Euno
by Euno (Delphi.io Inc.)
AI-native context platform that gives enterprise agents governed, trustworthy data
Euno is an enterprise context platform that continuously reconstructs lineage, meaning, ownership and governance rules across a company's data and AI stack, then serves that context to AI agents at query time. It is built for data platform and governance teams who need agents such as Claude or Cursor to query warehouse data accurately without exposing PII or uncertified tables.
Euno is an AI-native context platform for enterprises trying to put autonomous agents on top of a real data stack. Its premise is that enterprise data is documented for humans, not machines, so an agent asked a business question has no reliable way to know which table is authoritative, what a metric means, who owns it, or whether it contains regulated data. Euno's 'context brain' addresses that by continuously analysing patterns across an evolving metadata graph — warehouse tables, dbt models, Looker and Tableau assets, query history and ownership signals — and inferring the institutional knowledge that normally lives in people's heads. It exposes that as column-level lineage from source through transformation to BI dashboards and downstream agents, conversational discovery over metrics and models, automated labeling that marks assets compliant, PII-free or AI-ready against governance rules, PII exposure tracking that shows where sensitive columns propagate into the business layer, and governance workflows that fire notifications, tickets or recommendations when metadata changes. Crucially, context is delivered persona-based and task-specific at query time rather than as a catalogue an agent must browse, and access rules travel with it. The company claims this compresses building a production context layer from roughly a year to a few weeks. Euno was founded in 2023 by CEO Sarah Levy and CTO Eyal Firstenberg, runs about 30 people across San Francisco and Tel Aviv, and named AlphaSense and Zayo Group as customers when it raised a $23 million Series A led by N47 on 9 September 2026, bringing total funding to $29 million. Gartner listed it in its 2026 Cool Vendors in Data Management report.
The head of data platform or analytics engineering at a company that already runs Snowflake or Databricks with dbt and a BI layer, and has been asked to make AI agents query that data safely — they are the ones who own the semantic/context layer that agent projects stall on.
A governed, continuously-maintained context layer that agents can query in weeks instead of the ~year a hand-curated semantic layer normally takes, with PII exposure and asset certification enforced in the same graph.
At a Glance
- Category
- Data & Analytics
- Pricing
- Contact for pricing, Subscription
- Target Market
- CDOs, Data Engineering Leaders, Analytics Engineers, Data Governance Leads, CIOs
- Deployment
- Cloud-only
- Founded
- 2023
- Headquarters
- San Francisco, United States
- Team Size
- 11-50
Key Features
- ✓Context brain
Continuously researches and reconstructs lineage, meaning, ownership and governance rules across the entire data and AI stack, without manual stewardship on every asset.
- ✓Conversational discovery
Lets users query complex relationships between metrics, models and dashboards in natural language instead of navigating a catalogue by hand.
- ✓Column-level lineage
Traces data flow from source columns through transformations all the way to BI dashboards and downstream AI agents, so impact analysis is exact.
- ✓Automated labeling
Applies governance rules to mark assets as compliant, PII-free or AI-ready automatically, which is what makes certification tractable at enterprise scale.
- ✓PII exposure tracking
Maps how sensitive columns propagate into the business and semantic layers, producing evidence of exactly what an agent can reach.
- ✓Persona-based agent context
Delivers task-specific, role-scoped context to AI agents at query time rather than exposing the whole catalogue to every caller.
- ✓Governance workflows
Triggers notifications, tickets or recommendations automatically when metadata, ownership or certification status changes anywhere in the graph.
Capabilities
Use Cases
- •Standing up an enterprise AI agent on the warehouse
A data platform team certifies which tables and metrics an agent may query, cutting the context-layer build from roughly a year to a few weeks.
- •dbt-to-BI impact analysis
Analytics engineers see which Looker or Tableau dashboards depend on a dbt model before shipping a breaking change, avoiding silent downstream dashboard failures.
- •Privacy impact assessment
A governance lead maps every dashboard and agent touching a PII column, producing DPIA evidence without manually tracing lineage in spreadsheets.
- •Retiring unused data assets
Teams find models, metrics and dashboards nothing depends on and decommission them, cutting warehouse spend and shrinking the surface agents can wander into.
- •Grounding coding and analysis assistants
Tools such as Claude or Cursor receive scoped, governed context about company data instead of inferring schema meaning from column names alone.
Ideal For
Best For
- ✓Preparing a governed semantic and context layer before deploying LLM agents on Snowflake or Databricks
- ✓Column-level lineage tracing from warehouse source columns through dbt models to Looker and Tableau dashboards
- ✓Certifying which datasets are AI-ready, PII-free or compliance-approved and enforcing that at agent query time
- ✓Mapping where sensitive data propagates across the BI and semantic layer for privacy and DPIA reviews
- ✓Automating governance actions — alerts, tickets, recommendations — when metadata, ownership or certification changes
Not Ideal For
- ✗Small data teams without a warehouse + dbt + BI stack — the value comes from reconciling metadata across many tools, and a single-tool environment has nothing to reconcile
- ✗Buyers who need published pricing, a self-serve trial or a documented security posture before entering a sales cycle; Euno publishes none of the three on its site
- ✗Organisations requiring on-premises or air-gapped deployment — only a hosted service is advertised
- ✗Teams that want a widely-reviewed, heavily-referenced catalogue: Euno is a ~30-person company with no G2, Capterra or TrustRadius listing, no Hacker News discussion, and two publicly named customers
Deployment
Market Analysis
Pros
- ✓Targets a genuinely expensive bottleneck — the semantic and context layer is the part of an agent rollout that consistently takes longest and is hardest to staff
- ✓Column-level lineage and PII exposure tracking come from the same metadata graph, so governance and enablement are not two separate purchases
- ✓Two named production customers (AlphaSense, Zayo Group) plus a Gartner 2026 Cool Vendors in Data Management listing, which is unusual validation for a company this young
- ✓Cap table is heavy with operators who have built data and security infrastructure (Wiz, Cyera, Eon, Tavily, Tableau), suggesting informed technical diligence rather than generic AI enthusiasm
Cons
- ✗No independent practitioner record exists: Euno has no G2, Capterra or TrustRadius profile, and a Hacker News Algolia search for it returns zero stories, so there is no unfiltered account of how it behaves in production
- ✗Roughly 30 employees and $29M raised in total is thin for a Fortune 500 buyer standardising a governance layer on it — support depth, roadmap durability and acquisition risk are all real concerns
- ✗Neither the marketing site nor the product page publishes any security or compliance posture (no SOC 2, ISO 27001 or trust page found) even though the product reads enterprise metadata including PII lineage
- ✗No published integration catalogue: dbt, Looker, Tableau, Snowflake and Databricks appear across the vendor's materials, but there is no public list of supported connectors or their coverage depth
- ✗The headline claim that it compresses context-layer work 'from a year to weeks' is the vendor's own, repeated in coverage — no third party has independently measured it
Pricing
Enterprise
Contact for pricing
- ✓Context brain across the data and AI stack
- ✓Column-level lineage
- ✓Automated labeling and governance workflows
- ✓PII exposure tracking
- ✓Persona-based context delivery to AI agents
No list pricing is published anywhere on Euno's site — there is no pricing page, no self-serve tier and no free trial, so every evaluation goes through sales. Nothing public indicates whether it is metered by seats, connected assets or warehouse volume, which makes budget-setting before a first call impossible. Buyers should expect an annual enterprise subscription and should ask explicitly what the metering unit is.
Security & Compliance
Sources
This page was written from 5 sources, 3 on domains other than euno.ai.
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