The problem
A general model knows the world, not your domain.
A general model has read a great deal of the world and very little of your domain. Ask it something specific to your product, your policies, or your customers, and it answers from its general prior — fluent, and not necessarily grounded in what's true for you.
What closes that gap is giving the model your own content to answer from, at the moment it answers.
How it works
Anchor every answer to your own content.
Retrieval grounds an application in your own material: when it answers, it draws on the content that's true for your domain, so responses are anchored rather than improvised. As that source material changes, the application reflects it — no model retraining required.
Because retrieval runs through the same runtime as memory, routing, and prompts — and its results are cached — grounding stays fast as usage grows, and it's one substrate to build on rather than another system to stitch in.
- The application draws on your own content when it answers
- Answers are anchored to what's true for your domain, not just the model's general prior
- New and changing source material is reflected without retraining a model
- Grounding runs through the same runtime as memory, routing, and prompts
- Retrieval results are cached, so grounding stays fast under load
Answers grounded in what's true for you.
AI Infra is one of the accelerators our Services team builds on. Tell us what you're building and we'll stand it up on a runtime that's ready for production.