Become the authors
of your own tools.
We don't sell turnkey AI. We listen, we map, we build. Five ways to let machines carry what you should never have carried yourself.
You run an organization, a team, a practice. Some tasks eat time, invisible bottlenecks slow everyone down, processes could be lightened. An AI audit isn't theoretical overview. It's a dive into your reality: how you actually work, where machines can carry what you shouldn't.
Up to five days, we map your business ecosystem. Every process is scanned: document handling, communication, prospecting, customer service, internal operations. For each, three evaluations. Potential gain, technical feasibility, integration effort.
The deliverable isn't a dusty report. It's a visual process map, an opportunity score per item, and a prioritized roadmap: where to start to see measurable results in three months.
What's included
- Team interviews (1 hour minimum each)
- Complete business process mapping
- AI opportunity scoring per process
- Prioritized roadmap with estimated gains
- Final presentation with leadership
Generic AI agents know nothing about you. Custom agents learn your processes, your documents, your technical vocabulary, even your inside jokes. They converse with your clients in your tone. They process documents using your rules.
We build three kinds of agents: conversational (customer queries, FAQ, onboarding), document (ingesting reports, invoices, contracts), and orchestrated (multiple agents collaborating and escalating complex cases). Each agent has memory, limits, guardrails. You stay in control.
There is no single right way to run AI inside an organisation. There is a spectrum of solutions, from fully local setups to commercial APIs, with many shades in between. The right call depends on how sensitive your data is, your budget, your in-house skills, how much maintenance you can absorb, and the actual level of performance you need. Our job is to study those constraints with you and then compose the right mix. We do not sell a single model or an empty promise of sovereignty.
The spectrum
- Local LLM on your machine. Llama, Mistral, Qwen, DeepSeek through Ollama / LM Studio / vLLM. Data never leaves your box. Easy to launch, limited by your hardware, tolerant of some latency.
- LLM on a dedicated European server. Same philosophy on a leased GPU at Scaleway, OVH, Hetzner, Infomaniak or a private datacenter. Higher performance, regular maintenance, legally sound confidentiality (GDPR, EU law).
- Commercial APIs through a proxy with logging. You use premium models (Claude, Mistral Large, xAI Grok, OpenAI) but requests go through a proxy you control (LiteLLM, self-hosted OpenRouter). You see what goes in, what comes out, you can anonymise, audit, and switch vendors without rewriting the code.
- Hybrid architecture. Local LLM for sensitive data (HR, medical, internal legal), premium API for tasks that demand the best available model, automatic routing depending on request classification. Often the right balance for an SME or an institution.
- Multi-model orchestrator. LiteLLM, Langfuse and similar tools to route each request to the most suitable model (small fast model for triage, large model for deep analysis) and keep costs in check.
How we choose together
We start by mapping your use cases, the sensitivity of the data involved, your IT resources, your appetite (or not) for managing hardware. We lay the concrete alternatives on the table with their real costs (licences, GPU, API, human time), their level of automation, how difficult they are to administer day to day. You leave with a lucid decision, not a parachuted architecture. Then we install, configure, document, train your people. If your constraints change tomorrow, the architecture is built to migrate without breaking everything.
In 2026, AI engines read your website as a source of knowledge. Perplexity, Claude, ChatGPT with browsing, Google AI Mode, and traditional engines inhabit a new ecosystem. Different from classic SEO. It's content mapping, tactical indexing, strategic accessibility for LLMs.
Three axes: GEO/AEO (Generative/Answer Engine Optimization), LLM indexing (llms.txt, schema.org), semantic fragmentation (structuring knowledge into units that AIs can find, cite, recommend).
Training teams in AI isn't installing a mandatory online course. It's learning to think with this new tool, to question it without naivety, to evaluate what it does well and its real limits. A CEO, a technical manager, a developer each deserve a different program.
Half-day for executives, 1-2 days for technical teams, 2-day intensive workshop, or a 3-month coaching arrangement (weekly sessions, we build your agents together). On site or by video call, depending on your organisation. Available across France and French-speaking Europe, remotely anywhere else.