OMNI is the Semantic Core. It sits between your agent and its tools, refining chaotic streams into high-density intelligence. Our goal isn't just to send fewer tokens, but to ensure every token sent is high-signal. We don't just trunced, we distill. Your AI gets the full context, without the fluff. 30% - 90% token efficiency savings while improving reasoning signal. Analyzed and routed cleaner signal, better reasoning, benchmarks prove LLMs perform better with 50 pure tokens than 500 noisy ones.

Framer — Launch websites with enterprise needs at startup speeds.

Launch websites with enterprise needs at startup speeds.

Hi Product Hunt 👋

I am Fajar, i built OMNI after running into a frustrating problem while working with LLMs.

Most tools focus on reducing tokens and they do it well. But in real workflows, I kept losing things that actually mattered. A warning hidden in test output. A small but critical diff. A subtle change in logs that explained everything.

The issue wasn’t too much context.
It was too much noise inside the context.
So instead of building another “token reducer”,
I wanted something that could understand what to keep.

OMNI is a semantic distillation engine:
- Every token is evaluated → keep, compress, or drop
- Based on meaning, not length or rules
- Designed for real dev workflows (logs, tests, diffs)

The goal is simple:
- Give your LLM less noise, without losing signal.
- It’s built in Zig, runs locally, and adds almost no latency.
- Still early, but it’s already been a huge upgrade in my own workflow.

Would love to hear your thoughts, feedback, or even edge cases where this breaks