Contextual AI in Practice

Most enterprise AI today is still built around retrieval alone. And while no one can deny retrieval-based AI delivers results quickly and efficiently, there’s a huge difference between AI that merely retrieves context for your users and AI that understands what that context means to your business. It's because retrieving data isn't the same as understanding it, and more context doesn’t always mean more accurate AI outputs.

That's where business context comes into play.

Context is why AI architectures primarily built around traditional retrieval systems fall short once pilots move into production-critical work. Without context, even the best RAG-based AI will never know, for example, that two of 10 chunks retrieved to answer a query describe the same customer relationship or that last quarter’s policy change means one chunk supersedes the other.

In this architecture guide, we explain:

• The same five walls nearly every at-scale RAG-based AI deployment runs into;
• Why contextual AI needs a different architectural foundation than RAG-based AI stacks; and
• How to own and control your data/models by implementing rich, continuously learning contextual AI across whichever cloud platform and deployment model you prefer.