Everything your knowledge graph needs,
before you build it.
A knowledge graph is an ontology filled in with real records. Atlan supplies the parts that make yours trustworthy: the ontology, column-level lineage reverse-engineered from real SQL, and the operational metadata that says which records can be trusted.
A knowledge graph with real entities drawn from CRM, billing, and network operations, joined by named relationships, with a refund-eligible conclusion derived by the ontology rule rather than entered by anyone.
Trusted 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
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets."
Andrew Reiskind
Chief Data Officer, Mastercard
"We have Atlan as the metadata plane across our tech stack, independent of where technology is. We have one place to define the data, understand what it means, and where it comes from."
Oliver Gomes
VP Analytics & Strategy, FOX
"With Atlan we cataloged over 18 million assets and 1,300+ glossary terms in our first year, so teams can trust and reuse context across the exchange."
Kiran Panja
Managing Director, Cloud & Data Engineering, CME Group
WTF IS THE CONTEXT LAYER
A bi-weekly live series for AI leaders and builders. One burning question per episode, an open AMA floor, and guests who've actually built context infrastructure.
One question.
Three kinds of context.
Knowledge is which records are involved. Expertise is how to move between them. Norms are what the graph is allowed to conclude.
Why is drive-through time up this week?
Question It Raises
Context Type
Answer It Needs
Which records are involved?
Store 412 from ops, its region from the org system, last week's readings from the POS feed — one connected graph
How do you get from one to the next?
Traverse store to region to service metric — a few hops from a known starting point, in milliseconds
What is it allowed to conclude?
Only what the rules permit. Every conclusion is traceable to the rule that fired
No agent runs on
a knowledge graph alone.
It holds what is true right now. What a metric means, which table is certified, and how to act on the answer sit elsewhere.
How do I traverse business knowledge?
The ontology plus the instance data that conforms to it, drawn from CRM, billing, and network operations. Because the rules travel with it, the graph derives conclusions nobody entered.
Explore Knowledge GraphONTOLOGY + INSTANCE DATA
DERIVED BY THE RULE, NOT ENTERED BY ANYONE
A knowledge graph is only as good
as the context you feed it.
Real records from real systems, mapped onto the meaning you already declared.
A graph is only as good as the records feeding it. Atlan connects across systems of record, data, knowledge, and work, and maps what it finds onto the entities and relationships your ontology declares.
ORDERS_RAWDIM_CUSTOMERSREVENUE_AGGA wrong rule is a wrong answer at scale, so rules get owners.
A graph that derives conclusions is only trustworthy if the rules producing them are owned and tested. Atlan manages entities, relationships, and rules the way software manages code.
The context behind your graph, reachable by every agent you run.
A graph an agent cannot reach at query time is a diagram. Atlan serves traversal through whichever interface the consumer speaks, and stores the underlying context in open formats.
Cortex AnalystSee the context layer, built live.
Watch Atlan engineers bootstrap, test, and deploy enterprise context on real data — from cold start to production-ready agents.
Learn more about knowledge graphs with Atlan.
Knowledge Graphs for LLMs
How a knowledge graph grounds an LLM in your business — turning fluent, general-purpose answers into ones rooted in what your data actually means.
Context Graphs for AI Agents
A knowledge graph captures what things mean. A context graph adds how your business works, and remembers it: the enterprise memory an agent needs.
Knowledge Graph vs Graph Databases
A graph database is the storage engine. A knowledge graph is the layer of ontology and meaning on top. Here's how the two differ — and why it matters for AI.
Knowledge Graphs for LLMs
How a knowledge graph grounds an LLM in your business — turning fluent, general-purpose answers into ones rooted in what your data actually means.
Context Graphs for AI Agents
A knowledge graph captures what things mean. A context graph adds how your business works, and remembers it: the enterprise memory an agent needs.
Knowledge Graph vs Graph Databases
A graph database is the storage engine. A knowledge graph is the layer of ontology and meaning on top. Here's how the two differ — and why it matters for AI.
Connect the facts once.
Let every agent traverse them.
