Semantic Coherence: Atlan – Signal Evidence & AI Readability

The Context Layer for AI
[H1] 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.Book a DemoSee How it Works
[H2] Trusted by AI-forward enterprises
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See All Customer StoriesThe Observation
[H2] 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 InsightWhen 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 DosSantosVP, Enterprise Data & Analytics
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The AI Context Gap
[H2] 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 RaisesContext LayerAnswer It NeedsUser ContextWho's asking — and what decision?CS team or Sales team?CS team optimizes for renewal riskKnowledge ContextWhat does "customer" mean here?Account or individual?Parent account, not individual locationMeaning ContextHow do you define "top"?Revenue, orders, or margin?Top = highest net ACV, not order countData ContextWhich tables hold net ACV?CRM vs. billing?Use billing.subscriptions joined with crm.accountsData ContextHow do you calculate revenue?Gross or net of discounts?Revenue net of discounts and refundsWhy Customers Love Atlan
[H2] The only proven way to create context
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The Context Pipeline
[H2] 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.
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UNIFY
[H3] 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.CatalogGovernanceLineageQualityGlossary“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 PanjaManaging Director, Cloud & Data Engineering
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BOOTSTRAP
[H3] 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 GeneratorTerm LinkageMetrics GeneratorSemantic ViewsOntology 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 UekiHead of Enterprise Data & Analytics
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COLLABORATE
[H3] 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 ResolutionAnnotationLabellingCertificationFeedback 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 AdameEnterprise Data Governance Leader
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ACTIVATE
[H3] 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 ServerSQLAPIsSDKEvals & 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 DosSantosVP, Enterprise Data & Analytics
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Industry Recognition
[H2] A leader across every context category
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[H3] 95% of G2 users seeAtlan as a true partner
Read the G2 report
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“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 SolutionsRead the Gartner MQ report
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“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 GovernanceRead the Gartner D&A report
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[H3] A Leader and a Customer Favourite in the Forrester Wave™
Data & Analytics Governance Solutions and Enterprise Data CatalogsWhat We Believe
[H2] 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.
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[H3] 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.
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[H3] 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.
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[H3] 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.FAQ
[H2] Frequently asked questions
What is Atlan?Atlan is the context layer for enterprise AI. It sits between your business systems and your AI agents, connecting lineage from data pipelines, business definitions from BI tools and SQL logic, knowledge from SOPs, quality scores, and access policies into a unified context store. Every agent and analyst queries that context store directly — no manual context-building per use case. Gartner named Atlan a Leader in the 2025 Metadata Management and 2026 Data and Analytics Governance Magic Quadrants. Forrester did the same in its 2024 Enterprise Data Catalogs and 2025 Data Governance Solutions Waves. The only platform recognized across all four.What does Atlan do for enterprise AI?Atlan gives every AI agent the enterprise context it needs: the business definitions behind column names, the lineage behind every output, and the access policies behind every query. Without this, agents hallucinate, misclassify sensitive records, or return answers compliance teams reject. Every AI output is traceable — every answer points back to the data, the definition, and who certified it.What is an enterprise context layer?An enterprise context layer sits between your business systems and your AI stack. It unifies context from across the business — lineage, semantic definitions, SOPs, access controls, usage patterns — into a single graph that agents and analysts query in real time. Without one, every new agent deployment starts with months of manual context-building. With one, every new agent inherits the organization's full institutional memory on day one.How does the context pipeline work?Four stages: unify, enrich, certify, activate. Atlan unifies metadata from native connectors — data warehouses, BI tools, pipeline orchestrators like dbt and Airflow. Context Agents auto-generate descriptions, metrics, and business ontology across the full data graph. Human experts review and certify — human-on-the-loop, not out of the loop. Certified context activates to every agent and tool via MCP, SQL, and open APIs. Evals and traces feed back in with each cycle, so context quality compounds over time.How does Atlan work with AI agents?AI agents get enterprise context through Atlan's MCP server, SQL interface, and open APIs. A query returns the data graph, business definitions, lineage, and access policies for that specific task. Context repos version and package this knowledge, so every new agent starts with the organization's full institutional memory instead of a blank slate. No context hardcoded per use case. No starting over.Which enterprise systems does Atlan connect