An AI agency
for real business problems.

Only if you decide so. Three options exist and we arbitrate with you: commercial APIs (Claude, GPT, Gemini) under contractual no-training and zero-retention terms; European models (Mistral) or EU-region deployments on Azure, AWS Bedrock or Google Vertex; or self-hosted open models (Llama, Qwen, Mistral) on your own infrastructure — nothing leaves your walls.

By construction, not by hope. We ground answers in your documents (RAG) and display cited sources. We add guardrails: explicit refusal when the information is absent, cross-checking on critical answers, human validation on irreversible actions.

Above all we measure: a reference question-and-answer set is built during framing and replayed on every change.

Retrieval-Augmented Generation. Instead of asking the model what it “knows”, we first retrieve the relevant passages from your documents, then ask the model to answer only from those passages. The result is current, verifiable and specific to your company — with no model retraining, so no associated cost or delay.

Almost never at the start. In more than nine cases out of ten a good RAG architecture on a market model beats fine-tuning, at a tenth of the cost, and updates instantly when your documents change. Fine-tuning keeps value for enforcing a strict output format or a specific brand tone.

Yes, and it is handled during framing rather than afterwards. We produce the processing map, legal basis, retention periods and an impact assessment where required. We classify the system against the AI Act risk tiers and implement what the tier demands: user disclosure, human oversight, logging and technical documentation.

A usable proof of concept in 3 to 5 weeks and production in 3 to 4 months for one use case. We deliberately start narrow and measurable — one department, one document type, one user group. That is what proves value before scaling.