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About
Atlan serves as the context layer for enterprise AI, solving one of the most critical challenges in AI adoption: agents and models that lack the business context needed to reason effectively about your organization's data. While most enterprises can build AI prototypes, they hit a wall in production because no model understands what your data means, how your teams define key metrics, or how your systems are structured. Atlan addresses this by enabling enterprises to build an Enterprise Data Graph — a unified, enriched representation of all data assets, including column-level lineage, business glossaries, data products, and metadata. This context is then exposed to any AI tool through an MCP Server, active metadata feeds, and an open App Framework. Key capabilities include a full-featured Data Catalog with intelligent automation, column-level lineage for tracing data flows, a Business Glossary for aligning teams on shared definitions, AI Governance and Data Quality modules, and a Metadata Lakehouse for storing and querying enriched metadata at scale. Atlan also supports personalization and curation so different teams — finance, sales, engineering — get contextually relevant information. Atlan is purpose-built for AI-forward enterprises in finance, technology, healthcare, manufacturing, media, and retail. It integrates with hundreds of data connectors and supports developer-first extensibility. Teams use Atlan to ensure their AI agents can answer business questions accurately, consistently, and in alignment with how the organization actually defines its data.
Key Features
- Enterprise Data Graph & Catalog: Build a unified, enriched graph of all your data assets with column-level lineage, connectors, and intelligent automation to keep metadata current.
- MCP Server for AI Context: Expose your enterprise data context to any AI agent or tool via a Model Context Protocol (MCP) server, enabling agents to reason accurately about your business.
- Business Glossary & Shared Definitions: Create organization-wide definitions for key metrics and concepts so AI agents and human teams share the same understanding of terms like 'revenue' or 'top customer'.
- AI & Data Governance: Enforce data quality, compliance, and AI governance policies across your data estate with built-in workflows and visibility into how data is used by AI systems.
- Active Metadata & Personalization: Surface contextually relevant metadata to different teams and roles through personalization and curation, ensuring each user gets the right context for their workflows.
Use Cases
- Providing AI agents with accurate business context so they can answer questions like 'Who are our top customers this quarter?' using the correct definitions and data sources.
- Building and maintaining a company-wide Business Glossary so all teams and AI tools share consistent definitions for key metrics like revenue, churn, and conversion.
- Governing AI usage across the enterprise by tracking which AI tools access which data, enforcing data quality standards, and ensuring compliance with regulatory requirements.
- Enabling data engineering teams to trace column-level lineage across complex pipelines to understand data origins, transformations, and downstream impact.
- Accelerating the transition of AI prototypes to production by giving agents the institutional knowledge they need to reason reliably about business-specific data structures.
Pros
- Closes the AI Context Gap: Directly addresses why enterprise AI fails in production — missing business context — by providing a structured, queryable layer of institutional knowledge to any AI tool.
- Works Across the Entire Data Stack: With hundreds of connectors and open APIs, Atlan integrates with virtually any data source, BI tool, or AI platform, making it a central hub for enterprise data context.
- Developer-First Extensibility: The App Framework and MCP Server allow engineering teams to build custom integrations and expose context programmatically to their specific AI agent architectures.
Cons
- Enterprise Pricing Complexity: Atlan is designed for large enterprises and requires a demo to get pricing, making it inaccessible or overly complex for smaller teams or individual use cases.
- Significant Onboarding Investment: Building a meaningful Enterprise Data Graph and Business Glossary requires substantial upfront effort from data engineering and business teams to populate and maintain.
Frequently Asked Questions
What is the AI context gap Atlan solves?
The AI context gap is the disconnect between what an AI model knows generically and what it needs to know about your specific business — your data definitions, metric calculations, organizational structure, and institutional knowledge. Without this context, even the best AI models give inaccurate or irrelevant answers to business questions.
How does Atlan connect to AI agents and tools?
Atlan exposes your enterprise data context through an MCP (Model Context Protocol) Server, active metadata APIs, and an open App Framework, allowing any AI agent or tool to query and consume your organization's knowledge layer at runtime.
What is an Enterprise Data Graph?
An Enterprise Data Graph is a connected map of all your data assets — tables, columns, dashboards, pipelines, and business terms — enriched with lineage, ownership, quality scores, and business definitions, giving AI and humans a complete picture of your data landscape.
Which industries and teams use Atlan?
Atlan is used by AI-forward enterprises across finance, technology, healthcare, manufacturing, media, and retail. It serves data engineers, data analysts, data governance teams, and business stakeholders who need a shared understanding of enterprise data.
Does Atlan support data governance and compliance?
Yes. Atlan includes dedicated AI Governance and Data Governance modules with data quality monitoring, policy enforcement, and full lineage tracking to support compliance and responsible AI deployment.
