Atlan

Founded by Prukalpa Sankar in 2019

Connect enterprise data systems into a shared context layer for AI agents

Cost

Demo

Rating

★ People love it

Time to value

Long Setup (> 1 day)

You can use Atlan to build a unified context layer that connects your data warehouses, BI tools, pipelines, and business systems into one living graph. It automatically documents data assets, generates business definitions, tracks data lineage, and enforces governance policies. AI agents and analysts can then query this shared context instead of each team building their own logic from scratch. It connects to 80+ systems including Snowflake, Databricks, BigQuery, Tableau, and dbt, and exposes certified context via MCP, SQL, and open APIs.

What Atlan does

Connect data warehouses, BI tools, and pipelines through 80+ native connectorsAuto-generate descriptions and business term links for data assets using AI agentsReview, annotate, and certify context through a human approval workflowPublish certified business definitions and lineage to AI agents via MCP or SQLTrack data lineage from source systems through transformations to BI reportsSet and enforce access policies on data assets at the column or table levelCreate and version business glossary terms tied to specific data assetsMonitor AI agent query traces to identify missing or incorrect context and feed improvements backAutomatically scans 80+ enterprise systems to build a living data graph with lineage and business definitionsAI agents auto-generate asset descriptions, metric definitions, and business ontologies before humans reviewHuman-in-the-loop certification workflow where domain experts review and approve context before it goes liveExposes certified context to AI agents via MCP server, SQL interface, and open APIsVersioned context repositories that bundle business logic and definitions for reuse across AI use casesEnd-to-end data lineage tracing every AI answer back to its source data, definition, and certifierIceberg-native context store with vector search and graph architecture built for AI retrievalFeedback loops where evals and traces from AI interactions improve context quality over time