Euno announced a $23 million Series A on September 9 for software that helps enterprise data teams give AI agents the context behind their numbers. Rather than build another general-purpose model, the company maps how business data is defined, where it comes from and which rules govern its use.
Consider an analyst asking why two revenue dashboards disagree. The difficulty may not be calculating a total, but discovering that each report uses a different definition or source table. Euno reconstructs relationships between data warehouses, models and reports, alongside ownership and usage information. That gives a person or agent a way to investigate the disagreement rather than simply choose the first plausible number.
The company's Slack Assistant makes that workflow available inside a conversation. An employee can ask what feeds a dashboard, who owns a table or how a metric is defined. Euno's documentation says the assistant uses a linked Euno identity and an assigned access profile to determine which resources and actions are available. Connecting a chat tool is therefore not meant to give every participant unrestricted access to the underlying catalog.
Euno sells to organizations running complex data environments, with analytics and governance teams responsible for keeping them usable. Its pricing scales with the data assets managed rather than employee seats, with different packages for governance and AI features. The announcement names AlphaSense and Zayo among its customers. Monitoring metadata—the descriptive information about data—and triggering tickets when it changes makes this an ongoing maintenance product, not just a one-time warehouse inventory.
N47 led the round, with existing investor 10D and several technology founders and executives participating. Both the company and N47 announced the investment, which Euno says brings total funding to $29 million. Founded in 2023 by Sarah Levy and Eyal Firstenberg, Euno plans further investment in research and its commercial organization.
The specific challenge is keeping context useful as teams change definitions and add new data. A map that was correct at setup can become misleading later. Euno is betting that continuously reconstructing those relationships will make AI work on business data more dependable; its access controls and automated labels are product mechanisms, not a guarantee that every resulting answer is correct.