Gargi Reflex: Autonomous Model Caching for System One Decisions

You write the prompt. Reflex learns from your LLM's answers and serves the repeat calls locally, in milliseconds.

You do the prompt design. Reflex does the data science.

Watch the film ↓

One minute, from 3.6 s to 5 ms. #

What Reflex does #

1. 01### It watches.Your typed LLM calls (routing, intent, moderation, extraction) go to your model exactly as today. Reflex logs each input and answer.
2. 02### It proves.It trains a small model on CPU in minutes, then tests it on data it never saw. Unless agreement, calibration and coverage all pass, nothing is swapped.
3. 03### It swaps, and keeps checking.Confident calls are served locally in about 5 ms at $0. Everything else, plus a permanent holdout slice, still goes to your LLM, which takes back over if agreement drops.

Built for Python teams whose product asks an LLM the same kind of question thousands of times a day.

The evidence #

[Read the research →](https://www.gargi.io/reflex/research)

If anything in Gargi fails (no model, low confidence, a model that won’t load, a corrupt database, a bug), the call goes to your function, exactly once. Your exceptions propagate unchanged.