Euno Raises $23M Series A to Build a Context Layer for Enterprise AI Agents

Euno has raised $23 million in Series A funding to expand its enterprise AI context platform, as companies increasingly confront a problem that better foundation models alone cannot solve: giving AI agents enough organizational knowledge to reliably work with complex business data.

The round was led by N47, with participation from existing investor 10D and a group of technology founders and executives that includes Wiz co-founder Yinon Kostika, Cyera co-founder and CEO Yotam Segev, Eon co-founder and CEO Ofir Ehrlich, Tavily founder Rotem Weiss, and former Tableau CEO and president Mark Nelson. The financing brings Euno’s total funding to $29 million.

Founded in 2023 by Sarah Levy and Eyal Firstenberg, Euno plans to use the new capital primarily to expand sales and marketing while increasing investment in AI research. The company, which operates across the U.S. and Israel and employs nearly 30 people, expects to roughly double its workforce by the end of the year.

The Enterprise AI Bottleneck Is Shifting Toward Context

The rapid improvement of large language models has made it increasingly feasible to deploy AI agents capable of querying databases, generating analytics, writing code, and automating business processes. But connecting an agent to enterprise systems introduces another challenge: knowing which information is authoritative.

A large organization may have thousands of tables, dashboards, metrics, data models, and competing definitions for the same business concept. An AI agent asked to calculate revenue, for example, needs more than access to a database. It needs to know which revenue metric is approved, which tables are current, how the metric is calculated, where its inputs originated, and whether the requesting user is permitted to access the underlying information.

Euno is attempting to turn that institutional knowledge into machine-readable infrastructure.

Its platform continuously constructs a live context graph incorporating information such as data lineage, usage, ownership, business logic, health signals, and governance status. Rather than presenting every available asset to an agent, the system can deliver context relevant to the specific user, task, and decision being made.

The distinction could become increasingly important as companies move from AI systems that answer questions toward autonomous agents capable of taking actions across multiple enterprise systems.

“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,” said Euno co-founder and CEO Sarah Levy.

Turning Metadata Into Infrastructure for AI Agents

Euno’s technology builds on metadata, the information describing where data comes from, how it is transformed, who uses it, and how different assets relate to one another.

The platform maps column-level lineage across warehouses, analytics systems, dashboards, and metrics, then combines that information with usage and governance signals. Its automated labeling system can dynamically classify assets as properties such as “AI-ready,” “PII-free,” “healthy,” or “certified” based on rules established by the organization. Those classifications update as the underlying data environment changes.

Euno also provides workflows that can respond when those conditions change. Organizations can, for example, identify uncertified dashboards that suddenly become heavily used, track the propagation of personally identifiable information (PII), flag undocumented models, or surface metrics that no longer meet internal quality standards.

The company has built its own Euno Query Language (EQL) for navigating relationships within the graph. Euno says its graph-based architecture allows agents to retrieve interconnected metadata through fewer queries, potentially reducing the amount of contextual information that needs to be passed into a model.

Importantly, the platform is designed to sit alongside existing enterprise data infrastructure rather than replace it. Euno supports integrations spanning data warehouses and platforms such as Snowflake and Databricks; transformation tools including dbt; and business intelligence platforms including Tableau, Looker, Power BI, ThoughtSpot, Sigma, and Omni.

Bringing Context Directly Into AI Systems

Another component of Euno’s strategy is making this organizational context directly accessible to third-party AI agents.

The company operates a Model Context Protocol (MCP) server through which tools such as Claude, Cursor, Visual Studio Code Copilot, and other compatible systems can query Euno’s enterprise context. Agents can use those tools to find resources, inspect upstream lineage, perform impact analysis, search documentation, or generate and execute EQL queries.

Euno has also documented integrations with systems including Snowflake Intelligence, illustrating how its context layer could effectively become an intermediary between general-purpose AI models and an enterprise’s underlying data estate.

That positioning reflects a broader shift taking place in enterprise AI. As access to capable models becomes increasingly commoditized, differentiation may move toward the proprietary information surrounding those models: organizational processes, permissions, relationships, historical decisions, definitions, and feedback from previous agent activity.

Context Could Become Part of the Enterprise AI Moat

Euno argues that this accumulated context can become more valuable over time.

When an agent performs a task, the system can capture information about how context was used and feed those signals back into the enterprise context layer. In theory, that creates a feedback loop where an organization progressively builds a richer machine-readable representation of how its business operates.

This also addresses one of the central challenges facing agentic AI deployments: governance. Giving an autonomous agent access to enterprise systems without understanding what information it can retrieve or how it should interpret that information introduces operational and security risks. Euno’s approach combines contextual retrieval with role-aware governance so that an agent receives information appropriate for its task and permissions rather than unrestricted access to the entire environment.

The company says its platform reads enterprise metadata rather than the underlying business data itself, an architectural distinction intended to reduce data exposure while still allowing the system to map relationships and provide agents with operational context.

Euno is already working with enterprise customers including AlphaSense and Zayo Group, while its website also documents deployments involving companies such as Bolt.

For the wider AI industry, the significance of Euno’s Series A extends beyond another enterprise infrastructure funding round. Foundation models are rapidly becoming more capable and increasingly interchangeable for many workloads. If that trend continues, one of the harder problems will be giving those models a reliable understanding of the organizations in which they operate.

That could make context infrastructure, rather than the underlying model itself, one of the more consequential layers of the emerging enterprise AI stack.