LLM Routing Platform

The challenge

An AI product company was spending unpredictably on frontier models for every request — including simple ones a small open-source model could handle. Latency was inconsistent and quality was hard to track.

Our approach

1. 1Built a task classifier that routes requests across 14 models based on task type, length, and quality target.
2. 2Implemented response caching, speculative decoding, and request batching for hot paths.
3. 3Shipped a quality evaluator that compares router outputs against frontier baselines daily.
4. 4Exposed a single OpenAI-compatible endpoint so product teams could adopt with zero changes.