Enterprises have spent decades investing in data infrastructure. Yet many still struggle to find the right data at the right time.
Ask teams across a large organization which numbers are correct or which dashboards to rely on and the answers often vary. Information is scattered across systems, owned by different teams, and documented according to different standards. The result is a familiar paradox. Organizations are more data-rich than ever, but employees need to be able to pull the right results with confidence.
That uncertainty is magnified in the age of AI where dependable data is critical to success. Models and agents don’t interrogate business context or meaning. They consume what they’re given and act on it with confidence whether it’s warranted or not. Unsurprisingly, data quality, governance, and data readiness remain leading obstacles to AI success.
Collate brings order, context, and accountability to modern data environments with an AI-powered enterprise data and semantic intelligence platform. Founded by Suresh Srinivas and Sriharsha Chintalapani, Collate uses AI agents and natural language interfaces to help organizations manage data discovery, quality, governance, lineage, and compliance. The result is a unified platform serving as a single source of data truth, accessible by technical and non-technical workers alike.
Collate serves two audiences. For workers at enterprises interested in better utilizing data, it offers a clearer, shared understanding of data assets across teams, reducing friction caused by conflicting definitions or undocumented assumptions. For AI agents, it supplies trust signals, context, and meaning, allowing them to operate reliably inside enterprise environments where accuracy and governance are non-negotiable.
In July 2025, Collate announced a $10 million Series A venture capital raise and counts Carrefour Brazil, inDrive, Loggi, Gorgias, and many Fortune 500 companies as clients. Global fashion retailer Mango used Collate to unify data discovery, observability, and governance across hundreds of data sources and thousands of users. Previously siloed teams struggled with inconsistent quality and limited visibility, a pattern common across large, regulated data environments. With Collate, Mango improved productivity among data teams by 20%, reduced manual workflows, and strengthened its ability to build reliable machine-learning models.
From Open Source Roots to Enterprise Scale
Collate’s origins trace back to OpenMetadata, one of the fastest-growing open-source metadata platforms. What began as a community-driven project now boasts tens of thousands of users, more than 8,500 GitHub stars, and hundreds of contributors. The project centered around metadata, information about the data itself providing context to make information findable, understandable, and manageable.
“Data about data is the most important data any organization has,” said Srinivas. “Knowing where data comes from, how it’s used, how it changes, and what it means in a business context is mission-critical.”
Srinivas and Chintalapani spent years working on large-scale data systems at companies like Uber, Yahoo!, and within the Hadoop ecosystem. They saw firsthand how data knowledge concentrated among a few experts, even as organizations pushed for broader data democratization. That insight led them to build Collate in a way that helps organizations capitalize on metadata and ensure data can be trusted, especially in complex, highly regulated enterprises.
With Collate, companies can avoid what Srinivas called “data disasters,” situations where employees misinterpret metrics, make assumptions, or draw conclusions without knowing where the data came from or how it was shaped.
Pushing AI Agents Further
Collate joined the Comcast NBCUniversal LIFT Labs Accelerator in fall 2025 to engage directly with enterprise teams, learn how they handle data issues, and explore potential collaboration.
In the near term, Collate is pushing AI-driven automation deeper into data management. Its recent launch AskCollate introduces a conversational interface for enterprise data designed for shared use. Teams can collaborate within a single AI-driven conversation around data management, decisions, and context.
Longer term, Collate sees itself as the intelligent data foundation for enterprise AI. As agentic systems proliferate, organizations will need a trusted semantic intelligence layer that AI can safely operate on. Collate’s bet is that it can help define what “AI-ready” actually means inside complex organizations.
“For years, smart people have been spending most of their time just trying to understand data,” said Srinivas. “Our goal is to take that work away, so people can focus on what actually moves the business forward.”
