Atlan

Overview

Atlan is a modern data workspace designed to create an enterprise context layer for AI. It connects business systems and metadata sources into a unified graph, enabling organizations to make their data AI-ready by capturing and operationalizing business context—definitions, relationships, and domain knowledge—across the entire data estate.

Key Features

Enterprise Data Graph Connects all business systems and pulls context across the data estate into one living graph, serving as a central repository for metadata and business understanding.

Context Engineering Studio Allows teams to bootstrap, test, and ship business context that AI systems need. Features include:

• Auto-generation of semantic layers from existing data graphs
• Evaluation suites for testing context accuracy across personas
• Version control and rollout management for context repositories
• Live tracing and drift observation for deployed context

Context Agents AI teammates that document tacit knowledge and make data AI-ready by reading and writing context across the stack. Agents handle ontology, descriptions, metrics, quality, glossary, and README documentation.

Context Lakehouse An Iceberg-native, open-format storage layer engineered for AI, featuring graph and file architecture with vector-native AI search capabilities.

Data Lineage Tracks data flow and relationships across systems, providing visibility into how data moves through the organization.

Data Marketplace Delivers context to humans where they work—in Slack, Teams, Claude, ChatGPT, and other tools—ensuring business understanding is accessible across the organization.

Connectors & Apps Integrates with major data platforms and business systems including Snowflake, BigQuery, Databricks, dbt, Looker, Tableau, Confluence, and others.

Governance & Compliance Includes RBAC (role-based access control), GDPR compliance, and metadata management capabilities for data governance and regulatory requirements.

Use Cases

• AI Readiness: Preparing data and context for AI agents and LLMs to produce accurate, trustworthy outcomes
• Data Discovery: Helping teams understand what data exists, what it means, and how to use it
• Governance at Scale: Maintaining consistent definitions and context across distributed data estates
• Semantic Layer Development: Building and testing business logic that multiple AI tools and analytics platforms can consume
• Data Mesh Implementation: Enabling domain teams to own and share context about their data products
• Compliance & Risk: Tracking data lineage and maintaining audit trails for regulatory requirements

Pricing & Access

Pricing is available through enterprise quote only. Contact sales for custom pricing based on organizational needs and scale.

Who Is It For?

Primary Users:

• Data leaders and CDOs building enterprise data strategies
• Analytics and data engineering teams managing data governance
• AI/ML teams preparing data for AI applications
• Organizations implementing data mesh architectures
• Enterprises requiring GDPR and compliance management

Industry Focus: Atlan serves customers across finance, technology, manufacturing, media, healthcare, and retail sectors, with case studies from organizations including Nasdaq, Autodesk, Dropbox, General Motors, Fox, Scripps Health, and Dr. Martens.

Organization Size: Designed for enterprise-scale organizations with complex, distributed data estates and multiple business systems requiring unified context management.