My services
From intelligent chatbots to data infrastructure and process automation.
RAG intelligent chatbots
- Custom assistants that understand your internal documents (PDFs, emails, knowledge bases)
- Context memory across conversations, answers based exclusively on your data
- Every response is traceable to its source
- Ideal for customer support, internal assistance or document management
- EU hosting available
LangChainPythonWeb fullstackInfrastructureQdrant
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Data engineering
- Reliable data collection and transformation pipelines, from zero to production
- Modern tooling: Python, dbt, Airflow
- From connecting new sources to preparing datasets for model training
PythonDBTSQLAirflow
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AI automation
- Document classification, drafting standard replies, information extraction, automatic summaries
- Agents that orchestrate multiple AI models and connect to your existing tools (CRM, APIs, databases)
- Cost control: not all AI providers are equal depending on the task
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SaaS development
- From idea to MVP: backend architecture, web interfaces, AI integration, APIs and deployment
- Iterative approach to validate your concept before scaling investment
- Modern stack (Next.js, Python, Docker, ...), delivered as something you can grow
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Machine learning
- Model trained specifically on your data to outperform generic solutions
- Data preparation, training, evaluation and deployment handled end-to-end
- Use cases: classification, extraction, enrichment, semantic search
- Ideal for companies that already have historical data to leverage
MLClassificationPrédictionData Science
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Frequently asked questions
- What is the difference between a RAG chatbot and ChatGPT? ▾
- A RAG chatbot answers exclusively from your internal documents. Unlike ChatGPT, it doesn't generate out-of-context responses and every answer is traceable to its source. The result: no hallucinations on your business data.
- Can I use these services while staying GDPR-compliant? ▾
- Yes. All integrations can be deployed with European hosting or on-premise, with no data transfer to US servers. I primarily work with Mistral AI and OVHcloud for use cases requiring strict GDPR compliance.
- How long does a typical AI project take to deliver? ▾
- For a RAG chatbot or targeted automation, expect 2 to 4 weeks depending on complexity and source data quality. A data pipeline or SaaS MVP typically takes 4 to 8 weeks. A precise scoping call is done before any estimate.
- I have limited internal data. Can I still benefit from an LLM? ▾
- Absolutely. Modern LLMs work very well with a few dozen well-structured documents. The data engineering phase can also help consolidate and enrich existing data, even when fragmented, before putting it to use.
- How does a typical engagement work? ▾
- A scoping call (30 min) to understand your needs, a technical proposal and quote within 48h, iterative development with weekly check-ins, then delivery with documentation and knowledge transfer so your team is fully autonomous.
Let's discuss your project
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