GuideRAG reranking with Jev: how we use it in production
RAG reranking with Jev, TypeSafe's AI model: how we use it in production, the scores it gives, the chunks it keeps, response time and cost.
7 min
Guide
GuideHow AI works: the 10 concepts to understand before you sign
Neural networks, tokens, training, fine-tuning, hallucination, LLMOps: the ten concepts that explain where your AI budget goes and where your dependency begins.
Digital SovereigntyBest French AI for enterprises in 2026: 14 vendors put to the sovereignty test
Fourteen French enterprise AI vendors checked against public sources: who owns the capital, who hosts your data, who runs inference.
GuideAI Inference: the 7 ways to run your models in the enterprise
Enterprise AI inference: 7 approaches compared, from buying GPUs to a turnkey provider. Real costs, sovereignty, latency and how to choose.
11 min
GuideEnterprise AI SLA: one AI provider for your agents is not enough
Why one provider is not enough for an enterprise AI SLA. Compare commitments, multi-provider gateways and fallbacks for RAG and AI assistants.
3 min
GuideEnterprise RAG: 5 mistakes that show up after the POC
An enterprise RAG is relatively easy to prototype. Making it a useful, stable product is a much bigger challenge. Here are the five most common production pitfalls—and how to get ahead of them.
AILLM On-Premises for Your Enterprise: What Can You Do with €60k?
Agents, RAG, internal assistants: how to choose between Make and Buy for your AI project. Figures, use cases, and cost analysis to help you decide.
4 min
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TutorialHow RAG Transforms Your AI into an Expert on Your Business
RAG and LLM: definition, how it works, choosing the model. The guide to connecting an LLM to your internal documents and enterprise data.
ConceptsOpen-Source in AI: The Big Misunderstanding
Open-source vs open-weight: why LLMs can't be truly open-source like Linux, and what you actually get when a model releases its weights.