Artificial Intelligence

It doesn’t have to be. We design private or on-premise AI when confidentiality requires it.

With a narrow, high-value use case; we measure results and scale from there.

Solve real tasks: handle customers over chat, answer questions about your internal documents, classify and process emails or invoices, and assist sales and support. It is not generic magic; it is applied to a specific process where it saves time or money.

We use OpenAI, Azure OpenAI, Claude, Gemini or Ollama as the case fits, orchestrated with LangChain. RAG is the technique that makes the model answer using your own documents, so responses are accurate and grounded in your information rather than made up.

On the use case, the volume of documents or queries, whether the model runs in the cloud or private on-premise, and the integrations with your systems. We start with a narrow pilot to validate value before scaling spend.

Models can be wrong, which is why we use RAG over your data, validations and traceability to reduce it. AI is not worth it when a simple rule already solves the problem, or when you tolerate zero error without human oversight; there we design with review in the loop.

It depends on the case, but a narrow pilot can be tested relatively quickly because we tackle a single high-value process. We measure real results and, if it convinces, we scale from there.

Not always. For assistants over documents, your existing documentation is enough; predictive cases do need quality history. We assess what data you have before proposing the approach.

Yes, and that is exactly where we design private or on-premise AI —for example with Ollama— so data never leaves your environment. Write to [email protected] to assess your case with privacy as a requirement.