---
title: "The AI Context Stack | Atlan"
url: "https://atlan.com/resources/ai-context-stack/"
description: "This two-page overview breaks down the differences between context graphs, knowledge graphs, ontologies, and semantic layers - and which is right for you."
keywords: "atlan, context layer, context for AI, context graph"
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

**The AI Context Stack** (brief, two pages) is your at-a-glance guide to navigating knowledge graphs, context graphs, ontologies, and semantic layers, and which is right for you. Access is free behind a short form: [get the brief at https://atlan.com/resources/ai-context-stack/](https://atlan.com/resources/ai-context-stack/).

## Everyone has an opinion about context infrastructure

The question is - what's right for you?

## The problem: the AI context stack isn't one-size-fits-all

AI is turning your data architecture inside out. Meanwhile, the board is asking where the value is. You need to do something fast - but don't know where to start.

Context is the answer to production-ready AI, but how to wrangle it remains unclear. Knowledge graphs, context graphs, ontologies, semantic layers - which is right for you?

## The solution: our approach

1. **Define the problem** - AI failed in production. Now what?
2. **Explore the solutions** - the 4 types of context infrastructure.
3. **Prioritize your needs** - understanding what you're solving for.
4. **Make a plan** - get started based on your AI use cases.

## What to expect

Who should read this:

- **CDOs & VPs of Data** - prioritize investments to accelerate your AI roadmap.
- **AI/ML Product Leaders** - identify the context infrastructure needed to fix AI issues.
- **Data Platform Architects** - map against your current stack to pinpoint context gaps.

You'll get:

- The 4 types of context infrastructure
- The relationships across all 4 types
- How each component differs and interacts
- How to think about your own infrastructure
- Next steps based on your AI use cases

## Related resources

- [The CIO's Guide to Context Graphs](https://atlan.com/resources/cio-guide-to-context-graphs/) (ebook) - everything you need to know about context layers and graphs, including implementation.
- [How to Build an AI-Ready Semantic Layer](https://atlan.com/know/regovern-building-semantic-layer-guide/) (blog) - a step-by-step guide to building a unified, machine-readable semantic layer for AI.
- [Ultimate Guide to Evaluating a Data Catalog](https://atlan.com/resources/ultimate-guide-evaluating-data-catalog/) (guide) - the 5 steps to follow to find the right data catalog for your organization.
- [Context Is the New IP - But Who Will Own It?](https://atlan.com/know/gdd-who-will-own-ai-context/) (blog) - what leaders from Snowflake, Glean, and more had to say about who owns context for AI.
- [Metadata: The Critical Foundation for Conversational AI](https://atlan.com/resources/metadata-research-report/) (report) - across 522 test queries, enriched metadata improved accuracy by 38%.
- [How to Experiment with Conversational Analytics](https://atlan.com/know/regovern-conversational-analytics-guide/) (blog) - use the guide to create a blueprint before you start a conversational analytics experiment.