The context AI needs
to fly on enterprise data
The context AI needs to fly on enterprise data
to critical info we couldn’t
manage manually at scale.”
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to critical info we couldn’t
manage manually at scale.”
The only enterprise context platform built for how AI agents work
Automated
Governed
Scoped
Imagine what AI can do when it’s
truly connected to your enterprise data…
Imagine what AI can do when it’s truly connected to your enterprise data…
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But AI fails where you need it most:
Why? Because AI lacks
the context to navigate a cluttered,
complex data environment
Why? Because AI lacks
the context to navigate a cluttered, complex data environment
Fragmentation
chaos
SPRAWL
And it’s only gonna get worse...
don’t scale and stay
outdated
behind reality
can’t extend to
business layers
If AI can deliver so much value,
how do you make it work with enterprise data?
If AI can deliver so much value, how do you make it work with enterprise data?
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The AI context platform
for enterprise data
just sit there, it builds trust in AI
your metadata doesn’t just sit there, it builds trust in AI
Unified context for reliable AI across
every data workload
Unified context for
reliable AI across
every data workload
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overhead that held us back and
finally makes enterprise AI real”
removes the manual
overhead that held us back and
finally makes enterprise
AI real”
Zero onboarding lift
transform your metadata into a powerful source of context for your teams and AI.
modern data stack so you can transform your metadata into a powerful source of context for your teams and AI.
Watch a demo
Fit for enterprise scale
mapping of all assets across environments.
your metadata only.
Euno reads your metadata only.
speed and enterprise-grade security.
built for speed and enterprise-grade security.
FAQs
What is Euno?
Euno is the enterprise context platform built for how AI agents work. It continuously reconstructs a live context graph of your data stack, covering end-to-end column-level lineage, usage, health, ownership and business logic, then delivers every agent exactly the context its task needs, governed by persona, at enterprise scale.
Why do enterprise AI agents fail on company data?
Because the context they depend on was built for people rather than machines. Catalogs assume someone browses them, semantic layers assume someone maintains them, and governance assumes someone reads the policy before running the query. Agents do none of that, and they query thousands of times faster than the humans those systems were designed around.
How does Euno work?
Euno connects to your existing stack through native integrations, continuously reconstructs a live context graph from what it observes, applies rule-based labels driven by your institutional knowledge that certify what is AI-ready, and serves scoped, persona-governed context to your agents over MCP, SDK and API.
Is Euno a data catalog?
No. Catalogs are built for humans to browse and stewards to maintain. Euno is a context platform built for how agents work: the graph reconstructs itself, scopes itself to each task, and governs itself by persona. Customers typically replace catalog work rather than adding to it.
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.
Why not build this ourselves with MCP servers and skills?
You can, and for one domain on one schema it works. Hand-built skills go stale on schema drift, need engineering attention whenever business logic changes, and multiply per agent and per domain. That is per-project labor scaling with project count, against shared infrastructure scaling with the stack.
Do we have to rip out our existing catalog, semantic layer or governance tooling?
No. Euno reads them as sources, including security and observability tools such as BigID, Cyera, Microsoft Purview, Monte Carlo and Elementary, and reconciles what they report into one graph. Most customers reduce manual curation and stewardship effort rather than running a migration.
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.
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.