Featured articles
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.
GuideData analysis tools: which family for which need in 2026?
Excel, Power BI, Python, conversational AI: a comparison of data analysis tool families to help you pick the right one for your needs.
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.
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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.
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GuideEnterprise AI agents: use cases and real costs
Enterprise AI agents: the definition, what an agent can really do, the method that gets it into production, and what it actually costs.
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.
GuideManagement control: how to combine Excel, Power BI dashboards and AI?
Excel, Power BI dashboards or conversational AI: which tool for your management control, financial reporting and data analysis? A guide to choosing.
GuideHow to generate qualified leads with a company website?
Starting a conversation quickly and making a strong first impression are essential to building a lasting relationship.
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.
GuideGetting AI automation right: use as little AI as possible
What is AI automation? Benefits, examples, tasks worth automating, and key steps to do it right while using as little AI as possible in production.
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.
GuideHow MCP accelerates agentic AI
Discover how MCP enables AI agents to dynamically use tools without custom integrations. This article clarifies key concepts including function calling, skills, and MCP apps.
ConceptsHow a Failed Demo Forced Us to Rethink Our Embedding Strategy
ATG’s lessons learned from our embedding solutions in production
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.
Digital SovereigntyWhat if the United States Turned Off Your Digital Tap?
How dependence on U.S. infrastructure exposes Europe to growing strategic risks - and what to do to protect yourself.