to?Atlan connects natively to 80+ enterprise systems: Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI, and Postgres, among others. Once connected, lineage, query history, BI semantics, tags, and quality signals flow in automatically through scheduled and event-based workflows — no manual mapping required. Atlan also layers on top of existing catalogs like Microsoft Purview and Snowflake Horizon, pulling their metadata into a unified context layer.Who uses Atlan?Atlan is deployed at enterprises including General Motors, Workday, Nasdaq, Mastercard, and Virgin Media O2. AI leaders use it to give agents governed access to enterprise context. Data engineers automate lineage and discovery. Governance teams enforce policies at the asset level. AI platform teams build and deploy agents faster because business logic is already in the context layer — not scattered across prompt files and wikis.What analyst recognition has Atlan received?Atlan is the only platform named a Leader in all four major analyst evaluations for metadata and data governance: Gartner's 2025 Metadata Management Magic Quadrant, Gartner's 2026 Data and Analytics Governance Magic Quadrant, Forrester's 2024 Enterprise Data Catalogs Wave, and Forrester's 2025 Data Governance Solutions Wave. No other platform has been recognized across all four.How does Atlan work alongside my existing data tools?Atlan layers on top of your existing data stack. Many enterprises run Atlan alongside Microsoft Purview or Snowflake Horizon or Databricks Unity Catalog — pulling metadata from all into a unified context layer rather than rebuilding from scratch. Built on open APIs and Iceberg-native formats, context stored in Atlan stays portable: it is not locked to any vendor's proprietary schema. Switch AI frameworks, add new systems, or consolidate tools — the context layer moves with you.How does Atlan approach context engineering?Context engineering is the practice of selecting, structuring, and delivering the specific knowledge an AI agent needs at each step of a task. Most teams do this manually for each agent — months of work, duplicated across every use case. Atlan automates it: context from 80+ systems is unified, Context Agents auto-generate descriptions, metrics, and ontology across the full data graph, human experts certify, and certified context activates via MCP, SQL, and APIs. In April 2026, Context Agents generated 690K+ descriptions across 50+ enterprise customers — 87% rated on par or better than human writing. Every eval and trace feeds back in. Context quality compounds with each cycle.How do teams get started with Atlan?Start with a Context Workshop: Atlan's team maps your data and AI architecture, designs a context layer for a priority use case, and sets a measurable baseline. From there, a four-week Context Sprint delivers a working agent and accuracy results you can compare directly against your current approach. Most teams see the first value in weeks.
[H2] Bridge the context gap.Ship AI that works.
Book a DemoSee How it Works
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[H1] An honest conversation about your AI context gap.
We'll discussThe challenges you’re facing and where we typically see teams struggle with AIWhy context is complicated and what it takes to build a context layerA quick preview into how Atlan can solve your AI context gapTrusted by the world's leading AI teams
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[H2] Start the conversation

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[H1] The most trusted enterprise context layer
Andrew ReiskindChief Data Officer
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NOW PLAYINGHow Mastercard Designs Context on Atlan
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PLAY NEXTHow VMO2 Powers Analytics Agents with Atlan
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PLAY NEXTHow Workday Connects Context on Atlan
[H2] Making AI useful for hundreds of leading businesses
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[H2] See how the world's best teams use Atlan
[H2] Explore Customer Stories
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Case Study
[H3] How Dropbox Built a Context Layer for Federated Data Ownership
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Case Study
[H3] How Fox Unified Context Across Every Part of Their Tech Stack
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Case Study
[H3] How General Motors Uses Atlan For Transparent AI
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[H3] How Elastic Scales Context Across Their Data Estate with Atlan
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[H3] How Porto is Building an Encyclopedia of Context with Atlan
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Case Study
[H3] How Autodesk Powers a Context Layer Across Their Data Mesh on Snowflake
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[H3] How Postman Found the Missing Context Layer in Their Data Stack
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[H3] How Nasdaq Built a Context Layer to Drive Their Enterprise Data Strategy
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Webinar
[H3] How Dropbox Scales Context with Federated Ownership and Trusted Data Products
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Case Study
[H3] How Dr. Martens Built Context for Global Data Transparency with Atlan
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[H3] How Zip Made Context a Company-Wide Practice
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[H3] Why a $3 Billion Healthcare Provider Chose Atlan as Their Context Layer
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[H3] How Porto is Building an Enterprise Context Layer for Their Data Platform
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[H3] How Aliaxis Created a Shared Context Layer for a Worldwide Team
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Case Study
[H3] How Tide Embedded Privacy Context into Automated Data Processes
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[H2] Join hundreds who areshipping AI that's useful, with context.
