The Context Layer for AI Your AI doesn't know your business.
Build a shared understanding of your data, your business logic, and your institutional knowledge, and make it available to every AI tool you run.
Book a Demo See Product Tour Trusted by AI-forward enterprises Story Story Story Story Story Story Story See All Customer Stories The Observation Enterprise AI fails not because of the model, but because of missing context We've spent years studying how enterprises deploy AI agents.
The pattern is consistent: teams build impressive prototypes, but hit a wall when moving to production.
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, .
The enterprise that best articulates its own knowledge — its data, its processes, its meaning — will build AI that's most useful to its people. context becomes the differentiator “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 Watch Video The AI Context Gap 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.
Who are our top customers this quarter?
Question It Raises Context Layer Answer It Needs User Context Who's asking — and what decision?
CS team optimizes for renewal risk Knowledge Context What does "customer" mean here?
Parent account, individual location not Meaning Context How do you define ? "top" Revenue, orders, or margin?
Top = highest , not order count net ACV Data Context Which tables hold ? net ACV CRM vs. billing?
Use joined with billing.subscriptions crm.accounts Data Context How do you calculate ? revenue Gross or net of discounts?
Revenue net of discounts and refunds Why Customers Love Atlan The only proven way to create context Watch Video Watch Video Watch Video Watch Video Item 1 of 4 The Context Pipeline It comes from a pipeline.
Context doesn't come from a prompt.
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.
Catalog Governance Lineage Quality Glossary “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.
Description Generator Term Linkage Metrics Generator Semantic Views Ontology Generator “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.
Conflict Resolution Annotation Labelling Certification Feedback Loops “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.
MCP Server SQL APIs SDK Evals & Traces “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 Industry Recognition A leader across every context category 95% of G2 users seeAtlan as a true partner Read the G2 report “The Metadata Lakehouse forms the core foundation, built on an
