Euno

**Product and the concrete problem it solves.** Euno is addressing the gap between connecting an AI agent to enterprise data and giving that agent enough business context to use the data safely. A modern company may have thousands of tables, dashboards, metrics, reports, transformation models, owners, access rules, and competing definitions of the same term. An agent that can technically query those systems can still select an obsolete dashboard, confuse exploratory and certified metrics, expose personally identifiable information, or return a confident answer based on the wrong business definition. Euno's product is an AI-native context platform, described by the company as a "context brain," that reconstructs the meaning, lineage, ownership, usage, quality, and governance of data assets. It then delivers the particular slice of context required for an agent's task and role rather than dumping a large schema into a prompt. The concrete customer outcome is more reliable analytics and agentic automation with less manual cataloging and fewer uncontrolled data paths.

**Core technology and how it works.** Euno's architecture is graph-native and metadata-first. Its platform continuously discovers enterprise data assets and relationships, reconstructs lineage and business meaning across the stack, and maintains a live graph rather than a static catalog that becomes stale after its initial documentation pass. The company's public materials describe a dedicated query language called EQL, automated discovery, Active Metadata Tags, and runtime context retrieval. In practice, that means an agent can receive the definition of an official metric, the models and source columns behind it, the asset's freshness and quality signals, the relevant owner, and the governance labels attached to sensitive data. Euno also applies persona-based access so that context is not only accurate but scoped: a finance agent, customer-success agent, security analyst, and data engineer can be directed to different assets and capabilities. The company says it integrates with systems and agent surfaces including Snowflake, Databricks, Claude, Cursor, Glean, ChatGPT, Gemini, and Microsoft Copilot. The important technical claim is not that Euno trains a frontier model; it is that it supplies a live, policy-aware context plane around models that already exist. Public sources do not disclose the full graph schema, retrieval algorithms, model mix, latency envelope, or independent accuracy benchmarks.

**Market, customers, and go-to-market.** Euno sells into enterprise data, analytics, governance, privacy, and AI-platform teams that need to move from experimentation to production without allowing every agent to roam through every data system. Its initial buyer can be a chief data officer or analytics leader responsible for definitions and lineage, a security or privacy team responsible for sensitive-data exposure, or an AI platform team responsible for deploying agents reliably. That cross-functional entry point is commercially useful because Euno can frame the same infrastructure as productivity, data quality, governance, and security rather than as a narrow catalog replacement. The company says it is working with Fortune 500 organizations and has named AlphaSense and Zayo in public ecosystem coverage; revenue and customer count remain undisclosed. Its product pages feature customer testimonials from data and analytics leaders, while the official blog documents integrations with Cyera and Omni and demonstrations over Snowflake and Databricks. The go-to-market motion appears enterprise and platform-led: land with governed context for a high-value data workflow, prove that agents use more precise and explainable data, then expand across departments, data stores, and agent surfaces. That motion carries long security review and integration cycles, but a successful deployment can become embedded in the organization's definitions and access controls.

**Traction, funding, and third-party validation.** Euno was founded in 2023 and announced a $23 million Series A in September 2026 led by N47, with existing investor 10D participating and additional backing from technology founders including Yinon Kostika of Wiz, Yotam Segev of Cyera, Ofir Ehrlich of Eon, Rotem Weiss of Tavily, and Mark Nelson, former CEO and president of Tableau. The round brings publicly reported total funding to $29 million, following a $6.25 million seed announced in 2024. The size of the round and the mix of data and security operators are meaningful ecosystem validation, but they are not substitutes for disclosed revenue or retention. Euno was named to Gartner's 2026 Coolest Vendor Innovations in Data Management report, and N47's diligence thesis independently frames the product as infrastructure for agents that must understand definitions, trust, and organizational knowledge rather than merely retrieve raw data. The company has published a steady body of product and governance material, including a Cyera integration that propagates sensitive-data classifications into business-layer assets and an Anthropic-framework analysis that explains runtime context, lineage, ownership, and access. These are credible productization signals. The missing evidence is equally important: no public ARR, customer count, renewal rate, independent benchmark, security certification, or quantified reduction in agent errors is disclosed.

