Blog
Why Natural Language Analytics Is Not a Technology Project
Natural language analytics succeeds or fails on readiness, not models. The four areas leaders invest in: data, context, AI, and the often-missed organization.
•Piet Loubser
Resources
Explore blogs, demos, datasheets, whitepapers, and more to learn how Codd AI transforms your data into insights
Blog
What Is a Semantic Layer? A Complete Guide for AI and BI
What is a semantic layer? A practical guide to how semantic layers work, the main types, how they compare to related tools, and why AI analytics needs one.
•Ravindra Punuru
Blog
Why Runtime Context Is the Wrong Foundation for Enterprise AI Analytics
Most conversational analytics builds context at runtime, after the question is asked. Why that doesn't scale, and the case for a governed semantic layer.
•Piet Loubser
Blog
Why Most AI Analytics Projects Quietly Stall
Most enterprise AI analytics projects never reach real decisions. The cause is rarely the model. It is missing business context and the trust lost without it.
•Piet Loubser
Blog
Why Conversational Analytics Is Replacing Dashboards (And Why Most AI Still Fails)
Conversational analytics is replacing dashboards, but ease of use isn't the story. It's the translation problem BI never solved, and why most AI still fails.
•Piet Loubser
Blog
Your Analytics Team Is Already Drawing a Knowledge Graph on a Whiteboard
When knowledge graphs earn a place in your AI stack, when they don't, and seven analytics questions that tell you which. A plain-English guide, no RDF required.
•Ravindra Punuru
Blog
You Don't Need Another AI Tool. You Need to Know When Context Matters
Do you need a knowledge graph or just better context? A framework to sort low-context from high-context AI use cases and see where a context layer pays off.
•Piet Loubser
Blog
The Rise of the AI Control Plane for Analytics: Why Context Is the New Infrastructure
Every analytics tool now has its own AI copilot. Learn why an AI control plane built on a contextual semantic layer is becoming core enterprise infrastructure.
•Piet Loubser
Blog
What Ontology Actually Means for Analytics
Why the data industry argues about the wrong thing, and a practical ontology for analytics: a middle path between BI semantic layers and formal OWL ontologies.
•Ravindra Punuru