GuideMistral OCR: cut your bill 10x with open weights
Mistral OCR costs $4 per 1,000 pages. An open-weight OCR on a European GPU drops to $0.15 and stays sovereign. Benchmark, break-even point and pitfalls.
10 min
Guide
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
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.
ConceptsHybrid search: the 6 ways to find a piece of information
Semantic, keyword, business tools, database queries, document catalog, tailored processing: the six paths an agent chains to find the right answer.
GuideInternal company chatbot: how to scope the project right
Internal company chatbot: what it is really for, how it differs from a website chatbot, the classic pitfalls and the conditions for success.
GuideKnowledge management: why your initiatives fail, and what AI changes
Why knowledge management initiatives fail, what AI really changes about managing what a company knows, and what it will never change.
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
GuideContextual AI for your company: 6 concrete productivity gains
Discover 6 concrete productivity gains that contextual AI with RAG can bring to a company by structuring and sharing its knowledge more effectively.
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.
ProductYour databases are a gold mine, AI gives you the shovels
Juggling enterprise systems, getting a simple answer often takes twenty minutes (or a Teams ping to the “person who knows”). Your real data lives in structured databases: it’s often reliable, up to date… but also inaccessible. Most companies seem to overlook it, yet AI can query it in plain language and cross-reference documents and databases in seconds.
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
DemoBuild your agents in a few clicks
Deploy an enterprise chatbot in 3 minutes.
ConceptsHow a Failed Demo Forced Us to Rethink Our Embedding Strategy
ATG’s lessons learned from our embedding solutions in production
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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.