Connect all your business systems and pull context across your data estate into one living graph.
Give humans the context they need to understand your business.
Slack
Teams
Claude
ChatGPT
AI teammates that document tacit knowledge and make your data AI-ready.
Bootstrap, test, and ship the business understanding every AI needs.
Cortex
GenieClaude
Codex
The world's first context store engineered natively for AI.
Finance
Technology
Manufacturing
Media
Healthcare
Retail
We're writing down everything we learn. 1292+ articles, how-to guides, and resources on data governance, context engineering, enterprise AI and more
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LLMOps platforms compared: LangSmith, W&B Weave, MLflow, Arize, Helicone, Portkey, Braintrust, Langfuse. Plus the governed context layer your stack is missing.
May 20, 2026
Compare 9 semantic layer tools for BI metric consistency and AI agent governance in 2026: dbt, Cube, AtScale, Atlan, Snowflake, Databricks, and more.
May 20, 2026
9 chunking strategies for RAG: from fixed-size to agentic and metadata-enriched. When to use each, their trade-offs, and what drives retrieval accuracy in production.
May 20, 2026
How to standardize AI tooling across business units: from auditing your AI landscape to activating governed context across every team, agent, and copilot.
May 20, 2026
MCP vs function calling: the core differences, when to use each, how they compose in production, and why governed context behind the MCP endpoint matters.
May 20, 2026
Top vector databases for enterprise AI in 2026: Pinecone, Weaviate, Qdrant, Milvus, pgvector, and more. Pros, cons, and the governance layer every team needs.
May 20, 2026
Vector store vs. graph database for agent memory: retrieval patterns, multi-hop reasoning, governance gaps, and the enterprise architecture that combines both.
May 20, 2026
Enterprise skills make agents reliable specialists, but the spec only governs the procedure. Learn the two-layer governance model and why context decides truth.
May 19, 2026
Active ontology keeps business meaning bound to live data, lineage, and governance signals. See the architecture, use cases, and how to evaluate it.
May 19, 2026
Enterprise memory is the governed substrate that AI agents read from across users and sessions. Learn how it works, why it matters, and how to evaluate it.
May 19, 2026![12 Advanced RAG Techniques Beyond Naive Retrieval [2026]](https://atlan.com/og/know-advanced-rag-techniques.png)
Advanced RAG techniques like hybrid retrieval, Self-RAG, RAPTOR, and CRAG fix the gaps naive retrieval leaves behind. 12 techniques with benchmarks and ratings.
May 18, 2026
Dynamic context delivers live knowledge at inference time; static context is a fixed snapshot. Learn how the difference shapes AI agent accuracy and trust.
May 18, 2026
Hybrid RAG combines dense vector search and sparse BM25 retrieval to improve NDCG by 26–31% over dense-only systems. Learn how it works and when to use it.
May 18, 2026
Compare LiteLLM vs Portkey vs AWS Bedrock Gateway: routing, caching, compliance, and pricing. Which LLM gateway fits your enterprise AI stack?
May 18, 2026
LLM cost management for enterprise is a governance problem. Covers evaluation frameworks, vendor scoring, and attribution for systematic cost control.
May 18, 2026
LLMOps vs MLOps compared across artifacts, cost, evaluation, and governance. See why the governance gap is the critical difference for enterprise AI in 2026.
May 18, 2026
Manage multiple LLM providers at scale with a governance-first framework: data estate mapping, cost attribution, provider registry, and gateway deployment.
May 18, 2026
Eighty percent of enterprise RAG projects fail. Learn the four layers where RAG accuracy breaks down and how to fix each, starting with data governance.
May 18, 2026
500+ sessions. One week. Here's what's actually worth your time at Snowflake Summit 2026 — curated picks for data and AI leaders, by Atlan's Austin Kronz.
May 18, 2026
LLMOps governs large language models in production. Learn 7 core components, implementation steps, and why most enterprise AI failures are governance failures.
May 18, 2026![What Is MCP (Model Context Protocol)? A Complete Guide [2026]](https://atlan.com/og/know-what-is-model-context-protocol.png)
MCP is Anthropic's open standard for connecting AI agents to external systems. Learn the architecture, three primitives, and why governance matters.
May 18, 2026
The perceive-reason-act loop, four memory types, and the external context layer that standard AI agent architecture diagrams leave out — explained.
May 15, 2026
How AI agent planning works, why context quality determines plan accuracy, and when humans need to stay in the review loop before agents act.
May 15, 2026
Five context failures explain why AI agents stall between POC and production — and what each failure requires to fix before your agents can scale.
May 15, 2026
