Supplier invoices that enter themselves
Custom AI development: automate, analyse, extract
Repetitive tasks, documents to process, content to produce: AI can handle them when properly scoped. We integrate solutions tailored to your business, directly inside the tools your teams already use.
AI at the service of your productivity
We automate the time-consuming tasks your teams do reluctantly: document processing, data extraction, repetitive writing. AI handles the noise, teams keep the decisions.
Before we write a line of code, we look with you at where the time is being lost, how much it represents, and whether AI is actually the right tool. Sometimes the answer is no, and that's useful to know before investing.
- AI Integration : Add intelligence to your existing tools: data analysis, automations, assistants, recommendations...
- Intelligent Document Management : Create a dedicated platform to automatically analyze, classify, extract and organize your documents.
- Content Generation : Generate clear texts tailored to your business: emails, product sheets or internal documents.
Examples.
Situations we come across often, and the tool we can make of them.
A report that writes itself
Projects.
Concrete custom development achievements
+ 150 projects delivered+ 10 years of experience
See all projectsIdentify levers to strengthen farm autonomy
An extranet to centralize all information and documentary resources
Offer a mortgage simulation that matches real lending conditions
Provide tools to master energy consumption
Carry out genomic sequencing projects
Identify levers to strengthen farm autonomy
An extranet to centralize all information and documentary resources
Offer a mortgage simulation that matches real lending conditions
Provide tools to master energy consumption
Carry out genomic sequencing projects
Identify levers to strengthen farm autonomy
An extranet to centralize all information and documentary resources
Offer a mortgage simulation that matches real lending conditions
Provide tools to master energy consumption
Carry out genomic sequencing projects
Commitments.
What we won't compromise on when putting AI in production.
Security & compliance
Encrypted data, strict isolation, documented and enforceable GDPR policy.
Response times we can hold
We pick the model that fits the need: we don't pay for GPT-4 to classify three categories.
Integration into your tools
AI arrives via API inside your business applications, not in a parallel interface your teams have to learn.
Short cycles
A first use case shipped within a few weeks, then we iterate based on real feedback from the field.
Measurable tracking
Time saved, error rate, cost per request: we report numbers, not impressions.
No AI for AI's sake
If a business rule or a script does the job, we say so. We only bring in AI where it delivers something we couldn't do otherwise.
Our approach.
A concrete method for AI projects that hold up over time.
We spend half a day mapping the repetitive tasks in your organisation: how long they take, who does them, how often. Then we compare that cost to the likely cost of an AI solution (integration, API calls, supervision). When the ROI isn't obvious, we say so and move on to the next use case.
We don't rebuild your CRM or ERP. We add AI components via API inside the tools your teams already use. Concretely: a button that summarises a ticket in Zendesk, automatic classification in your back-office, data extraction in your document system. Your teams keep their habits.
AI gets things wrong. So we always design safeguards: human validation when the decision carries weight, traceability of every call, filtering of sensitive data before it reaches the model, GDPR compliance. Your team must be able to understand why the system answered that, and take back control at any time.
Methodology.
Our approach for AI projects that deliver measurable results
We identify together the high-volume repetitive tasks in your organisation. For each one, we quantify time spent, current error cost, and expected ROI. Low-ROI use cases are dropped before any development starts.
We build a prototype in two to four weeks using a sample of your real data. We measure precision, recall, latency and cost per request. If the PoC doesn't meet the targets, we stop there — no production pipeline.
Human validation on high-impact decisions, logging of every model call, GDPR filtering before sending, model selection calibrated to need (not GPT-4 to classify three categories). AI is exposed via API inside your existing tools.
Monthly tracking of metrics (precision, drift, cost, error rate). User feedback loop built in. We refine prompts, adjust models, document each iteration.
Frequently asked questions.
What clients ask us most often before kicking off an AI project.
We compare the cost of automating with AI (integration, API calls, supervision) against the manual cost. If a deterministic rule, a script or an existing SaaS already does the job, we say so and don't engage in an AI project.
Rarely. For 90% of business use cases, fine-tuning a foundation model (GPT-4, Claude, Gemini, Mistral) or building a RAG system on top of it is enough — and it's faster, cheaper, and easier to maintain.
Depending on sensitivity: Azure OpenAI Europe, Mistral hosted in France, AWS Bedrock Europe, or self-hosted on your own infrastructure. We document the chain of custody.
We design every pipeline assuming the model will sometimes be wrong. Concretely: human validation gates, structured outputs (JSON Schema), retrieval grounding, confidence scoring, and a fallback path that escalates to a human.
A scoped use case delivers a first measurable result within 6 to 8 weeks of kick-off. PoC lands in 2 to 4 weeks, production rollout follows if the numbers hold up.
Free first scoping: rephrasing of the need, points to decide, first range. Production rollout of one use case: priced according to its complexity. Ongoing supervision and evolution: in packs of days, based on volume.
A process to digitize?
The first scoping is free, with no commitment.
Describe it in a few lines: you get a first reading of the need, the points to decide and a budget range.