See Euno make agents fly on your data

Get end-to-end column-level lineage
Auto-classify your data assets
Reduce clutter and spend
Sustain trust in AI, at scale
Automate governance workflows
Is your company currently using dbt?
Choose your data warehouse(s)
Choose your BI tool(s)
We'll only use your email to schedule the call.
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Agents work better with context.
So do people.
WHAT HAPPENS AFTER YOU BOOK
1

Meet Euno

Let's learn about you and what you want to know about Euno. We'll share common use cases and answer every question. No hard feelings if it stops there.
Generate live lineage from warehouse to BI, stitched with usage, ownership, and logic. Trace every data flow and propagate signals across your stack.
2

See Euno

Together, we'll work out who needs to see what, then build a tailored demo that walks your stakeholders through Euno and makes it real for your organization.
Apply custom governance rules  to
automatically label assets, so agents instantly know what data can be usedby AI (e.g., certified, PII-free, healthy).
3

Prove Euno

A good POC takes two weeks, not months. You'll see Euno's value right away, and how easily it extends to other use cases.
Ensure AI agents always use the right
data and logic, so every output is
accurate and fully explainable,
no matter the agent type or task.

FAQs

What does a Euno POC look like?

Each POC is tailored to your specific goals. One potential shape is that you supply around 20 real, high-usage business questions as a benchmark. Euno connects and crawls in about a day, reconstructs the graph, then calibrates through glossary terms, rule-based labels, personas and skills. Accuracy is scored against your benchmark, then tested against a second held-out set to measure long-tail performance.

How long does it take to see value?

At enterprise scale, in weeks. A typical proof of concept runs one day to connect and crawl, then roughly two weeks of calibration against your own benchmark questions.

Which integration should we connect first?

If you use dbt, start there, since it shows transformations and logic and establishes model relationships quickly through GitHub or dbt Cloud. Without dbt, start with the warehouse to map structure, usage and cost. If BI sprawl is the pressing question, start with the BI tool. Most integrations take 15 to 25 minutes.

Who needs to be involved on our side?

Usually three people, and often the same person twice. Your data platform or engineering team grants source credentials and permissions. Someone in analytics or the business supplies the benchmark questions and decides what counts as a correct answer. Whoever owns governance sets the rules and personas.

Do we need dbt, Snowflake or a particular stack?

No. Euno is deliberately stack-agnostic and federated, which is the point. It reconstructs lineage across whatever combination of warehouses, transformation tools, BI tools and upstream systems you run, including long lineage from sources such as SQL Server, Salesforce, S3 and Postgres into Snowflake or Databricks.

Which AI agents and clients does Euno work with?

Any MCP-compatible client. Documented setups cover Claude, Cursor, ChatGPT in developer mode, Claude Code, Gemini Enterprise, Microsoft Copilot Studio and Snowflake Intelligence, alongside a Slack assistant, SDK/CLI and REST API for custom agents. Clients without remote HTTP MCP support can use a local stdio bridge.

Does Euno access our actual data?

Euno reads metadata and query logs rather than the contents of your tables. It does not query your data and does not affect warehouse performance.

Who is Euno for?

Enterprises putting AI agents to work on their data. Buyers are typically data platform, analytics engineering, data governance and AI/ML platform leaders. Day-to-day users are analytics engineers, data engineers and stewards, plus business users reaching Euno through the agents it serves.

What do you add over dbt and an agent, or Cortex, or Fabric? We already have that.

Inside one well-modelled dbt project, dbt plus an agent works. The question is what happens at the boundaries, because business logic also lives in BI workbooks, in ETL upstream of the warehouse, and in operational systems nobody modelled. Ask a question spanning two of those and the agent has no lineage linking them, so it guesses at the join.

How do we design a POC that measures the right thing?

Agree which workflow is being evaluated before it starts, verify that workflow works on day one, and instrument who uses what. A POC measures whatever people actually run, so if most of a team evaluates through a path nobody recommended, the result reads fairly on that path and misleadingly on the product.