Book a DemoSee Product Tour
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Context Agents
[H1] The team that makes your data AI-ready.
Documentation has been an unsolved problem for years, and AI needs more documentation than humans can write. Context Agents are the AI teammates that write, maintain, and continuously evolve the documentation your team never did.See How it WorksBook a Demo
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SCOUTRanks assets by what your team actually queries.SUPERPOWERS?Query Analysis⚡Usage Signals?️Asset Ranking
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WORKS BEST WITHScribe to prioritize what gets described first
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ScoutUsage Intelligence
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ScribeDescription Writer
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LexisGlossary Builder
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DocReadme Author
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NexusTerms Linker
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SageMetric Arbiter
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AtlasDomain Classifier
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VeraQuality Scorer
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OrionOntologistTHE JOURNEY
[H2] Data catalogs were built for humans... who never documented them.
The First Copilot
[H3] In 2023, we launched the first AI documentation agent.
We called it Atlan AI. It could write descriptions automatically, but accuracy was at 75%. Good enough to show the vision, but not good enough to replace human work.We Hit a Wall
[H3] We realized AI accuracy at scale needed a rebuild.
To be accurate, AI needed to access rich signals like lineage, query history, usage patterns, relationships between assets. Atlan stored all of that, but AI couldn't use it. So we rebuilt the foundation: the Context Lakehouse.The New Reality
[H3] Today, context agents outperform humans on quality.
Customers are telling us the agent-written descriptions are more accurate and more complete than what their teams were producing manually.Acceptance Rate Today90%+AI Descriptions Applied350K+
[H3] Start your AI-readiness sprint.
Learn how Context Agents can get you to AI readiness in 30 days.Book a Strategy SessionHOW IT WORKS
[H2] The teammates that solve the biggest blocker to context: documentation.
Stage 1: FoundationalStage 2: DerivedStage 3: Compounded
[H3] Agents that read raw metadata to build foundational context.
Stage 1 · FoundationalTask Plan①Scan SQL query historyacross all teams and use cases②Identify top-queried assetsby team, frequency, and function③Rank by usage scoreand assign enrichment priorityRevenue AssetsProduct AnalyticsCustomer Datafinance.revenue_table847 queriesGold Layer ↑finance.arr_cohort693 queriesbilling.invoices541 queriesfinance.mrr_breakdown418 queriesfinance.ltv_by_segment263 queriesScout — Usage & Query IntelligenceFinds the most important assets needing context. Analyzes query history and access patterns so enrichment targets the assets people actually use.Scribe — Description AgentWrites descriptions that hold up under scrutiny. Reads SQL usage patterns, column names, and lineage signals to generate accurate descriptions for every table and column.Lexis — Glossary BootstrappingYour business glossary has been "coming soon" for years. Lexis builds it from existing definitions, column naming conventions, and domain patterns.
[H3] Agents that synthesize foundational context into structured business knowledge.
Stage 2 · DerivedREADME generatedfinance.arr_cohort · README.mdauto-generatedARR CohortOverviewTracks annual recurring revenue cohorts by customer segment and contract start date. Lineage confirmed: upstream from billing.subscriptions, downstream to exec.revenue_dashboard.Source Tablesbilling.subscriptionscrm.accountsKey Columnscohort_montharr_usdsegment_tierUsagePrimary consumer: Revenue Analytics team. Queried 284 times in the last 30 days.Doc — Readme AgentTurns scattered signals into documentation your team will use. Takes descriptions, usage signals, and lineage and turns them into comprehensive dataset documentation.Nexus — Terms & Metrics LinkageCloses the gap between code and conversation. Bridges technical column names and the business terms your analysts actually use.Sage — Metric ConflictsFinds where two teams define the same metric differently — and locks in one answer. Surfaces conflicts, routes to team stewards, and updates definitions once approved.
