Smaller prompts.
Same answers.
Bloat out. Meaning in.
Inline token reduction that preserves intent. Runs in single-digit milliseconds, on the parts of your prompt that don't deserve to be there.
Fidelity, measured. Not promised.
Every compression is scored against the uncompressed baseline. You set the floor, we hold the line — and skip the prompts we can't shrink safely.
Every trade-off, on a dial.
See savings, fidelity scores, and pass-through rates per request, per model, per customer. Move the dial between max savings and max fidelity — see the curve before you commit.
From bloat to bottom line,
in four steps.
Just a layer between you and the model.
Hard questions.
Honest answers.
No. Every compression is scored against the uncompressed baseline before it ships, and prompts we can't shrink safely pass through untouched.
Caching reuses identical prefixes when they match. We reduce tokens on the parts caching can't touch — they're complementary, not the same lever.
Then you'll have a faster button. We'll still have what's hard to ship in a button: per-customer tuning, fidelity monitoring, and one dial across every provider you use.
Inline compression runs in single-digit milliseconds on typical prompts. If a prompt is too small or too sensitive to compress safely, it passes through with zero added latency.
Anthropic, OpenAI, Google, Mistral, and the major open-source families. If it speaks the OpenAI-compatible protocol, we speak to it.
Sampled shadow runs against the uncompressed prompt, plus evaluators you define for your task. You decide what “correct” means; we instrument the rest.
Prompts are processed in transit, encrypted, and not stored unless you opt into logging. No prompt content is ever used to train external models.
A fraction of the tokens you save. If we don't move the needle, you don't pay — that's the whole pricing model.