Data cataloging that AI can reason over.
Not just describe.
Organizations need to describe, classify, and govern complex data, and make it accessible to both people and AI systems with confidence. Data Graphs goes beyond simple catalog tools to deliver a living, interconnected map of your data landscape that is both human-navigable and machine-readable.
From scattered knowledge to structured intelligence.
Fragmented data in an AI-first world
Data is scattered across siloed systems, stored in inconsistent formats, and locked behind tools that were never designed for the age of AI. At the same time, the rise of Agentic AI demands a foundation that goes far beyond traditional databases or document stores. AI systems need governed, relationship-rich, semantically consistent data to reason over, not just retrieve from.
The gap between where most enterprise data sits today and what AI-driven systems need is significant. Filling that gap requires more than a database or a simple metadata catalog. It requires a multi-capability data backbone.
The Gates Foundation and global agricultural data cataloging
The Gates Foundation deployed Data Graphs to address one of the most complex data cataloging challenges in global agriculture: bringing together diverse, fragmented agricultural and AgriTech datasets from around the world and organizing them against industry-standard dictionaries and classification systems.
Using Data Graphs' flexible domain modeling and semantic classification capabilities, the Foundation built a governed data catalog that describes datasets in structured, machine-readable detail, mapped to recognized agricultural vocabularies.
Deployed in a complex, multi-stakeholder environment where interoperability, governance, and AI readiness are non-negotiable.
Your schema, your vocabulary, your rules
Data Graphs is domain-agnostic by design. Its visual domain modeling tools allow organizations to define their own schema, capturing the concepts, relationships, and controlled vocabularies that matter to their business. Whether you are modeling financial instruments, agricultural products, clinical evidence, media assets, heritage collections, or regulatory records, Data Graphs adapts to your domain.
AI-assisted workflows support the extraction of structured data from unstructured sources such as PDFs and images, accelerating the transition from document-centric to data-centric operations.
Your catalog talks to AI natively
Data Graphs includes a fully hybrid GraphRAG and Agentic AI layer as a core capability. Users ask natural language questions grounded in the truth of their own governed data, with no coding required.
The same AI layer is exposed externally via native Model Context Protocol (MCP) services. External AI agents, copilots, and automated workflows can interact with your catalog directly, with governance, permissions, and business rules enforced automatically at the point of query.
Beyond traditional catalog tools
Ready to catalog your data for the AI era?
See how Data Graphs can turn your fragmented data landscape into a governed, AI-ready knowledge foundation.