https://stream.andersonr.net/videos/watch/2d695fe6-bfa2-4b35-8315-d7cfaaf27438 When AI agents guess at the database schema, the SQL is wrong and nobody trusts the output. Epoch8 built a metadata layer in Grist so Claude and other AI agents write accurate queries against real client databases. In this webinar, Olga Tatarinova, co-founder of Epoch8, walks through the production setup her team uses across client projects in legal, e-commerce, manufacturing, and automotive. She shows the anonymized data model on screen, explains the minimal modeling methodology her team uses to describe each client's domain, and demonstrates how Claude reads the structured metadata to write accurate SQL against a client database. If you're building RAG systems that have to query real databases, wiring up AI agents against structured client data, or watching your AI agent get schema questions wrong more often than you'd like, this walkthrough is for you. What Olga covers Why LLMs fail when you dump documents into them and expect accurate answers, The requirements Epoch8 evaluated tools against: open source, typed data, API-driven, Python-friendly, role-based access, Why databases, spreadsheets, and CMS tools all fell short of what Epoch8 needed, The three concepts of minimal modeling: anchors, attributes, and links, A live walkthrough of a legal domain data model built in Grist, How Epoch8's AI platform feeds the Grist metadata to Claude as context, Why Epoch8 uses Grist for expert editing and Memgraph as the production backend for large datasets, Practical advice on how to start documenting your own client data., Chapters: 0:00 Introduction: Olga Tatarinova and Epoch8 1:00 What Epoch8 does: computer vision and AI-driven chatbots 2:30 Why LLMs fail on business documents 3:45 Epoch8's requirements: open source, typed data, API-driven, role-based access 5:30 Why databases, spreadsheets, and CMS did not fit 7:00 Live demo: a legal corpus data model in Grist 9:00 Minimal modeling: anchors, attributes, and links 12:30 How Claude reads the metadata to write accurate SQL 17:30 Where the pattern generalizes: an automotive data model 21:00 Q&A begins 22:00 The Grist and Memgraph architecture for production 25:00 On PDFs, chunking, and structured extraction 27:00 Advice for practitioners starting their own data model Resources mentioned: Minimal modeling by Alexei Mahotkin (Substack): https://minimalmodeling.substack.com/, Alexei's book on minimal modeling: https://databasedesignbook.com/, Epoch8 case study on this setup: https://www.getgrist.com/case-studies/epoch8-grist-rag-solution/, Grist template Olga shared during the demo: https://docs.getgrist.com/7rnSmjBLbBRQ/Legal-RAG?utm_id=share-doc, Epoch8: https://epoch8.co, Grist: https://www.getgrist.com, About the speakers Olga Tatarinova is co-founder of Epoch8, an agency that helps startups and enterprises ship machine learning, AI, and computer vision systems into production. Epoch8's work spans intelligent chatbots, RAG systems, and computer vision for clients in retail, e-commerce, automotive, manufacturing, and legal. Anais Concepcion is CEO of Grist Labs. Grist is an open source spreadsheet-database that gives users the familiar spreadsheet interface backed by a real relational database, with Python formulas, row-level access controls, and self-hosting. About Grist Grist is the open source spreadsheet-database that turns critical spreadsheet workflows into secure, scalable applications. Self-hosted or managed. Trusted by teams at the European Space Agency, Toyota, and thousands of other organizations. Learn more at https://www.getgrist.com Tue, 28 Jul 2026 12:36:11 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://stream.andersonr.net https://stream.andersonr.net/client/assets/images/icons/icon-1500x1500.png https://stream.andersonr.net/videos/watch/2d695fe6-bfa2-4b35-8315-d7cfaaf27438 All rights reserved, unless otherwise specified in the terms specified at https://stream.andersonr.net/about and potential licenses granted by each content's rightholder.