Your AI doesn't know your
business.
Let’s fix that.
Build a shared understanding of your data, your business logic, and your institutional knowledge, and make it available to every AI tool you run.
Enterprise AI fails not because of the model, but because of missing context
The wall isn't the models. It’s that no agent can reason effectively about a business it doesn't understand — what your data means, how your teams work, how your company defines "revenue" compared to the rest of the world.
Key Insight
When every organization has access to the same intelligence, context becomes the differentiator. The enterprise that best articulates its own knowledge — its data, its processes, its meaning — will build AI that's most useful to its people.
“We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. We had no way to interpret human language against the structure of the data.”
Joe DosSantos
VP, Enterprise Data & Analytics
One question for AI.
Multiple layers of context.
Through our work with enterprises, we've found that even a simple agent task requires multiple layers of context working together. Miss one layer and the answer breaks.
Question It Raises
Context Layer
Answer It Needs
Who's asking — and what decision?
CS team or Sales team?
CS team optimizes for renewal risk
What does "customer" mean here?
Account or individual?
Parent account, not individual location
How do you define "top"?
Revenue, orders, or margin?
Top = highest net ACV, not order count
Which tables hold net ACV?
CRM vs. billing?
Use billing.subscriptions joined with crm.accounts
How do you calculate revenue?
Gross or net of discounts?
Revenue net of discounts and refunds
The only proven way to create context
Context doesn't come from a prompt. It comes from a pipeline.
What if every agent knew what your best analyst knows? Your business systems, data estate, and people already hold the context you need. The context pipeline makes it usable.
UNIFY
Unify business systems in the Enterprise Data Graph
80+ connectors pull context across your entire data estate — warehouse SQL, BI definitions, and business applications — into one living graph. That graph is what everything else in the pipeline builds on.
“Within the first year after that we cataloged over 18 million assets, defined more than 1300 glossary terms. Atlan had lineage across our on-prem Oracle databases, BigQuery, and Looker..”
Kiran Panja
Managing Director, Cloud & Data Engineering
BOOTSTRAP
Let AI bootstrap your context layer
Atlan’s AI agents read the Enterprise Data Graph — your SQL query history, BI semantics, and pipeline code — and generate asset descriptions, link business terms, and surface your top business questions. The first 80% of your context layer is ready before a human reviews a single line.
“We’re scaling context development as much as possible, and where can we leverage Atlan AI to build the most robust definitions across our data estate.”
Takashi Ueki
Head of Enterprise Data & Analytics
COLLABORATE
Humans resolve, annotate, and certify before context ships
The AI draft is a starting point, not the final word. Your domain experts resolve conflicts between sources, annotate edge cases, and certify what’s production-ready. What ships is what your team trusts.
“Atlan gives us a UI that our community can use to edit, update and manage classifications as well as other metadata enrichments into a verified state.”
Sherri Adame
Enterprise Data Governance Leader
ACTIVATE
Certified context flows to every AI agent across your stack
Production-ready context serves every downstream tool through SQL, APIs, and the Atlan MCP server. Evals, traces, and memory feed back into the pipeline and context gets sharper with every interaction.
“All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan’s MCP server.”
Joe DosSantos
VP, Enterprise Data & Analytics
A leader across every context category
“The Metadata Lakehouse forms the core foundation, built on an open and highly performant architecture. It is designed to be Iceberg-native and includes a knowledge graph for business domains, vector storage, and analytics, which is purpose-built for AI.”
Leader in the 2025 Gartner® Magic Quadrant™ for Metadata Management Solutions
Read the Gartner MQ report
“Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. They take a partnership and co-innovation based approach, which is reflected in their App Framework as a marketplace for context.”
Leader in the 2026 Gartner® Magic Quadrant™ for Data & Analytics Governance
Read the Gartner D&A report
A Leader and a Customer Favourite in the Forrester Wave™
Data & Analytics Governance Solutions and Enterprise Data Catalogs
Context will make AI worthy of
humanity’s most important moments
We hold strong convictions about how the the context layer should be built.
These shape every decision we make.
Context is a Team Sport
Your frontline teams — not just engineers — should be able to read, question, and improve the context that shapes how AI behaves. The best context comes from people working together.
AI-Native, Built for Change
Your context layer should outlive any single technology cycle. Today it powers MCP and A2A. Tomorrow, whatever protocol comes next — no migrations, no rebuilds.
Open & Portable
Your context should move freely across agents, models, and clouds. You should never be locked into a single vendor's representation of your own knowledge.
Bridge the context gap.
Ship AI that works.
