Euno Lands $23M to Power Autonomous Agents With AI-Native Context Layer

Euno, an Israeli artificial intelligence company, has announced a $23 million Series A funding round aimed at developing an AI-native "context brain" for autonomous agents. The round was led by N47, with backing from 10D (which previously led the company's seed stage), along with individual investors including Yinon Kostika of Wiz Inc., Yotam Segev of Cyera Inc., Ofir Ehrlich of Eon, and Rotem Weiss of Tavily. The company has now accumulated $29 million in total funding.

Operating under the legal name Delphi.io Inc., Euno has built an enterprise data platform that continuously monitors how organizations handle their work processes. The system observes the creation, use and governance of business data across an organization, then converts this understanding into a context layer that AI agents can leverage to complete tasks reliably.

According to co-founder and Chief Executive Sarah Levy, the primary barrier to widespread enterprise adoption of AI agents remains a fundamental trust deficit. Organizations harbor concerns about "hallucinations," the phenomenon in which AI systems produce false or misleading outputs, and fear that autonomous agents might malfunction or act unpredictably. These anxieties have left many agentic AI initiatives stalled in early testing phases.

Levy identified the core problem: business data is typically structured for human consumption rather than machine processing. When AI systems attempt to access and act on business information, they lack the contextual understanding necessary to interpret what the data signifies or how it relates to other datasets. Some enterprises have attempted to solve this by generating extensive documentation—sometimes thousands of pages—but this approach demands significant ongoing effort to maintain accuracy.

Euno's solution replaces manually maintained documentation with what the company describes as a live, AI-native context brain. Through research into knowledge capture methods, Levy explained that the company discovered much of the institutional context governing data use and trustworthiness can be automatically derived rather than manually documented. The platform analyzes operational signals across continuously evolving metadata graphs to automatically extract critical institutional knowledge, then refines it for AI agent comprehension. The system also incorporates organizational governance policies to ensure each agent accesses only the context and data necessary for its assigned function.

Euno claims its approach can compress the timeline for preparing context layers from as long as a year down to several weeks. Levy stated, "The long-term AI moat for enterprises will increasingly come from the accumulated record of how work gets done. 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."

The platform has already supported large organizations including Zayo Group Holdings Inc. and AlphaSense Inc. in scaling their autonomous agent deployments. The company currently employs approximately 30 people and intends to expand to roughly double that size by year-end, with new hires focused on research, sales and marketing functions.

Moshe Zilberstein, a partner at N47, emphasized the market opportunity: "For an AI agent to move the needle, it must act on current and trusted business data. That demands precise, scalable and contextual infrastructure. Demand for this is accelerating fast, and Euno is the only company building it AI-native, instead of retrofitting a solution meant for people."