Why we invested in Euno: The missing context for enterprise AI
We are excited to announce that N47 has led Euno's $23 million Series A. Euno is building an AI context platform that gives agents the organizational knowledge they need to reliably use enterprise data. The round also brings together existing investors, including 10D, and leading technology founders and executives from Wiz, Cyera, Eon, Tavily, and Tableau.
At N47, we believe AI will become embedded across virtually every function of the enterprise. Agents will analyze, decide, coordinate, and increasingly act across all systems that businesses depend on.
However, artificial intelligence alone will not enable that future.
An agent operating inside an enterprise needs to do more than access data. It needs to navigate vast amounts of structured data across thousands of tables, reports, and dashboards, and understand what the numbers mean, which definitions to trust, and what matters for the task at hand.
The missing element for making sense of that complexity is context, and it is becoming a foundational building block of the AI-native enterprise. Euno is building the platform that gives agents that context.
The missing piece in the AI stack
Models are becoming dramatically more capable, and MCP is making enterprise systems increasingly accessible to agents. But connectivity solves access, not understanding.
The structured data that powers an enterprise is typically messy by design. Behind even simple data-driven business decisions sit hundreds of tables, conflicting definitions, evolving business logic, and years of institutional knowledge. Humans navigate this complexity using accumulated judgment: they decide which dashboard is reliable, when a definition has changed, and when a data point simply doesn't smell right.
This "intuitive" organizational context is simply missing from the AI stack, which is why a new cohort of AI-native context platforms is emerging to give machines the organizational knowledge they need to operate reliably, at scale. Euno is positioned at the center of this emerging category.
Context becomes more valuable as AI gets better
We were particularly drawn to Euno's AI-first approach: enterprise context cannot scale if it relies on people to document it manually.
Euno stitches together the operational signals an institution already produces, including lineage, usage, ownership, transformation logic, and other relationships, into a context graph. Its research focuses on deriving institutional knowledge from patterns within that graph and delivering the right knowledge to an agent when it needs it.
The goal isn't just to make metadata easy to search or up-to-date. It's to derive the specific context agents need to reliably use enterprise data, then to continuously refine that context as agents interact with it and the organization evolves.
This creates a powerful compounding advantage. Models will get better, cheaper, and increasingly commoditized, while proprietary context, the accumulated knowledge of how an enterprise operates, becomes more valuable over time.
Agents can't make a meaningful business impact without understanding the business. That starts with its metrics, but metrics without context are, at best, lucky guesses. Enterprises that provide that context can turn increasingly powerful AI into meaningful business outcomes.
Sustained trust: The new battlefield
Agents will become embedded across virtually every major enterprise workflow, working across functions and taking on increasingly complex tasks across data and systems.
Agents will always attempt to find data on their own, searching documents and tables, and looking at stale dashboards or old wikis with varying degrees of success. Enterprises need a precise, governed context for each task to ensure consistent, deterministic results.
We have had many conversations with enterprises inside and outside of the N47 ecosystem. What we’ve learned is that sustained trust is a critical infrastructure challenge that determines whether AI agents become integral to daily operations or remain experimental.
With customers such as Alphasense, Zayo, Cyera, and Bolt, Euno is well-positioned to make that future possible.
An exceptional team for a complex challenge
Creating a context platform that can understand relationships across enterprise data, derive institutional knowledge, and continually improve is primarily a research and engineering challenge. That's a big reason why we decided to invest.
Sarah Levy and Eyal Firstenberg met as 18-year-olds in Talpiot, the Israeli military's elite technology program. Sarah went on to serve as CTO of Sight Diagnostics and lead a top IDF cybersecurity department. Eyal was a section head in Unit 8200 before becoming VP of R&D at LightCyber, which was acquired by Palo Alto Networks.
What stood out to us was not simply their technical pedigree but the combination of deep data expertise, exceptional engineering ability, and a research mindset suited to a problem for which there is still no established playbook.
We've looked closely at this emerging space and have seen a growing cohort of companies approaching enterprise context from different directions. The increasing activity in this space reinforces the scale and importance of the opportunity.
Sarah, Eyal, and the Euno team have the depth and ambition to build the category-leading company.
Conclusion
The next phase of enterprise AI will not be defined solely by access to the best model. The harder question is what those models know about the enterprises in which they operate.
We believe the answer will come from a foundational context platform that connects structured enterprise data with the agents putting it to work. As AI becomes embedded across business operations, context will be what makes those agents trustworthy, consistent, and useful, creating a compounding advantage for enterprises that get it right.
This is the future Euno is building toward, and it's why we're excited to lead its Series A.
Find out more at euno.ai.
