Giving AI Agents Institutional Knowledge

As companies become increasingly data-driven, the challenge isn’t just accumulating information but enabling artificial intelligence to make sense of it all. Euno , an enterprise AI context platform, has closed a $23 million Series A funding round led by N47, bringing its total to $29 million. This investment will help address the growing need for AI systems that can navigate complex corporate data environments.

“Data in enterprises is often fragmented across multiple platforms,” explains Sarah Levy, co-founder and CEO of Euno. “We’re seeing companies use 10-12 different data tools on average, with knowledge spread between databases, spreadsheets, and even people’s memories.” This creates a significant barrier to AI adoption, as waiting for perfect data consolidation could take years.

How Euno Works

The platform acts as an intelligent layer across existing systems—integrating with data lakes, warehouses, and BI tools. Instead of moving or copying data, Euno collects metadata and usage signals to create a “context graph” that shows how information relates to each other. For example, if an employee asks Claude AI about sales performance, the system can provide not only the answer but also where it came from, who created the analysis, and what metrics are included.

This approach addresses two key challenges:

1. Siloed AI: Different departments often deploy separate AI applications with their own knowledge bases, creating inconsistencies across the organization.
2. Lack of Trust: Business users need to understand not just what an AI agent says but also why and how it arrived at that conclusion.

Addressing Governance Concerns

As AI agents gain access to more institutional knowledge, governance becomes paramount. Euno addresses this through role-based personas that limit access based on job function—ensuring agents only see the data they need for their tasks. This aligns with regulatory scrutiny of AI systems and helps prevent unauthorized information disclosure.