**Founders and team background.** Euno's founders provide unusually direct Israeli technical and security provenance for an AI-infrastructure company. CEO Sarah Levy and CTO Eyal Firstenberg are both graduates of Talpiot, Israel's elite military technology program. Levy is described publicly as a former CTO of Sight Diagnostics and as the former head of a major IDF cybersecurity department, combining applied AI and computer-vision product experience with security operations. Firstenberg led a section in Unit 8200 and later served as VP of R&D at LightCyber, which Palo Alto Networks acquired. That background is relevant to Euno's product because the hard problem is not only indexing data; it is modeling trust boundaries, access paths, operational meaning, and the consequences of an automated system making a plausible but unauthorized decision. Euno's public company page lists a broader team spanning R&D, software architecture, AI engineering, product, solution architecture, sales, and operations, while the September funding announcement places total headcount near 30 across Israel and the United States. The company says it expects to double by year-end and has a context-focused research group. Diligence should still test whether the current team has enough enterprise security, distributed-systems, data-platform, and customer-success depth for global production deployments.

**Competitive dynamics.** Euno competes against several categories rather than one direct incumbent. Traditional data catalogs such as Collibra, Alation, and Atlan provide discovery, lineage, governance, and business glossaries, but their systems were largely designed for human data users and can require extensive manual curation. Cloud and platform vendors such as Microsoft Purview, Databricks, and Snowflake can bundle catalog, governance, and agent features into infrastructure customers already own. Semantic-layer and transformation approaches from dbt and Cube compete for the definition and metric layer that Euno wants to make usable by agents. Data-observability vendors such as Monte Carlo and Bigeye own adjacent quality signals, while security products such as Cyera and BigID address sensitive-data discovery and posture from a different control point. Euno's proposed edge is to combine continuously reconstructed organizational context, active metadata, agent-oriented retrieval, and runtime governance in one system. That combination could reduce the friction of preparing enterprise data for many models and agents. It is not yet a proven moat: incumbents can add agent interfaces, open standards can commoditize context exchange, and a platform customer may prefer to assemble the same capabilities from its warehouse, catalog, semantic layer, and identity stack.

**Defense, security, and resilience relevance.** Euno qualifies as dual-use through the security and sovereign-AI layer rather than through a fielded military product. Commercially, its core platform governs which enterprise data an agent can understand and use; the same requirement is acute in defense organizations, intelligence environments, government agencies, and critical-infrastructure operators where sensitive information must be segmented, auditable, and available to the right workflow without broad uncontrolled exposure. A live context graph can help an on-premises or private-cloud agent distinguish authoritative operational data from stale or untrusted material, preserve lineage for after-action review, and enforce role-specific access when autonomous software acts across multiple systems. The Cyera integration shows a concrete security adjacency: Euno describes propagating PII and PCI classifications from warehouse columns into reports, dashboards, and agent-facing business assets, with visibility into who can access or view sensitive data and the ability to revoke access. That is relevant to cyber resilience and AI governance. Calibration is essential: public sources show commercial data and security integrations, not defense customers, classified deployments, air-gapped certification, government authorization, or mission-system use. Euno is best viewed as enabling infrastructure for trusted allied AI, with strategic value rising if it proves sovereign deployment, offline operation, auditability, and policy enforcement in high-consequence environments.

**Growth stage, trajectory, and key diligence risks.** Euno is best classified as early despite its Series A and substantial capital relative to headcount. It has a real product, named integrations, customer references, a nearly 30-person organization, and a clear enterprise problem, but it remains pre-scale in public evidence: the company has not disclosed revenue, customer count, net retention, deployment volume, service-level metrics, or independent performance evaluations. Its trajectory depends on converting the data-catalog and governance pain point into a control plane that customers cannot easily reproduce with existing cloud tooling. The key diligence questions are: (1) whether the context graph stays accurate as schemas, policies, owners, and business definitions change; (2) whether agent responses improve measurably against a well-governed baseline; (3) whether persona-based controls prevent sensitive-data leakage without blocking legitimate work; (4) how much implementation and metadata cleanup remains services-heavy; (5) whether Snowflake, Databricks, Microsoft, or model providers absorb the feature set; (6) how the company protects customer metadata and supports private or air-gapped deployment; and (7) whether the Israeli-U.S. operating structure can satisfy public-sector procurement and data-residency requirements. The near-term milestones are repeatable paid deployments, quantified agent reliability, security certifications, a durable partner channel, and evidence of adoption in regulated or resilience-critical environments.