I integrate artificial intelligence into your applications and workflows to automate, accelerate and create value.
Generative AI is no longer an R&D topic. It's a production tool transforming real business processes. Automatic support ticket sorting, personalized content generation, assistants answering your customers' questions 24/7.
What changed since 2023: models are reliable, APIs are mature, costs are manageable. Integrating an LLM into an application costs a few thousand euros, not hundreds of thousands. And ROI is often visible in weeks, not months.
My role isn't to sell you AI. It's to determine where AI creates concrete value in your product, and integrate it properly.
Cognitive work automation. Tasks requiring text comprehension, judgment, classification — that's no longer humans-only. An LLM sorts 1,000 support tickets in 2 minutes with consistency that 5 operators can't match.
Personalization at scale. A personalized email for each prospect, a product description adapted to each segment, a tailored response to every customer question. What was impossible manually becomes trivial.
Information access. Your teams spend hours searching through documentation, emails, Slack. A RAG system gives them the answer in seconds, with the source.
New products. Features that didn't exist 2 years ago become possible: automatic contract analysis, report generation from raw data, sales assistants that know the entire catalog.
I don't plug an OpenAI API call and call it AI. Every integration follows a production-grade architecture.
Vercel AI SDK as foundation. Real-time response streaming, tool calling for actions, multi-provider support (Claude, GPT, custom models). The SDK handles protocol complexity, I focus on business logic.
Structured prompt engineering. Versioned system prompts, few-shot examples, dynamic templates. Prompts aren't hardcoded strings, they're maintained and tested modules.
Systematic guardrails. Rate limiting, content filtering, output validation, cost monitoring. An LLM in production without guardrails is a ticking bomb.
Beyond raw LLM integration, I design complete systems: RAG agents connected to your data, multi-action agents with tool calling, and intelligent automation workflows via n8n.
Contextual assistants connected to your business data. Not a generic ChatGPT, an agent that knows your product, processes and customers.
AI queries your document base to answer precisely. Internal docs, dynamic FAQ, augmented technical support.
Ticket sorting, lead scoring, document categorization, data extraction. AI processes in seconds what takes hours manually.
Product descriptions, personalized emails, automatic reports, translations. Content generated from your data, in your tone, following your rules.
Agents that chain actions: search, analyze, decide, execute. From simple prompts to multi-step orchestration with tools.
Insight extraction from texts, emails, reviews, documents. Anomaly detection, sentiment analysis, automatic classification.
Vercel AI SDK
Streaming, tool calling, multi-providers, React/Vue.
Claude API (Anthropic)
Complex reasoning, long context, vision, tools.
OpenAI API
GPT-4, embeddings, fine-tuning, assistants.
LangChain / LlamaIndex
RAG, prompt chains, agent orchestration.
Pinecone / pgvector
Vector database for semantic search.
n8n
Visual AI workflows, no-code, self-hosted.
Founders and business owners who had a project, a need, a deadline. Here's what they have to say.
"Disponibilité, réactivité et implication. Valentin est professionnel et pédagogue."
Alban B.
CEO Belho Xper
"Il allie une expertise technique pointue à une solide vision business."
Charley A.
Co-fondateur Avnear
"La communication a toujours été fluide et les délais respectés, ce qui est rare et très appréciable."
Chihab A.
CEO E-commerce
"Valentin a su être à l'écoute de mes attentes et de mes besoins. Les résultats ont été plus que satisfaisants."
Sandrine V.
Gérante Sandrin's Nail
"Une entreprise qui sait s'adapter parfaitement au besoin client."
Stanislas M.
Commercial
"Depuis la mise en ligne, nous avons remarqué une nette augmentation des appels et des demandes de renseignements."
Christophe R.
PDG Ravi Groupe
A classic chatbot follows predefined decision trees. An AI chatbot understands natural language, reasons on your data, and produces personalized responses. It adapts to questions it's never seen, where a classic chatbot fails.
RAG (Retrieval-Augmented Generation) splits your documents into segments, transforms them into vectors, and stores them in a vector database. When a user asks a question, the system retrieves relevant segments and injects them into the LLM context. The model answers based on your data, not its general training.
Claude (Anthropic) for complex reasoning, long document analysis and code. GPT-4 (OpenAI) for versatile generation. Open-source models (Mistral, Llama) when data must not leave your infrastructure. I recommend the model suited to the use case, not a single provider.
Technical integration (API, interface, pipeline) costs between €3,000 and €15,000 depending on complexity. Production API costs are often under €50/month for most SMB use cases. ROI is measurable within the first weeks.
Yes. Claude and GPT don't reuse your data for training via APIs. For ultra-sensitive cases, I deploy open-source models on your infrastructure. Data never leaves your environment.
Yes, LLMs can hallucinate. That's why I design systems with guardrails: source verification (RAG), confidence limits, human fallback, response monitoring. AI is reliable when properly framed.
Complementary technologies
Free first call, no commitment.
