Collate launched a live session on September 3rd where a data engineer defines context once, a business user asks a question in plain language, gets a trusted SQL answer, and turns it into a governed dashboard that runs on a schedule.

Enterprise AI is only as good as the context behind it. Frontier models that score 90% on academic benchmarks drop to roughly 11% accuracy on real enterprise data, because the meaning of that data lives in scattered tools, stale docs, and people's heads. Collate is the AI for Data platform, built on OpenMetadata, the open context layer for AI. We ground people and AI agents in shared semantic context: what your data means, where it came from, whether it can be trusted, and what your organization has already learned about it. That foundation powers two things: • Agents that automate the mundane in data management — documentation, classification, quality testing, and governance at scale. • AI analytics business users can trust — ask a question in plain language, get answers grounded in your own business meaning, human knowledge, definitions, lineage, and policies. The result is measurable: grounding AI in governed semantic context lifts answer accuracy from ~11% to 76.5% on real enterprise data (Spider 2.0) and greatly reduced AI token consumption. The world's most advanced AI teams take this approach. OpenAI built its internal data agent on OpenMetadata, serving 3,500+ employees across 600+ PB of data a day. Wix centralized 25,000+ data assets and 130,000+ lineage connections, cutting engineering toil by 675 hours a month while AI agents answer data questions in about a minute. Yelp runs 100K+ assets in production, tuning agent payloads ~80% smaller. Teams at Scout24, Unity, Rakuten, and Ambry Genetics build on the same foundation. Built in the open: OpenMetadata is the largest open-source metadata community, with 130+ connectors and open standards that keep your context portable and LLM-ready. See it in action: getcollate.io/book-demo