Where AI and humans build
business context, together.
Bootstrap, test, and ship the business understanding every AI needs to produce accurate, trustworthy outcomes.
Dylan JacobsTrusted by AI-forward enterprises
"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."
Joe DosSantos
VP Enterprise Data & Analytics, Workday
"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."
Joe DosSantos
VP Enterprise Data & Analytics, Workday
Built to solve the three biggest context challenges.
Context is scattered across every tool.
Most enterprises are stuck here. You have a thousand AI use cases but you don't know what data you have, what it means, or how to make it machine-readable.
"You can create a cortex analyst in five minutes but your data has to be just right for it to work. It would take us a lot more time to get the data right first and then build."
— Leading UK Retail Group
Solved by Context Bootstrapping: Context Engineering Studio reads your existing data graph to auto-generate a semantic layer you can build on.
Go from scattered knowledge to production-ready agents in days, not months.
Don't start building context on a blank page.
The knowledge AI needs already exists in your systems of records, SQL queries, BI dashboards, and communication threads. Context Engineering Studio reads it all, drafts a semantic layer, and lets domain experts refine it. So you can ship in days, not months.
Know when your agent is ready before your users find out it isn't.
The hardest problem in enterprise AI is knowing whether your agent works accurately in real world business scenarios and what context it's missing. Context Engineering Studio reads BI dashboards and SQL queries for context, generates 100s of questions that your AI agent needs to answer correctly, and turns those into an evaluation suite.
Improve every agent with every interaction.
One Context Repo, shared across every agent in your stack via MCP and native integrations, and improved continuously. Every question a user asks your agent — and every correction they make — improves the context repo for every AI that accesses it.
The future of context, validated by analysts and customers
Learn more about context engineering with Atlan.
Everything you need to know about
Context Engineering Studio
Context Engineering Studio is Atlan's AI-assisted workflow for building, testing, and deploying the context AI agents need to answer questions correctly. It combines specialist AI agents that bootstrap context from your existing metadata, human-in-the-loop workflows where domain experts fill gaps, automated evals that validate context before production, and a Context Repo that every AI agent in your ecosystem reads from through MCP. Context Engineering Studio is the engineering environment for the world model your AI runs on.
A semantic layer defines metrics and dimensions for BI tools. Context Engineering Studio goes further — it captures business logic, resolves definition conflicts across teams, documents edge cases, applies regional rules, and governs who owns each definition. The output is a versioned, model-agnostic Context Repo that any AI system reads through MCP, not locked to any single tool or vendor. A semantic layer answers "what does this metric mean." Context Engineering Studio answers "what does this metric mean, for which team, with which exceptions, and who certified it."
A Bounded Context Space is a scoped, versioned, governed environment inside Context Engineering Studio where domain experts do their part of context building. Each space is tied to a specific use case or business domain. Domain experts review AI-bootstrapped definitions, add business logic, resolve conflicts, and approve context for production — in plain language, with no SQL or YAML required. The boundary ensures that conflicting definitions from different teams are resolved deliberately, not silently overwritten.
Context Engineering Studio turns your existing dashboards, reports, and production queries into a test suite. Before any context ships, you run your AI agent against the actual questions your teams ask — surfacing gaps, wrong answers, and missing definitions before your users encounter them. Evals persist across versions, so every improvement is tested against the same benchmark. You would never push code to production without tests. Context Engineering Studio applies the same standard to the semantic layer your agents run on.
Context Repos are model-agnostic and expose context through the Model Context Protocol (MCP). Any AI agent or tool that supports MCP — including Snowflake Cortex Analyst, OpenAI models, Claude, Gemini, and custom-built agents — can read from the same shared Context Repo. You build context once. Every agent benefits. Not locked to Snowflake. Not locked to Databricks. Not locked to any AI vendor.
No. Context Engineering Studio complements existing semantic layers by adding the business context, governance, and conflict resolution they were not designed for. It reads from and enriches existing tools — bringing in definitions, ownership, and edge-case logic that improve the accuracy of any AI agent working on top of your data stack. If you already have a dbt semantic layer or a Snowflake Cortex model, Context Engineering Studio makes it more accurate, not redundant.
Write the context your agents are missing.
Test it. Ship it. Keep it true.
