The Token Company

Teams building LLM applications pay for every token sent to a model, including filler, boilerplate, redundant phrasing, and structural scaffolding that does not add meaning. The page frames this directly: 'every filler token in your context is revenue for a lab.' Long-running agents and long prompts (like Helonic's construction drawing analysis) accumulate large token counts, driving up API costs and run time. Existing alternatives like summarization rewrite input, which changes wording, can introduce hallucinations, and loses structure like citations, code, numbers, and JSON.

The Token Company sits as a middleware layer between a prompt and the LLM, using a proprietary ML classifier (bear-2) that scores every token and deletes the ones least likely to affect the model's output, without summarizing, paraphrasing, or generating new text. Integration is a one-line wrap of an existing OpenAI or Anthropic client via a Python package, so existing code stays the same while compression happens automatically. The system claims typical compression of 10-50% depending on input density, with an adjustable aggressiveness parameter, sub-50ms to p95 150ms inference latency, full determinism, and cache-safety so provider or self-managed prompt caches remain valid. It also offers compliance-oriented options: SOC 2 Type I attestation (Type II expected September 2026), HIPAA compliance with a signed BAA, optional zero data retention, and on-premise or VPC deployment for enterprise customers.