a corpus of tens of thousands of long documents, and how to tell that chunking is the culprit.
→ RAG & knowledge base8 min readHow to update a RAG knowledge base without stopping the system
A demo lives on a static corpus, production on a moving one. Incremental updates instead of full reindexing, swapping the index without a restart, deleting documents, and which metrics reveal retrieval degradation before users report it.
→ RAG & knowledge base9 min readHow 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.
→ AI agents8 min readWhat 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.
→ RAG & knowledge base