Collate Tackles AI’s Trust Problem with Semantic Intelligence

MENLO PARK, Calif. – February 24, 2026 – As enterprises race to deploy artificial intelligence, a fundamental crisis of confidence is brewing. AI models, particularly Large Language Models (LLMs), often fail to grasp the specific context of internal business data, leading to inaccurate and untrustworthy results. Addressing this critical gap, semantic intelligence company Collate, Inc. today announced a new suite of capabilities designed to ground AI in a deep, verifiable understanding of enterprise data.

The launch introduces the Collate Semantic Intelligence Graph, which transforms an organization's metadata into a machine-readable layer of meaning. Paired with a new AI Studio for building custom agents and an AI SDK for third-party integration, the platform aims to move AI from the realm of error-prone pilots to trusted, production-ready systems.

The Billion-Dollar 'Meaning' Gap in Enterprise AI

The promise of AI agents automating tasks and uncovering insights from corporate data has been tempered by a significant obstacle: they don't understand what the data means. An LLM trained on the public internet has no inherent knowledge of a specific company's definition of "regional revenue" or which database table contains certified customer information. This knowledge gap forces the AI to guess, often leading to confident but incorrect outputs known as "hallucinations."

This challenge is not theoretical. Industry research firm Forrester has projected that poorly governed Generative AI incidents could erase over $10 billion in enterprise value in the coming years due to stock declines, legal settlements, and fines. This reflects a broader trend identified by Gartner, which notes that GenAI has entered the "trough of disillusionment" as early adopters grapple with performance issues, low ROI, and governance failures. The core issue is trust.

“As enterprises deploy AI agents across analytics and operations, the biggest failure mode is rarely the model. It’s the lack of governed, shared meaning across data, including definitions, relationships, and policy context that humans assume but systems can’t reliably infer,” said Mike Ferguson, founder and CEO of Intelligent Business Strategies and Conference Chairman for Big Data London. “Semantic intelligence makes that meaning explicit and machine-readable, so agents can ground their work more consistently. That improves accuracy and enables safer automation.”

From Metadata to Meaning: A Semantic Foundation

Collate’s solution is to build a foundational semantic layer that serves as a single source of truth for both humans and AI. The platform analyzes an organization's entire data landscape, from databases and dashboards to data pipelines, and constructs a knowledge graph on top of its existing metadata. This is not simply a catalog of data assets but an active, traversable map of their relationships and business context.

To achieve this, the company leverages a combination of open standards, including the Resource Description Framework (RDF) and ontologies, which provide a structured way to define concepts and their interconnections. This graph is built using OpenMetadata’s JSON schema extensions, providing a flexible and robust framework. By connecting abstract business concepts to specific data structures, the platform makes enterprise knowledge machine-readable and usable by AI.

“Enterprises have no shortage of data to feed into their AI agents, but what they are missing is shared context,” said Suresh Srinivas, CEO of Collate. “Metadata explains what the data is but not what it means. AI agents will confidently make up that meaning to fill the void. With semantic intelligence, we’re giving organizations a new foundation to build AI that’s grounded in shared definitions, ontologies and meaning, so they can trust the outcomes.”

Further enhancing this contextual understanding, the platform now supports OpenLineage, an open standard for collecting data lineage. This allows the semantic graph to incorporate a complete history of how data is created and transformed, providing crucial context for governance and troubleshooting.

Empowering Teams with Customizable AI Agents

Beyond creating the semantic foundation, Collate is providing the tools to act on it. Central to the launch is the new AI Studio, an environment that enables enterprises to build, customize, and deploy AI agents tailored to their unique data environments. The studio includes a suite of pre-built agents designed to automate common data management tasks:

• Data Quality Agent: Automates the design of data quality strategies and test suites.
• Tier Management Agent: Analyzes usage patterns to assign appropriate criticality tiers to data assets.
• Documentation Agent: Assists in generating and updating descriptions for tables and columns.
• SQL Query Agent: Generates optimized SQL queries from natural language user requests.

Enterprises can use these agents out-of-the-box, customize their behavior, or build entirely new agents and workflows to address specific business needs, from enforcing governance policies to conducting routine inquiries.

To extend this capability beyond its own platform, Collate also introduced an AI SDK. This software development kit, available for Python, Java, and TypeScript, allows developers to embed Collate's semantic context into external applications and agentic workflows. For example, a development team could build a GDPR compliance workflow that uses the semantic graph to automatically identify sensitive data, trace its lineage, and route data subject access requests to the correct owners with the appropriate policy context. Another use case involves integrating with engineering workflows in tools like GitHub to automatically assess the downstream impact of a proposed schema change before it is merged.

Building on an Open-Source Bedrock

Collate's commercial offerings are deeply rooted in its stewardship of the OpenMetadata project, a popular open-source metadata platform. Launched in 2021, OpenMetadata has cultivated a vibrant community with thousands of members on its Slack channel and nearly 9,000 stars on GitHub. Its adoption by over 3,000 enterprises provides a strong, community-vetted foundation for Collate's semantic intelligence capabilities.

This open-source heritage signals a commitment to interoperability and avoiding vendor lock-in, a key concern for enterprises building modern data stacks. The platform's support for a wide array of standards, including RDF, DCAT, and the emerging Open Data Contract Standard (ODCS), along with over 120 data source integrations, reinforces this philosophy. By building on an open and extensible base, Collate aims to provide a unifying semantic fabric that works across an organization's diverse toolchain. The company is betting that by providing this semantic foundation, it can help enterprises finally move their AI initiatives from the lab to the core of their business operations.