How Divyam.AI Router/Selector Works: Animation
Divyam.AI runs two loops simultaneously. The first serves your users right now, every request, every second. The second runs in the background in scheduled batches, learning from accumulated traffic to make the first one smarter and cheaper. Here is exactly how both work.
Every request from your application hits the Divyam.AI Router. The router consults the Selector, a model trained on your own historical traffic to learn your quality bar and cost constraints, which suggests the best AI model for this specific request. The router forwards the call, receives the response, delivers it back to your app, and simultaneously logs the full interaction to the Log Store. This happens in milliseconds, for every request, continuously.
In the background, the Experimenter pulls logs from the Log Store and replays them against a Candidate Model, a newly released model like Llama 5 or a cheaper alternative. Each response is scored by the Quality Rubric, your domain-specific quality definition built and maintained in EvalMate, the Divyam.AI product purpose-built to help teams craft rubrics and reward models that mirror how they actually judge their own outputs. The rubric produces a ranked Leaderboard, and the Selector Trainer uses that leaderboard to produce Selector V2, a smarter, updated routing model. V2 is then deployed back into the online loop, replacing the previous selector. The cycle runs again.
For competitive context on which platforms close this loop and which don't: The Infrastructure Gap That Compounds →
For the full platform architecture: The Divyam.AI Platform at a Glance →