Labs waste your tokens.
We don’t.

Prompt compression, also called token optimization, context compression, context optimization, or token minimization, cuts the number of tokens in an LLM prompt or context window while preserving its meaning, so the same request uses fewer tokens and costs less.

Compression finds the most token-efficient way to represent your context to the model, keeping every bit of signal and shedding the rest. It is fully deterministic, and nothing is summarized or rewritten, so the text that reaches the model stays verbatim and in original order.

We sit as a middleware layer between your prompt and your LLM. A small ML classifier scores every token in your input and removes the ones least likely to affect the model's output.

Nothing is summarized, paraphrased, or generated. We only ever delete.

That's why we're faster, cheaper, and 100% deterministic, where a small-LLM-based compression step is none of those.

Typically 10–50% while maintaining full accuracy, depending on how dense your input is.

Clean, information-rich text compresses less. Noisy web scrapes, long chat histories, and verbose documents compress more.

You control how aggressive we are with the aggressiveness parameter.

Latency depends on input size, but we're built for real-time use, with p95 at 150ms.

For most workflows the shorter prompt after compression cuts time-to-first-token from the downstream LLM by more than the compression step adds, so end-to-end round-trip goes down with compression in the loop, not up.

No. The models are trained to remove only the parts of your input that are not important: boilerplate, filler, redundant phrasing, structural scaffolding. Important information is never removed.

What changes is the representation, not the meaning. The text that reaches the model is denser and reads differently, but the intention of the original is preserved: the model still sees what you meant.

We never add information either. Nothing is invented, paraphrased, or summarized. Compression only ever takes boilerplate away.

Summarization rewrites your input. It changes wording, introduces hallucinations, and loses structure.

We only delete. The remaining text is verbatim, in the original order, which keeps citations, code, numbers, and JSON intact.

No. Our output is deterministic for a given input and setting, so caches (yours or your LLM provider's) stay valid. Cache management is important thus we've designed the compression models to be cache preserving.

Zero data retention is available on request and can be set at the account level.

We're SOC 2 Type I (audited by Advantage partners, type 2 expected on 09/2026) and HIPAA-ready with a BAA.

Yes. On-premise deployment is available on Enterprise: the compression engine runs inside your own VPC or data center, so raw prompts never leave your network.