Euno productizes Anthropic's framework for trusted enterprise AI
Anthropic published what might be the clearest blueprint yet for enterprise AI you can trust.
The team behind Claude recently reported that Claude now handles around 95% of its internal analytics requests, letting employees query business data independently instead of relying on data teams.
Anthropic didn’t simply connect Claude to a data platform and hope for the best. They invested in governed data foundations, lineage, business context, skills that encode the relevant level of exposure, and validation systems that keep answers trusted.
What Anthropic effectively described is a framework for making AI work on top of core data. But building that framework required the full weight of Anthropic’s data org, with who knows how many hours of manual cleanup and curation. Most enterprises don't have the time or resources to replicate that.
That’s exactly the problem we built Euno to solve.
Euno productized Anthropic's framework for the enterprise, so you can deploy AI across your data ecosystem today without having to build the entire foundation from scratch the way Anthropic did. Anthropic had to build the framework. With Euno, you can use it.
When an enterprise connects Claude, Snowflake Intelligence, Databricks Genie, Glean, ChatGPT, Gemini, Cursor, Microsoft Copilot or any other agent to its data ecosystem, the agent cannot be left to wander across thousands of tables, dashboards, metrics, and conflicting definitions. Ask “what was the total revenue for the previous fiscal year?" and the agent might find dozens of revenue-related assets, miss the real business definition of fiscal year, and generate its own guess. That is how every business user ends up with a different number.
Euno gives agents the live enterprise context they need at runtime: column-level lineage, usage, ownership, glossary, semantics, access, quality signals, and active governance tags like “AI-certified” or “relies on PII.” We can also turn that context into persona-specific, similar to Claude Skills. So a Customer Success Manager, Finance user, Security analyst, or Data Engineer querying AI is guided to the right assets and the right level of detail without being exposed to irrelevant or sensitive data.
Anthropic’s blog post is a powerful reminder that the model capabilities are good enough. It’s a context architecture that makes AI work on top of enterprise data. That’s how we build trust in AI for Euno customers, so everybody from business teams to data and security teams could act with agents on data. Just like Anthropic is showing is possible.
