Private AI that never hands your data to the outside world

Convenient AI services run on a simple rule: everything you send them has gone to someone else's servers abroad. For casual correspondence, that's tolerable. For contracts, medical data, financial reports and clients' personal data, it's a risk — and you'll answer for it, not the service provider. And from 2026, Azerbaijan is tightening its requirements for processing personal data, so "we just use a foreign bot" stops being a safe answer. Private AI removes the source of the risk itself: the model runs wherever you decide, and the data never leaves your perimeter.

Selecting an open model to fit your task and language (including work in Russian and Azerbaijani). GPU deployment — on our infrastructure as a service, or on your own hardware (on-premise, up to a fully air-gapped environment with no internet access). Connection to your documents and systems (knowledge-base search, RAG). Configuration, answer-quality checks and ongoing support. Where needed — fine-tuning the model to your industry's vocabulary.

Private AI is justified when at least one of two things is true. First — the data is sensitive: clients' personal data, contracts, medical or financial information, trade secrets, and sending them to a foreign service is off the table by law or by common sense. Second — the volume is large and constant: you push so much text through AI every day that the cloud-service bills can exceed the cost of running your own model.

And when you don't need it, we'll say so plainly. If you process modest volumes with no sensitive data, an ordinary cloud service is almost always cheaper and simpler: your own infrastructure doesn't always pay off, and we won't sell it to you "just in case." Where the line falls — we break it down in the article «Self-hosted AI server or a cloud service».

Where the model runs is your call: our infrastructure in Azerbaijan or your own site. For the most sensitive data, a fully closed environment with no internet access is possible — the model runs offline, and the data physically cannot leak out. Access control and logging (recording who accessed which data and when) are in place from day one, not "once we get around to it."

We use open models with free licenses (for example, the Qwen, Mistral, DeepSeek and GLM families) — they can be used legally in commercial products and fine-tuned without locking you to a single vendor. This is a matter of principle: you don't depend on someone else's service pricing, or on them changing the rules or cutting off access tomorrow. We pick the specific model and hardware configuration to fit your task, volume and budget — not "the most powerful," but enough.

We work the "10% Path" way: first we analyze the task and run the economics (your own AI versus a cloud service) — and that's free. If the solution is justified, we lock in the estimate, deploy a pilot, and only then move to full rollout and support. You don't pay for infrastructure blind. Pricing for hosting and support is on request and calculated for your data volume and required load. The details — how we work.

We use these points to decide whether you need your own AI at all — and in what configuration. You can run through the same checklist yourself before we even talk:

• Data sensitivity — are there personal, medical or financial data, or trade secrets, that can't be handed to someone else's cloud? If yes, that's an argument for private AI right there, regardless of volume.
• Daily volume — how much text actually runs through the AI. Small volume → a cloud service is usually cheaper; large and constant → your own model starts to pay off.
• Legal data requirements — do you fall under requirements for storing and processing personal data (relevant for Azerbaijan from 2026)?
• Internet access — is it permissible in your environment at all, or do you need fully offline operation?
• Quality per language — what language are the texts in (Russian, Azerbaijani, English) and how narrow is your vocabulary — that drives model selection and fine-tuning.
• Who will run it — do you have people to operate it, or do we take that on as a service?

The specific break-even thresholds in manats and tokens are worked out for your case in the free assessment — there is no single one-size-fits-all figure here.

A company wants an AI assistant for its internal regulations and contracts: an employee asks in plain language, and the system finds the answer across hundreds of documents. Handing the contracts to a foreign service is not an option. Here's how we usually solve this: analyze the task and data → choose an open model to fit the documents' language → deploy in a closed environment → connect document search (RAG) with a check that answers rest on real sources, not invented ones → verify quality on real questions → ongoing support. The goal: the assistant saves hours of searching, while not a single document ever leaves the company's perimeter.

• What is private AI? It's a language model (LLM) deployed on your site or ours, where the data doesn't go to a third-party provider abroad. You control both where the data sits and which version of the model you use. Other names for the same thing: local AI, private AI, on-premise LLM.
• Is it always more expensive than a cloud service? No — it depends on volume. At small volumes the cloud is usually cheaper, and we'll tell you so. Your own model wins at large, constant volumes, or where the data simply can't be handed outside.
• How much does private AI cost? There's no single price: it depends on the model chosen, the load volume and whose infrastructure we deploy on. We lock in an exact estimate for free after analyzing the task — so you know in advance what you're paying for.
• Why is private AI better than a foreign AI bot for a company? Because sensitive data never leaves the perimeter and doesn't fall under someone else's jurisdiction. In convenience and class of tasks the difference is small; the difference is in control over your data and in compliance.
• Can it run with no internet at all? Yes. For sensitive environments we deploy the model offline — it physically has no channel to send anything outside.
• What language does it work in? We pick the model to fit your data's language — Russian, Azerbaijani, English. For narrow industry vocabulary, fine-tuning is possible.
• What if we don't have our own servers? Not required. You can host on our infrastructure as a service (with placement in Azerbaijan per your requirements) — and the data still doesn't go to someone else's cloud.
• Who will maintain it? We can take operations on ourselves — updates, monitoring, recovery. Or hand it to your team with training. Your call.