Generative AI & Agents
“I want to automate and save my teams time.”
Generative AI should speed your teams up, not replace them or introduce new risks. We build supervised copilots and agents, grounded in your data and your processes, for time savings that are real and measurable.
Copilots that work for your teams, not instead of them.
We set the scope and the safeguards before we deploy, so every agent stays useful, reliable and under control.
- RAG (Retrieval-Augmented Generation) systems built on technical documentation and business content.
- Business copilots that help your teams with their everyday tasks.
- Process automation agents: document handling, standard replies, information extraction.
- Secure integration of language models into the tools you already use.
The kind of projects we deliver.
RAG copilot on a technical corpus
RAG copilot for querying the technical documentation of an industrial site, with sources cited.
Document automation agent
An agent that automates the handling of administrative files, from information extraction to pre-qualification.
Technical writing assistant
Generative AI assistant that helps write technical reports, grounded in internal standards.
Examples of the kind of projects our teams deliver.
Focused on results
A pragmatic approach built around real results and efficiency, not technology for show.
Scoping first
We define the project before we talk about tools, so we avoid dead ends.
Real-world delivery
We take solutions all the way to production, not just to prototype.
Plain speaking
Complex topics made clear and practical, with no needless jargon and no overpromising.
The questions we hear most often.
What is a RAG system, and how is it different from a standard chatbot?
RAG (Retrieval-Augmented Generation) connects a language model to your documentation and real business data, so its answers are grounded in your context instead of being generic or approximate.
Will AI agents replace our teams?
No, that’s the opposite of our approach. We use generative AI to support your teams by taking repetitive tasks off their plate, not by taking over the decisions that matter.
How do you make sure the agent doesn’t give wrong or made-up answers?
By grounding answers in your actual documentation through RAG, by limiting what the agent is allowed to do, and by building in verification checks before any sensitive action.
Can an AI agent be integrated into our existing tools (CRM, ERP…)?
Yes, and that’s often most of the work: integrating the language model securely into the tools you already have, rather than building yet another standalone tool.
Which language model providers do you work with?
We use models from the leading providers on the market, such as Anthropic, and choose the one that best fits your use case and your cost and confidentiality constraints, rather than defaulting to a fixed choice.
How long does it take to deploy a first business copilot?
A first, well-targeted use case (a document automation agent or a writing assistant, for example) is usually deployed within a few weeks. Tell us about your context for a precise estimate.
Have an automation to scope?
Tell us about your context. We’ll get back to you shortly to identify the use cases that make sense.
