Atlan
(https://atlan.com) 📸 Data Snapshot: June 20, 2026
Identity & Authority — The Lens
Inspect the JSON-LD. Is there an Organization or Person schema, and does it carry sameAs links to real external profiles (LinkedIn, socials)? Missing schema or no identity declaration signals an anonymous entity.
Identity & Authority
Expert verifiability & Schema depth.
15
Impact Weight: 15 / 100
100% Reputation
There are no authority gaps. The founders, Prukalpa Sankar and Varun Banka, are explicitly identified in the JSON-LD schema with verified social profiles. Technical authority is bolstered by naming specific customer experts (e.g., Andrew Reiskind, CDO of Mastercard) and citing leadership positions in four distinct analyst quadrants from Gartner and Forrester, which are high-friction proof points.
The site avoids the disconnect common in AI startups by providing specific benchmarks for its AI outputs. Instead of claiming ‘perfect accuracy,’ they admit to a 75% accuracy wall in 2023 and detail the foundation rebuild required to reach the current 90%+ acceptance rate. This level of transparency regarding technical evolution significantly reduces the bullshit factor.
Identity & Authority is read from the structured data first: whether the site declares who it is in machine-readable schema, with verifiable identity links. Below is the schema captured per page, then the external proof links that support (or fail to support) that identity.
🔗 Identity & Technical Layer — schema JSON-LD: identity chains, entity gaps
Homepage schema
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://atlan.com/#organization",
"name": "Atlan",
"url": "https://atlan.com/",
"logo": {
"@type": "ImageObject",
"@id": "https://atlan.com/#logo",
"url": "https://website-assets.atlan.com/img/atlan-blue.svg",
"width": 180,
"height": 60,
"caption": "Atlan Logo"
},
"slogan": "The missing context layer for enterprise AI.",
"description": "Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.",
"foundingDate": "2019",
"founders": [
{
"@type": "Person",
"@id": "https://atlan.com/#prukalpa-sankar",
"name": "Prukalpa Sankar",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/prukalpa/"
]
},
{
"@type": "Person",
"@id": "https://atlan.com/#varun-banka",
"name": "Varun Banka",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/varunbanka/"
]
}
],
"sameAs": [
"https://www.linkedin.com/company/atlan-hq/",
"https://twitter.com/AtlanHQ",
"https://www.facebook.com/atlanhq/",
"https://www.youtube.com/@atlanHQ",
"https://www.crunchbase.com/organization/atlan-239d",
"https://www.gartner.com/reviews/vendor/atlan",
"https://www.g2.com/products/atlan/reviews"
],
"contactPoint": {
"@type": "ContactPoint",
"contactType": "customer support",
"url": "https://atlan.com/forms/talk-to-sales-contact/"
},
"knowsAbout": [
"Enterprise data governance",
"AI context layer",
"Metadata management",
"Data lineage",
"Context engineering",
"Data catalog",
"AI agents for enterprise data",
"Enterprise data graph",
"Data observability",
"Model Context Protocol (MCP)"
]
},
{
"@type": "WebSite",
"@id": "https://atlan.com/#website",
"name": "Atlan",
"url": "https://atlan.com/",
"description": "The missing context layer for enterprise AI.",
"publisher": {
"@id": "https://atlan.com/#organization"
},
"inLanguage": "en-US"
},
{
"@type": "WebPage",
"@id": "https://atlan.com/#webpage",
"name": "Atlan - The Context Layer for AI",
"description": "The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance to act on trusted data.",
"url": "https://atlan.com/",
"inLanguage": "en-US",
"dateModified": "2026-04-09",
"isAccessibleForFree": true,
"primaryImageOfPage": {
"@type": "ImageObject",
"url": "https://website-assets.atlan.com/img/home-2026/hp-2026-og.png",
"width": 1200,
"height": 630
},
"mainEntity": {
"@id": "https://atlan.com/#organization"
},
"isPartOf": {
"@id": "https://atlan.com/#website"
},
"publisher": {
"@id": "https://atlan.com/#organization"
}
},
{
"@type": "FAQPage",
"@id": "https://atlan.com/#faqpage",
"mainEntity": [
{
"@type": "Question",
"name": "What is Atlan?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "What does Atlan do for enterprise AI?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "What is an enterprise context layer?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "How does the context pipeline work?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "How does Atlan work with AI agents?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "Which enterprise systems does Atlan connect to?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "Who uses Atlan?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "What analyst recognition has Atlan received?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "How does Atlan work alongside my existing data tools?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "How does Atlan approach context engineering?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
},
{
"@type": "Question",
"name": "How do teams get started with Atlan?",
"acceptedAnswer": {
"@type": "Answer",
"text": "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."
