01
First we decide if it’s worth it
Not every feature needs AI. Before writing a single line of code we analyse where it adds real value to your product and where it only adds cost and risk. If the answer is “not here”, we tell you.
02
Your AI answers with your information, not with what it imagines
AI models are statistical: they can give different answers to the same question, or make facts up. We connect the model to your documents, catalogues and databases so it answers with real, verifiable information. The quality of a system like this depends almost entirely on how well that retrieval layer is built — and that’s where we put the effort.
03
If you don’t measure, you don’t improve
We build a dedicated evaluation system: clear criteria, scores and automated tests for every part of the flow. It works like the tests in your software — you know whether each change improves or degrades the answers, instead of flying blind.
04
Security from input to output
There are well-known cases of corporate chatbots that wrote code because a user asked them to. Others leak internal instructions or data that should never leave. We put filters before the message reaches the model and after the answer comes back: no sensitive data going out, no out-of-place answers reaching the user.
05
From answering to acting
Once the system answers well, the next step is letting it execute: open a ticket, update a record, kick off a process. We add that when the system is mature, and always with control over what it can and cannot do.
06
If your data cannot leave, we set it up in your house
Some information simply cannot travel to an external API: patient records, client files, contracts, payroll. When you send it out, you stop controlling where it ends up, how long it’s kept and under which jurisdiction. With a private installation that conversation is over: data enters and leaves your infrastructure, and there’s a record of everything. For your DPO, your auditor or your end client, that’s the difference between being able to justify it and not.
And we don’t just install the model — anyone can do that. We build the full platform: GPU management, scaling, per-user and per-department access control, query logging, monitoring, backups and updates. On Docker or Kubernetes, on your hardware or your European private cloud.
07
The model by criteria, the cost under control
We analyse what you actually need. In many cases a well-tuned open model does the job at a much lower operating cost; in others a mixed scheme pays off: the sensitive part is processed inside, the generic part can go out. We give you the numbers so you decide with data.
On external APIs, we use cheap models for the simple work and expensive ones only when needed, and we cache what repeats: the bill drops very noticeably without touching quality. On your own infrastructure, the investment is upfront and the monthly cost is stable — past a certain volume it pays off, and we’ll tell you frankly where that threshold is in your case. If you don’t reach it, we won’t sell you servers.
08
No lock-in to anyone, not even to us
We use open software and standards. The platform is documented and operable by your team. If tomorrow you want to switch providers or bring it in-house, you can.
09
Continuous operation
Models get updated, hardware fails and load changes. We take care of the maintenance: patches, performance, availability and incidents, with the service level we agree on.
What you get
An AI system in production — built into your product or installed on your own infrastructure — with continuous evaluation, controlled cost, reviewed security, full traceability and metrics you can show to management.
Let’s talk about your project
Tell us the use case and we’ll tell you whether it’s worth it — and where it should live.