AI & Machine Learning

The artificial intelligence
working for
your business

Intelligent chatbots, recommendation engines, predictive models, NLP — we integrate AI directly into your processes to automate, personalise and make better decisions.

Discuss my AI project View examples

AI Model — Customer Classification — WebProducts

Conversion probabilities — customer #2847

Imminent purchase 92%

Upsell potential 74%

Churn risk 18%

AI Assistant — Customer Support

Where is my order #4521?

Your order #4521 is being prepared. Estimated delivery tomorrow before 6pm.

ce que nous livrons

De la donnée brute à la valeur métier

Pas de démo générique, pas de modèle pré-packagé revendu. Chaque solution IA est conçue, entraînée et intégrée pour votre contexte précis.

Chatbots & assistants intelligents

Agents conversationnels basés sur GPT-4, Claude ou des modèles open-source — support client 24/7, qualification de leads, FAQ automatisée et prise de rendez-vous.

Moteurs de recommandation

Personnalisation produits, contenus et offres en temps réel — filtrage collaboratif, content-based filtering et approches hybrides intégrées à votre plateforme.

Prédiction & scoring

Prévision de ventes, scoring de leads, détection de fraude, prédiction de churn — des modèles entraînés sur vos données pour des décisions plus rapides et plus précises.

Traitement du langage naturel (NLP)

Analyse de sentiment, extraction d'entités, classification de textes, résumé automatique et traduction — appliqués à vos emails, avis clients, documents et tickets de support.

LLM Integration & AI APIs

Connect OpenAI, Anthropic Claude, Google Gemini or local models (LLaMA, Mistral) directly into your web applications, internal tools or existing workflows.

Autonomous Agents & RAG

AI agents capable of planning and executing multi-step tasks, augmented with your own data via RAG (Retrieval-Augmented Generation) for precise and contextualised responses.

concrete examples

What our clients ask us

Real situations, tailor-made AI solutions. If your need resembles one of these cases, we can help you.

Customer support chatbot for e-commerce

An online store receives 200+ tickets/day on order tracking and returns. We deploy an AI assistant trained on their catalogue and policies — capable of resolving 70% of requests without human intervention.

GPT-4 RAG API WooCommerce 70% ticket reduction

Product recommendation engine

A marketplace wants to personalise the display for each visitor. We build a recommendation engine based on browsing history, purchases and similar profiles — with +35% conversion rate observed.

Collaborative filtering Python scikit-learn API REST

Predictive customer churn scoring

A SaaS operator wants to identify at-risk customers before they unsubscribe. We train a model on their CRM data and integrate it into their dashboard with automatic alerts for the Customer Success team.

XGBoost Python CRM API Alerts dashboard

Automated customer review analysis

A retail brand collects thousands of reviews each month but doesn't have time to analyse them. We deploy an NLP pipeline that automatically classifies, tags and summarises feedback by theme and sentiment.

NLP HuggingFace Sentiment analysis Insights dashboard

technologies

The AI stack that powers your projects

We choose models and frameworks based on your real needs — not trends. Maturity, performance and running costs first.

Modèles & LLMs

GPT-4o (OpenAI) Claude (Anthropic) Gemini (Google) LLaMA / Mistral

ML & AI Frameworks

Python PyTorch TensorFlow scikit-learn HuggingFace

Intégration & infrastructure

LangChain ChromaDB / Pinecone FastAPI Docker VPS Linux

how it works

How your AI project unfolds

From feasibility exploration to production — an iterative, transparent process grounded in your real data.

Needs analysis & AI feasibility

We frame your business problem, assess the quality and availability of your data, and give you an honest recommendation: AI or not AI, which model, what timeline, what running cost.

2–3 days

Data collection & preparation

Cleaning, normalisation, annotation and structuring of your data. We build a robust data pipeline — data quality determines 80% of model quality.

1–2 weeks

Prototyping & model training

Model selection and fine-tuning, experiments, metric evaluation (accuracy, recall, F1, BLEU…) and iterations until your performance targets are met.

1–4 weeks

Integration & business testing

The model is exposed via a secure API and integrated into your application or workflow. Load tests, robustness tests and validation with your teams on real cases before going live.

1–2 weeks

Deployment & continuous monitoring

Production deployment with model performance monitoring over time (data drift, degradation). We deliver monitoring dashboards and remain available for retraining and updates.

1–3 days

frequently asked questions

Questions we're often asked

Do I need a lot of data to start an AI project?

It depends on the type of project. For LLMs (chatbots, agents), little or no proprietary data is needed — we rely on pre-trained models that we adapt with your documents via RAG. For custom predictive models, a few thousand examples are often enough to achieve useful results. The first step of our process is precisely to evaluate this honestly with you.

What is the difference between AI and Machine Learning?

Machine Learning (ML) is a subcategory of AI — it is the technique by which systems learn from data without being explicitly programmed. AI in the broad sense also includes expert rules, planning and LLMs. In practice, most AI projects we deliver combine classical ML (for predictions) and LLMs (for language), depending on the need.

How does AI integrate with my existing tools?

We expose each AI solution via a secure REST API — which allows it to be connected to any tool: website, mobile application, CRM, ERP or e-commerce platform. We can also integrate it directly into your automation workflows (Make, n8n, Zapier) without requiring a redesign of your existing infrastructure.

How much does a custom AI project cost?

The budget varies with complexity: a chatbot based on an existing LLM can be delivered in a few weeks for a few thousand euros, while a custom-trained predictive model with a full data pipeline can represent a 2 to 3 month project. You should also budget for API costs (OpenAI, Anthropic) or continuous model hosting. Submit your requirement — we respond within 24h with a detailed and honest estimate.

Are my data shared with public models?

No, not without your explicit consent. Depending on your data sensitivity level, we can opt for locally hosted models (LLaMA, Mistral on your VPS), use the OpenAI/Anthropic API with enhanced privacy options, or set up a RAG architecture where your data stays within your infrastructure. Confidentiality is a technical design criterion, not an option.

The artificial intelligence working for your business