}
}
]
}
]
}
/forms/talk-to-sales-contact/
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://atlan.com/#organization",
"name": "Atlan",
"url": "https://atlan.com/",
"logo": {
"@type": "ImageObject",
"@id": "https://atlan.com/#logo",
"url": "https://website-assets.atlan.com/img/atlan-blue.svg",
"width": 180,
"height": 60,
"caption": "Atlan Logo"
},
"slogan": "The missing context layer for enterprise AI.",
"description": "Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.",
"foundingDate": "2019",
"founders": [
{
"@type": "Person",
"@id": "https://atlan.com/#prukalpa-sankar",
"name": "Prukalpa Sankar",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/prukalpa/"
]
},
{
"@type": "Person",
"@id": "https://atlan.com/#varun-banka",
"name": "Varun Banka",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/varunbanka/"
]
}
],
"sameAs": [
"https://www.linkedin.com/company/atlan-hq/",
"https://twitter.com/AtlanHQ",
"https://www.facebook.com/atlanhq/",
"https://www.youtube.com/@atlanHQ",
"https://www.crunchbase.com/organization/atlan-239d",
"https://www.gartner.com/reviews/vendor/atlan",
"https://www.g2.com/products/atlan/reviews"
],
"contactPoint": {
"@type": "ContactPoint",
"contactType": "customer support",
"url": "https://atlan.com/forms/talk-to-sales-contact/"
},
"knowsAbout": [
"Enterprise data governance",
"AI context layer",
"Metadata management",
"Data lineage",
"Context engineering",
"Data catalog",
"AI agents for enterprise data",
"Enterprise data graph",
"Data observability",
"Model Context Protocol (MCP)"
]
},
{
"@type": "WebSite",
"@id": "https://atlan.com/#website",
"name": "Atlan",
"url": "https://atlan.com/",
"description": "The missing context layer for enterprise AI.",
"publisher": {
"@id": "https://atlan.com/#organization"
},
"inLanguage": "en-US"
},
{
"@type": "WebPage",
"@id": "https://atlan.com/forms/talk-to-sales-contact/#webpage",
"name": "Talk To Our Sales Team Contact | Atlan",
"description": "Book a quick conversation with Atlan's team to explore where AI context gaps might be holding your AI initiatives back — and whether Atlan is the right fit for where you are.",
"url": "https://atlan.com/forms/talk-to-sales-contact/",
"inLanguage": "en-US",
"isPartOf": {
"@id": "https://atlan.com/#website"
},
"about": {
"@id": "https://atlan.com/#atlan-app"
},
"mainEntity": {
"@id": "https://atlan.com/#atlan-app"
},
"potentialAction": {
"@type": "CommunicateAction",
"name": "Request a Demo",
"target": "https://atlan.com/forms/talk-to-sales-contact/"
}
}
]
}
/customers/
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://atlan.com/#organization",
"name": "Atlan",
"url": "https://atlan.com/",
"logo": {
"@type": "ImageObject",
"@id": "https://atlan.com/#logo",
"url": "https://website-assets.atlan.com/img/atlan-blue.svg",
"width": 180,
"height": 60,
"caption": "Atlan Logo"
},
"slogan": "The missing context layer for enterprise AI.",
"description": "Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.",
"foundingDate": "2019",
"founders": [
{
"@type": "Person",
"@id": "https://atlan.com/#prukalpa-sankar",
"name": "Prukalpa Sankar",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/prukalpa/"
]
},
{
"@type": "Person",
"@id": "https://atlan.com/#varun-banka",
"name": "Varun Banka",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/varunbanka/"
]
}
],
"sameAs": [
"https://www.linkedin.com/company/atlan-hq/",
"https://twitter.com/AtlanHQ",
"https://www.facebook.com/atlanhq/",
"https://www.youtube.com/@atlanHQ",
"https://www.crunchbase.com/organization/atlan-239d",
"https://www.gartner.com/reviews/vendor/atlan",
"https://www.g2.com/products/atlan/reviews"
],
"contactPoint": {
"@type": "ContactPoint",
"contactType": "customer support",
"url": "https://atlan.com/forms/talk-to-sales-contact/"
},
"knowsAbout": [
"Enterprise data governance",
"AI context layer",
"Metadata management",
"Data lineage",
"Context engineering",
"Data catalog",
"AI agents for enterprise data",
"Enterprise data graph",
"Data observability",
"Model Context Protocol (MCP)"
]
},
{
"@type": "WebSite",
"@id": "https://atlan.com/#website",
"name": "Atlan",
"url": "https://atlan.com/",
"description": "The missing context layer for enterprise AI.",
"publisher": {
"@id": "https://atlan.com/#organization"
},
"inLanguage": "en-US"
