Services

AI implementation

AI isn't a product, it's a capability. And like every capability, it only adds value when integrated into a real process. Forget generic chatbots, we talk about AI applied to your operations.

Automatic document classification, structured data extraction, content generation, semantic search over your internal knowledge. Integrated where you need it.

Signs this is for you

If any of these sounds familiar, we can probably help.

We receive hundreds of documents a month and someone has to read and classify them one by one.

We want to use AI but we don't know where to start without falling into the generic chatbot.

We have huge volumes of internal data that nobody searches because it takes half an hour to find anything.

We tried ChatGPT for something concrete and it half works, but we don't know how to industrialize it.

We're worried about privacy and we don't want to send our data to a public model.

We need AI to decide, not just suggest, and that means integrating it into our processes.

Concrete use cases

We solve a real problem first. The technology comes afterwards.

Multi-provider models

OpenAI, Anthropic, Google, local models. We use whatever fits best.

Privacy & control

Architectures designed to keep your data where you want it.

Measurable

Objective evaluations to know whether the model actually works in your case.

How we work with you

Three clear phases from the first call until the system is running in your operation.

Phase 01

Understanding your operation

We talk to your team and map real workflows, bottlenecks and priorities. We leave with a clear picture of what to solve first.

Phase 02

Technical design

Architecture, integrations with your current systems, risks and a phased plan. Before writing any serious code, we agree on the how.

Phase 03

Go-live and support

Controlled rollout, team training, monitoring and early adjustments. We stay with you during the first weeks until everything runs on its own.

What we build

- Automatic classification of support tickets
- Structured data extraction from PDF invoices
- Semantic search over internal documentation
- Automatic report generation from operational data

What you take home

Concrete outcomes at the end of the engagement. Not slides — things that run.

Answers and documents generated in seconds instead of hours.
Automatic classification of incoming emails, orders or incidents as they arrive.
Information extracted from documents (delivery notes, invoices, PDFs) without keying it in by hand.
Recommendations based on your own data, not generic answers.
An internal assistant that knows your processes and answers with the right context.
Full control over which data is used and where, with nothing leaving the company.

FAQ

Do you use local or cloud models?

Both. The decision depends on the case: data privacy, acceptable latency and expected volume. Sometimes cloud wins on quality, sometimes local wins on sensitive data or long-term cost.

How do we know the AI actually works in our case?

We define objective metrics at the start of the project (accuracy, recall, human-acceptance rate, cost per call) and build an automated evaluation suite that runs with every change.

What happens to our data? Do providers train models with it?

No, unless you explicitly decide otherwise. We use providers with clear no-training-on-customer-data policies, or local models when privacy is critical.

Can we adapt a model to our internal language or customers?

Yes. Via prompt engineering, RAG over your documentation, or fine-tuning when the case justifies it. We pick the cheapest technique that solves the problem, not the most sophisticated one.

Want this service adapted to your context?

We can define a first scoped proposal in a short discovery call, with clear priorities, timeline and expected outcomes.

Book a call