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.
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.
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.
Demo
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.