Euno
Euno is a metadata management platform founded in 2025, emerging from the growing need to provide AI systems with contextual understanding of enterprise data.
Profile
Provides AI systems with real-time context about enterprise data to prevent errors and inconsistencies.
Euno is a metadata management platform founded in 2025, emerging from the growing need to provide AI systems with contextual understanding of enterprise data. The company, led by CEO Sarah Levy, focuses on solving the 'context gap' that causes AI agents to fail when interacting with complex business data environments. Euno's approach centers on automating governance workflows that traditionally required manual upkeep, such as certifying dashboards, resolving metric conflicts, and identifying stale assets.
Unlike legacy catalog tools, Euno reconstructs lineage, usage patterns, and business logic into a live context graph that AI systems can query in real time. The platform integrates with existing data tools like dbt, Looker, and Tableau while adding governance layers. As of late 2025, Euno appears to be in early growth phase, with customer testimonials from analytics leaders but no disclosed funding rounds or revenue figures. The company's timing aligns with industry recognition that 95% of enterprise AI pilots fail due to context limitations, positioning its technology as critical infrastructure for AI adoption.
Who buys this
- Data teams managing AI-driven analytics
- Enterprises scaling AI agent deployments
- Analytics engineering leaders
- Data governance professionals
- BI teams combating metric inconsistencies
Strengths and what to watch
Strengths
- Automates traditionally manual governance workflows like dashboard certification
- Reconstructs live context graphs from fragmented data systems
- Designed specifically for AI agent requirements rather than human users
Watch for
- Unproven at true enterprise scale - most references are to early adopters
- Competing against established players like Collibra with deeper pockets
- Risk of being a 'nice-to-have' layer if AI adoption slows
Recent moves
Key Information
- Founded
- 2025
Frequently Asked Questions
What does Euno do for enterprise AI systems?
Euno provides real-time context about enterprise data to prevent AI errors. Their platform automates governance workflows like dashboard certification and reconstructs data lineage into live context graphs. This helps AI systems understand complex business data environments, addressing the 'context gap' that causes many AI failures. (45 words)
How does Euno's approach differ from traditional metadata tools?
Unlike legacy catalog tools, Euno dynamically reconstructs lineage, usage patterns, and business logic into queryable context graphs. Designed specifically for AI needs rather than human users, it integrates with tools like dbt and Looker while adding real-time governance layers for machine consumption. (44 words)
What problems does Euno solve for data teams?
Euno automates manual governance tasks like certifying dashboards and resolving metric conflicts. It helps data teams maintain consistency across AI-driven analytics by providing systems with live context about data relationships, usage, and business meaning - preventing errors from context gaps. (46 words)
Who are Euno's typical customers?
Euno serves analytics leaders, data governance professionals, and BI teams in enterprises scaling AI deployments. Their platform specifically targets organizations where AI agents interact with complex data environments and need real-time context to operate accurately without human oversight. (45 words)
How does Euno prevent AI errors in data analysis?
By maintaining live context graphs that show data lineage, usage patterns, and business logic. This gives AI systems the metadata needed to understand relationships and avoid inconsistencies when analyzing enterprise data, addressing the root cause of most AI failures in business contexts. (47 words)
What tools does Euno integrate with?
Euno connects with popular data stack components including dbt for transformations, Looker for BI, and Tableau for visualization. The platform layers context-aware governance on top of these tools while reconstructing their metadata into unified graphs for AI consumption. (45 words)