Atlan

What Atlan does

Atlan is the context layer for AI, providing the infrastructure that gives AI agents the business context they need to work with enterprise data. It connects metadata, lineage, governance, and semantic definitions into a unified graph so AI systems understand what the data means before they use it. Atlan helps enterprises build a shared understanding of their data, business logic, and institutional knowledge, making it available to every AI tool. The platform addresses the AI Context Gap by unifying business systems in an Enterprise Data Graph, allowing AI agents to bootstrap the context layer, and enabling human collaboration to resolve, annotate, and certify context. Atlan's infrastructure, the Context Lakehouse, is designed for the AI era, being Iceberg-native, open, and built for agent speed. It offers proactive impact analysis through integrations with tools like GitHub and dbt, helping data teams prevent issues before they reach production.

Key capabilities

• Context Layer for AI
• Unified Enterprise Data Graph
• Metadata Management
• Data Lineage
• Data Governance
• Semantic Definitions
• Context Pipeline (Unify, Bootstrap, Collaborate)
• AI-powered context generation (Description Generator, Term Linkage, Metrics Generator, Semantic Views, Ontology Generator)
• Context Lakehouse infrastructure
• Proactive Impact Analysis
• Open APIs and extensibility (Atlan App Framework)
• Native connectors for data warehouses, databases, BI tools, ETL pipelines, orchestrators, data quality tools
• Automated metadata work
• Embedded & Personalized Experiences
• Intuitive, role-based interface
• Strong lineage and smart search capabilities
• Seamless integration across the data stack

Use cases

• Building a shared understanding of data, business logic, and institutional knowledge for AI tools
• Enabling AI agents to reason effectively about business data
• Optimizing customer service or sales decisions based on contextual data
• Creating a context pipeline to make business systems, data estate, and people's knowledge usable for AI
• Generating asset descriptions, linking business terms, and surfacing top business questions using AI
• Resolving, annotating, and certifying AI-drafted context by humans
• Preventing broken dashboards and lost trust due to data model changes
• Providing clear visibility of data flow and dependencies within GitHub workflows
• Improving BI reporting accuracy and reliability
• Scaling context development and robust definition building across data estates
• Governing data for AI-ready data products
• Achieving data democratization with AI-assisted discovery and end-to-end lineage
• Building a context layer for federated data ownership
• Unifying context across tech stacks
• Powering analytics agents
• Activating data meshes
• Embedding privacy context into automated data processes
• Driving data transparency and transformation
• Gamifying data governance
• Solving problems without asking teammates navigation, lineage, or ownership questions
• Finding the right tables quickly

AI approach

Atlan provides a "Context Layer for AI" that gives AI agents the business context needed to work with enterprise data. It unifies metadata, lineage, governance, and semantic definitions into a unified graph. Atlan AI agents read this Enterprise Data Graph to generate asset descriptions, link business terms, and surface business questions, aiming to bootstrap the context layer before human review. It focuses on contextual intelligence to make AI useful in enterprises.

Tech named: AI agents, Enterprise Data Graph, Context Pipeline, Atlan AI, Microsoft Azure, OpenAI, Context Lakehouse, Iceberg-native, knowledge graph, vector storage & analytics

What it says sets it apart

• The only proven way to create context for AI
• Context doesn't come from a prompt, it comes from a pipeline
• AI-native governance platform blending intelligent automation with deep integration and user accessibility
• Highest scores for data lineage, impact analysis, semantics, orchestration, and automation in analyst reports
• Purpose-built to solve root causes of governance failure (manual bottlenecks, low adoption, closed for new initiatives)
• Ability to scale automation across complex data ecosystems
• Enables data democratization with AI-assisted discovery, end-to-end lineage, and Netflix-like personalization
• Open & Extensible Platform for Innovation with open APIs and extensibility for custom integrations
• Customer-centric approach with a customer experience team larger than its sales team
• Metadata control plane captures, unifies, and contextualizes enterprise data, powering AI and agentic solutions
• The only platform that brings all the humans of data & AI together
• Context Lakehouse architecture built for the AI era: Iceberg-native, open, and built for agent speed
• Provides proactive impact analysis before changes hit production
• Clean and easy-to-understand lineage compared to competitors

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.