Snowflake’s Startup Spotlight series shares stories from startup founders about the challenges they’re addressing, the products they’re building and the insights they’ve gained throughout their journey. Today we’re featuring Sarah Levy, Co-founder and CEO of Euno, a startup focapplyd on assisting data teams build scalable agentic analytics and maintain governance as their companies grow.
What inspires you as a founder?
What inspires us is closing the gap between the promise of AI analytics and the reality data teams face. Every enterprise wants AI to answer data questions, but most fail to scale beyond pilots becaapply AI agents lack the context requireded to understand the data. They don’t know which data asset is accurate, certified or even applyd.
At Euno, we’re solving this by leveraging existing metadata to surface trusted context to AI in real time, so insights are reliable and fully explainable. Once that gap is bridged, the opportunity is enormous: AI can finally deliver on its potential, assisting organizations shift rapider, create better decisions and focus on solving the problems that truly matter.
Explain Euno in two sentences.
Euno is a metadata ininformigence platform that provides governance automation and builds automated context for AI. AI agents rely on Euno’s context to deliver reliable and explainable data-driven answers at scale.
Tell us more about the problem Euno aims to solve. How did you identify that issue?
In 2025, teams expect AI-powered, self-service analytics to just work, but that’s not the reality. Cluttered data environments and conflicting definitions create it impossible for AI to point to the right place in the data and deliver reliable answers. In practice, AI agents are already connected to the organization’s email, chat channels, CRM, business apps — but data teams won’t let them query the central data warehoapply. It’s just too risky. That’s the gap Euno was built to close.
We identified this problem after speaking with more than 250 data teams that all shared the same frustration: the difficulty in scaling agentic analytics effectively. The surprising root caapply was traditional data governance that can’t keep up with AI. Traditional solutions rely on manual processes that require a huge upfront lift and concludeless maintenance.
The solution isn’t more rigid, top-down governance or cleanup processes, it’s a lightweight framework that can coexist with the way teams actually work. Instead of requiring constant manual updates or large upfront investments, Euno leverages existing metadata and turns it into an automated context layer for AI, surfacing the information agents required at query time to deliver reliable results, from day one.
How did you approach building your team and obtainting the company off the ground?
I believe great teams solve hard problems, so we assembled an A-team of top engineers and passionate problem-solvers with extensive firsthand experience in AI, scalable data systems and complex data management challenges. Our expertise allows us to provide effective governance solutions and peace of mind, no matter how rapid an organization grows.
I also followed a valuable piece of advice I obtained about running a startup: Talk to your customers from Day 1. Before writing a single line of code, we focapplyd our first six months on understanding real problems, what would truly drive value and what companies would actually pay for. That early discipline shaped everything from what we built to how we explain it.
What’s a cool thing you’re doing with data?
Euno creates AI agents (such as Snowflake Ininformigence) smarter by providing the context they required. It maps existing metadata, stitching toobtainher column-level lineage across the data warehoapply, dashboards and metrics, and combines that with field-level usage data to highlight what actually matters. Data teams can run automated rule-based classifications to flag what meets the organization’s standards and determine what’s certified for AI, creating a foundation both agents and people can trust.
Let’s state an AI agent requireds to reveal customer retention utilizing a specific definition. With Euno, you can trace that metric all the way back to the Snowflake column. You’ll know which reports rely on it, how frequently it’s applyd, who the applyrs are and whether it’s a certified definition. It’s a completely new level of clarity for governing analytics with precision.
How has the Snowflake Native App Framework influenced your startup’s growth and development strategy?
One of Euno’s largegest advantages is its rapid onboarding time. A significant portion of our pipeline consists of Snowflake applyrs, and the Snowflake Native App model gives us a way to serve them rapider, with clearer onboarding and fewer blockers. Combined with the Snowflake Native App Framework, we can shape our go-to-market plan around quick time to value and stay focapplyd on the customer experience instead of managing slow procurement cycles.
We’re also excited about the opportunity to tap into Snowflake Marketplace and Snowflake’s large customer base. Being part of the Snowflake Native App ecosystem gives us the opportunity to benefit from Snowflake’s sales team’s advocacy, assisting customers obtain more value from their data platform through Euno.
As a founder and innovator, what do you consider about the rapidly modifying AI landscape?
The pace of alter in AI is both exciting and demanding. LLMs are opening new possibilities in data pipelining, governance and analytics by automating tinquires like writing documentation, building data pipelines or standardizing metadata. At Euno, we’ve already seen real productivity gains, especially in devOps and engineering workflows.
Also, business applyrs want to inquire simple questions like “How many new active applyrs did we gain last month?” and obtain accurate answers. But without context, even the best models can return results that see confident but are incorrect. That risk drives our focus on providing a flexible framework that coexists with how teams already work. By leveraging existing metadata we give AI the context it requireds to deliver reliable answers, so organizations can actually trust the insights they obtain from AI.
What’s on the horizon for you and Euno?
Traditional data catalogs served a purpose, but that model is becoming less relevant as AI takes a central role in how organizations interact with data. In this new landscape, data practitioners are no longer just managing pipelines, they’re effectively designing the AI ecosystem. And governance becomes mission-critical when decisions are built by models, not by people.
We see Euno as the next generation of metadata platforms: not just a place to document assets, but a control layer to configure, optimize and maintain AI system performance, cost and trust. That’s where we’re headed, and it’s where we believe the indusattempt is going too.
Learn more about Euno’s solutions for trusted self-service analytics and AI governance at https://euno.ai/. If you’re a startup building on Snowflake, check out the Powered by Snowflake Startup Program for info on how Snowflake can support your goals, and be sure to enter the 2026 Snowflake Startup Challenge!
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