},
{
"@type": "WebPage",
"@id": "https://atlan.com/customers/#webpage",
"name": "Customer Stories — Data Leaders Trust Atlan | Atlan",
"description": "Discover how Mastercard, Workday, Virgin Media O2, and hundreds of enterprises use Atlan to govern data, accelerate AI, and build trusted data products at scale.",
"url": "https://atlan.com/customers/",
"inLanguage": "en-US",
"isAccessibleForFree": true,
"primaryImageOfPage": {
"@type": "ImageObject",
"url": "https://website-assets.atlan.com/img/customers/customers-og.png",
"width": 1200,
"height": 630
},
"isPartOf": {
"@id": "https://atlan.com/#website"
},
"publisher": {
"@id": "https://atlan.com/#organization"
}
}
]
}
/context-agents/
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://atlan.com/#organization",
"name": "Atlan",
"url": "https://atlan.com/",
"logo": {
"@type": "ImageObject",
"@id": "https://atlan.com/#logo",
"url": "https://website-assets.atlan.com/img/atlan-blue.svg",
"width": 180,
"height": 60,
"caption": "Atlan Logo"
},
"slogan": "The missing context layer for enterprise AI.",
"description": "Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.",
"foundingDate": "2019",
"founders": [
{
"@type": "Person",
"@id": "https://atlan.com/#prukalpa-sankar",
"name": "Prukalpa Sankar",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/prukalpa/"
]
},
{
"@type": "Person",
"@id": "https://atlan.com/#varun-banka",
"name": "Varun Banka",
"jobTitle": "Co-founder",
"sameAs": [
"https://www.linkedin.com/in/varunbanka/"
]
}
],
"sameAs": [
"https://www.linkedin.com/company/atlan-hq/",
"https://twitter.com/AtlanHQ",
"https://www.facebook.com/atlanhq/",
"https://www.youtube.com/@atlanHQ",
"https://www.crunchbase.com/organization/atlan-239d",
"https://www.gartner.com/reviews/vendor/atlan",
"https://www.g2.com/products/atlan/reviews"
],
"contactPoint": {
"@type": "ContactPoint",
"contactType": "customer support",
"url": "https://atlan.com/forms/talk-to-sales-contact/"
},
"knowsAbout": [
"Enterprise data governance",
"AI context layer",
"Metadata management",
"Data lineage",
"Context engineering",
"Data catalog",
"AI agents for enterprise data",
"Enterprise data graph",
"Data observability",
"Model Context Protocol (MCP)"
]
},
{
"@type": "WebSite",
"@id": "https://atlan.com/#website",
"name": "Atlan",
"url": "https://atlan.com/",
"description": "The missing context layer for enterprise AI.",
"publisher": {
"@id": "https://atlan.com/#organization"
},
"inLanguage": "en-US"
},
{
"@type": "SoftwareApplication",
"@id": "https://atlan.com/context-agents/#softwareapplication",
"name": "Context Studio by Atlan",
"description": "The collaborative workspace where humans and AI build, test, and deploy the shared context enterprise AI needs to answer questions correctly.",
"url": "https://atlan.com/context-agents/",
"applicationCategory": "AI Context Management Platform",
"operatingSystem": "Web-based, Cloud",
"provider": {
"@type": "Organization",
"@id": "https://atlan.com/#organization",
"name": "Atlan",
"url": "https://atlan.com/"
}
}
]
}
🛡️ Trust Signals — external proof links that back the declared identity
119Review mentions (all pages)
4External proof links (all pages)
| Page | Reviews | Proof links |
|---|---|---|
| / (home) | 81 | 1 |
| /forms/talk-to-sales-contact/ | 7 | 1 |
| /customers/ | 22 | 1 |
| /context-agents/ | 9 | 1 |
Explore the other reputation pillars for Atlan
Analysis Disclosure & Source Attribution
This page presents a snapshot of public data from Atlan, captured on June 20, 2026, to show how machine logic reads Identity & Authority signals into an AI reputation evaluation.
Purpose: This data is presented under “Fair Use” for the purpose of independent signal analysis, allowing readers to see the raw signals behind the reputation score.
Notice to Atlan: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results are intended as professional feedback to help improve any website’s machine-readability and authority signals. The evaluation is free, and any company can request a fresh audit at any time.
Any company can use the insights for free and improve its voice. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://atlan.com to view the most current version of its content and see directly what this company is about and what it offers.