Wiz, Cyera and Eon founders back Israeli AI startup Euno in $23 million round
Euno is building a platform designed to give AI agents the organizational knowledge they need to work reliably inside large companies. The Series A, led by N47, will fund expansion in sales, marketing and AI research as the startup targets Fortune 500 customers.
Israeli startup Euno has raised $23 million in a Series A round led by N47, with existing investor 10D and a group of prominent technology founders participating, including Yinon Costica, co-founder of Wiz; Yotam Segev, co-founder and CEO of Cyera; Ofir Ehrlich, co-founder and CEO of Eon; Rotem Weiss, founder of Tavily; and Mark Nelson, former CEO and president of Tableau.
The funding brings Euno’s total raised to $29 million as it seeks to solve a growing problem for enterprises adopting AI, giving agents enough organizational context to know what information to trust, how to use it and what they are allowed to do with it.
Euno develops a platform designed to help AI agents understand an organization’s internal knowledge and navigate its data. The company describes its technology as an AI-native “context brain” that continuously learns how organizations build, use and govern their data, allowing companies to give agents the specific context they need to act reliably.
Nearly 30 employees work for the company in Israel and the U.S., including a research group focused on organizational context. Euno expects to double its workforce by the end of the year, with most of the new funding going toward sales, marketing and AI research.
The company was founded in 2023 by Sarah Levy (CEO) and Eyal Firstenberg (CTO). Both are graduates of Talpiot, the Israeli military’s elite technology program. Levy holds a master’s degree in physics from the Weizmann Institute of Science and was previously CTO of Sight Diagnostics, which developed AI-powered blood testing technology. She also held senior technology positions in the IDF’s Unit 81.
Firstenberg previously served as a section head in Unit 8200 and later became VP of R&D at LightCyber, which was acquired by Palo Alto Networks.
Euno began developing its platform as companies were only starting to experiment with AI inside their organizations. The company later shifted its focus toward AI agents and the problem of giving them access to the organizational knowledge that human employees often absorb naturally.
“Every organization wants to be AI. Today the agents are very intelligent, but when they are in the organization they don’t know what data to trust, what data is relevant and what data to use,” Levy told Calcalist. “They are stupid when it comes to the organization’s knowledge.”
Euno argues that enterprise data infrastructure was largely designed for humans. Companies have traditionally relied on data catalogs, manually maintained semantic layers and governance rules to help employees understand and use information. AI agents, however, can move across multiple systems and domains, consume and create information at a much faster pace and increasingly operate with a degree of autonomy.
The company’s platform is designed to replace much of that human-maintained context with a continuously updated system that understands how data flows through an organization, what it means and how it is used.
At the core of Euno’s approach is research into whether institutional knowledge can be inferred from operational signals rather than manually documented. The company says it has found that much of the information needed to determine which data should be used and trusted is encoded in patterns across continuously changing metadata graphs.
Euno analyzes those patterns to reconstruct organizational knowledge and then uses feedback from how AI agents interact with the context to refine its understanding. The goal is to allow companies to move AI initiatives from pilot to production within weeks rather than spending months or even a year making their data “AI-ready.”
The system also incorporates governance into the context layer. It is designed to determine what information an individual agent should be able to access based on its task and role, while enforcing organizational boundaries as the agent operates.
“We build a brain that connects to the models and explains to them what they are allowed to do and how. It also tells them where they are not allowed to touch and what information is confidential,” Levy said. “We use policies to enable business.”
The company is already working with Fortune 500 organizations across industries, including market intelligence platform AlphaSense and telecommunications company Zayo Group.
“Models keep getting better and cheaper, but the long-term AI moat for enterprises will increasingly come from the accumulated record of how work gets done,” Levy said. “Competitors may have access to the same frontier models, but they cannot easily replicate the proprietary context and experience an enterprise has accumulated through its own operations.”