[H3] Agents that build advanced, enterprise-grade intelligence.
Stage 3 · CompoundedPurposeTags every asset with its business domain — automatically, at scale.Task Plan1Read Scribe descriptions and usage signals2Match asset metadata against domain patterns3Score domain fit for each asset4Apply domain tag or route to stewardTagging assetsorders.revenue_tableFinancerevenue_usdorder_idbillingarr_q4product_events.sessionsProductsession_idfeature_useduser_idengagementmarketing.campaignsMarketingcampaign_idspend_usdimpressionschannelfinance.arr_cohortFinancearr_usdcohort_monthcustomer_tierchurneng.deploy_logsEngineeringdeploy_idenvpipelineshastatusAtlas — Domain TaggingMaps every asset to its place in the world. Classifies every asset into the right business domain so AI agents know how to find the right context based on the user.Vera — Data QualitySurfaces which assets your AI can and can't trust. Automatically scores your critical assets on completeness, accuracy, and freshness.Orion — OntologistMaps what every term means in every context. Maps every relationship between domains, terms, and assets, so when an agent asks what "revenue" means, it gets the right answer for the right context.ROLLOUT
[H2] Rollout in 30 days, not 12 months.
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[H3] Start With What Matters
Most of your catalog nobody touches. Context Agents identify your Gold Layer, Popular BI, Popular SQL, and upstream dependencies first — enriching the assets people actually use before spending cycles on the long tail. Value shows up in days, not months.
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[H3] AI Scores Every Output
Each agent output carries a composite confidence score across accuracy, clarity, style, and completeness. High-confidence outputs auto-apply. Lower-confidence outputs route to humans.
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[H3] Humans Decide & Govern.
AI generates descriptions, classifies assets, builds metrics, and scores quality at scale. Stewards shift from documentation to certification — sampling, validating, and resolving the cases that require judgment. One click. Not 847 manual reviews.INDUSTRY RECOGNITION
[H2] The future of context, validated by Forrester and Gartner
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"Atlan's solution focuses on automation, allowing every action to be performed programmatically via APIs and calling via an LLM. Its core components also include a knowledge graph for business domains and vector storage & analytics, which is purpose-built for AI."Leader in the 2025 Gartner® Magic Quadrant™ for Metadata Management SolutionsRead the report
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"Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. The underlying metadata lakehouse architecture boosts performance, scalability, extensibility and time travel auditability."Leader in the 2026 Gartner® Magic Quadrant™ for Data & Analytics GovernanceRead the report
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"Atlan offers features that are among the best in class for policy management, stewardship, and collaborative governance. Its knowledge graph and AI-powered automation support clear data ownership, surfacing policy-relevant context and automating governance workflows."Leader in The Forrester Wave™: Data Governance Solutions, Q3 2025*Read the reportItem 1 of 3Slide 1 of 3
[H2] Learn more about context agents and the context layer.
[IMG: Why your AI agents hallucinate on real data]
[H3] Why your AI agents hallucinate on real data
53+ resources on the enterprise context layer — the missing infrastructure between your data platform and your AI agents. What it is, how to implement it, and why teams that have it are 5x more likely to reach production.Explore 53+ resources→
[IMG: 84% invest in AI. 17% reach production. Here]
[H3] 84% invest in AI. 17% reach production. Here's the gap.
550+ data leaders told us what's changing — and what separates the teams that scale from the ones that stall. The 7 structural shifts forcing data teams to rebuild for an AI-first world.Read the report→
[IMG: Why your AI agents hallucinate on real data]
[H3] Why your AI agents hallucinate on real data
53+ resources on the enterprise context layer — the missing infrastructure between your data platform and your AI agents. What it is, how to implement it, and why teams that have it are 5x more likely to reach production.Explore 53+ resources→
[IMG: 84% invest in AI. 17% reach production. Here]
[H3] 84% invest in AI. 17% reach production. Here's the gap.
550+ data leaders told us what's changing — and what separates the teams that scale from the ones that stall. The 7 structural shifts forcing data teams to rebuild for an AI-first world.Read the report→
[H2] Leave metadata management behind.Compound context with agents.
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