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.
For internal teams
Every employee asks a question and finds useful information across your documents, tools, and knowledge bases. ATG connects your sources, secures access, and adapts the assistant to your business specifics.
Google Drive, SharePoint, Confluence, Notion, websites, files, FTP, business IT systems, SQL databases, and business sources: we connect your knowledge without starting from scratch.
European-law hosting providers, controlled permissions, cited sources, and answer traceability so teams can trust the system.
Your vocabulary, processes, internal rules, and use cases are taken into account so answers are genuinely useful.
Resources
Selected articles and videos to better understand this solution and see how it works in practice.
GuideWhy knowledge management initiatives fail, what AI really changes about managing what a company knows, and what it will never change.
GuideInternal company chatbot: what it is really for, how it differs from a website chatbot, the classic pitfalls and the conditions for success.
GuideAn 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.
AIAgents, RAG, internal assistants: how to choose between Make and Buy for your AI project. Figures, use cases, and cost analysis to help you decide.
TutorialRAG and LLM: definition, how it works, choosing the model. The guide to connecting an LLM to your internal documents and enterprise data.
Digital SovereigntyHow dependence on U.S. infrastructure exposes Europe to growing strategic risks - and what to do to protect yourself.
Enterprise RAG (Retrieval-Augmented Generation) connects an AI model to your internal documents and tools. Answers draw on your sources, with citations and access control, not only on a public chatbot’s generic training.
The expensive foundations (connectors, ingestion, retrieval, citations, permissions, evaluation, monitoring) already exist in ATG. You go to production faster, often with about 80% lower TCO than an internal build.
For every employee who wastes time searching Drive, SharePoint, Confluence, Notion, or email. The internal chatbot answers from your knowledge, with inherited permissions and cited sources.
Yes. On-premise / on-prem RAG does not always mean hosting the LLM yourself on GPUs: separate data and retrieval, the platform (connectors, permissions, citations), and inference. Depending on your constraints, ATG deploys with European hosting or on-premise: same data control and traceability, without rebuilding the full stack. For the cost of a fully local LLM, see: https://www.askthisguy.com/en/blog/llm-local-enterprise-sovereignty-illusion/
An assistant that answers from your sources, hosted under your control (Europe / on-prem), with citations and access rights. It is not a consumer chatbot trained on the open web.
Show us your sources and constraints. We will quickly map how to deploy a sovereign, operational RAG assistant.