Where humans and AI build shared truth.
When AI works on consequential decisions, it needs to understand the world it's operating in. That understanding doesn't live in any single system. It's built in the Context Studio.
Where humans and AI build shared truth.
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."
Andrew Reiskind
VP, Data Management
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."
Andrew Reiskind
VP, Data Management
AI fails in production because it doesn't understand the world it's operating in.
To answer any question correctly, an agent needs four layers of context: data, meaning, knowledge, and user. When organizations try to build this context, they hit the same three walls.
The Cold Start Problem
Critical business logic already exists but not in a form AI can use. Getting to a credible V1 means weeks of manual work before anything is testable.
Stuck in Testing Hell
There's no repeatable way to simulate what an agent will say before it says it. Validation relies on spot checks and intuition. Without a clear definition of "done", shipping feels risky. One wrong response may break trust.
Scaling Beyond One Agent
Each new use case introduces new context, definitions, and edge cases. Time, effort, and risk compound without a shared, well-governed foundation.
The Cold Start Problem
Critical business logic already exists but not in a form AI can use. Getting to a credible V1 means weeks of manual work before anything is testable.
Stuck in Testing Hell
There's no repeatable way to simulate what an agent will say before it says it. Validation relies on spot checks and intuition. Without a clear definition of "done", shipping feels risky. One wrong response may break trust.
Scaling Beyond One Agent
Each new use case introduces new context, definitions, and edge cases. Time, effort, and risk compound without a shared, well-governed foundation.
Where your organization builds the world model AI needs to be correct.
Four layers. One architecture. Every AI agent reads from it.
Enterprise Data Graph
Connectors & Apps
100+ native connectors across every data source and tool
Lineage
Column-level cross-system graph, auto-built from pipelines and code
Context Agents
AI agents that build and maintain your business ontology
Enterprise Data Graph
Context Repos
Versioned, bounded context units any agent can read and consume
Simulate & Test
Generate evals from real business context before you ship
Context Agents
AI agents that build and maintain your business ontology
Data Marketplace
Data Products
Context delivered where your people already work
Access & Governance
Automated request, approval, and compliance workflows
Conversational Experiences
Natural language access to any data product, in any tool
Context Lakehouse
From 800 tables and no shared definitions to production-ready context — in six steps.
Context Studio structures the workflow enterprises try to run in spreadsheets, Confluence, and Slack threads — and runs it in one place.
Find the right context sources
Context Studio starts by identifying the assets that matter for the use case. Not all 800 tables. The 12 that will make or break your AI agent's accuracy — surfaced from usage patterns, lineage, data quality signals, and downstream consumption.
Let AI kick off context creation
Domain experts fill context gaps
Test rigorously with automated evals
Push context to your AI systems
Monitor and improve context quality
The future of context, validated by analysts and customers
Everything you need to know about Context Studio
Context Studio is Atlan's enterprise context layer — a structured workspace where humans and AI collaborate to build, verify, and maintain shared understanding of your data assets. It replaces the fragmented workflows across Confluence, Slack, and spreadsheets with a single, governed environment.
A data catalog stores metadata. Context Studio builds meaning. While a catalog tells you what a table is called and where it lives, Context Studio tells your AI agents what that table means, how it should be used, who owns it, and what decisions it powers — the contextual layer that makes AI-driven data work trustworthy.
Yes. Context Studio connects to your existing sources — Snowflake, Databricks, dbt, Looker, Airflow, and more — through Atlan's 200+ native integrations. It layers context on top of whatever you already have without requiring a rip-and-replace.
AI bootstraps the first draft of context from usage patterns, lineage, and existing documentation. It then surfaces gaps and routes them to the right domain experts — data owners, stewards, or SMEs — for review and enrichment. Every contribution is tracked, versioned, and auditable.
Context Studio continuously monitors how context performs in production through automated evaluations, agent feedback loops, and quality drift signals. When context degrades or gaps are detected, the system routes updates back through the human-in-the-loop workflow to keep your shared truth current.
Context Studio is part of the Atlan platform. It is deeply integrated with Atlan's metadata lakehouse, lineage engine, and governance capabilities — which is what makes it possible to automatically surface the right assets, route to the right experts, and propagate verified context to AI systems at scale.