Brief
The AI Context Stack
Your at-a-glance guide to navigating knowledge graphs, context graphs, ontologies, and semantic layers.
Access the brief
Get instant access – then get to work.
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?
Get the BriefContext 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
Define the problem
AI failed in production. Now what?
Explore the solutions
The 4 types of context infrastructure
Prioritize your needs
Understanding what you're solving for
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
