The Token Company provides a deterministic prompt-compression middleware that reduces LLM context sizes by 10–50% before requests reach models like GPT, Claude, or Gemini. Its lightweight Python and Node.js SDKs wrap existing OpenAI or Anthropic clients, preserving verbatim text while removing low-signal tokens to cut costs and improve inference speed. The platform is SOC 2 Type I certified, HIPAA-compliant, and offers optional zero data retention for regulated industries.
Funding
Funding not disclosed
Founders
Product
Problem
Large language model prompts often contain redundant or low-information tokens—such as boilerplate, filler phrases, or verbose chat histories—that inflate context windows. This bloating increases API costs, slows time-to-first-token, and can degrade accuracy in long-context tasks, especially for enterprises processing high volumes of requests.
Solution
The Token Company offers a compression middleware layer that sits between a user's application and any major LLM provider, including OpenAI, Anthropic, and Gemini. A small ML classifier scores every token in the input and deletes those least likely to affect the model's output, achieving 10–50% token reduction while preserving full accuracy. The process is fully deterministic—nothing is summarized, paraphrased, or generated—so the remaining text stays verbatim and in original order, keeping citations, code, and JSON intact. Developers integrate the service by wrapping their existing client with a one-line change, and compressed prompts flow automatically to the downstream model. The platform is built for real-time use with p95 latency at 150ms, and it offers configurable aggressiveness levels to balance compression ratio against information density.
Target Audience
Primary customers are engineering teams and enterprises running high-volume LLM workloads on OpenAI, Anthropic, or Gemini who need to cut API costs and improve inference speed, particularly those in regulated industries like healthcare and finance requiring SOC 2 or HIPAA compliance.
Features
- Deterministic token deletion via a small ML classifier that scores and removes low-signal tokens without summarization or generation
- One-line SDK integration for Python and Node.js that wraps existing OpenAI, Anthropic, or OpenRouter clients
- Configurable aggressiveness parameter to control compression ratio, with typical savings of 10–50% depending on input density
- Real-time performance with p95 latency of 150ms, reducing end-to-end round-trip time by shortening downstream time-to-first-token
- SOC 2 Type I certification, HIPAA compliance with BAA signing, and optional zero data retention at the account level
- On-premise deployment option for running in a customer's own VPC or data center
- Protect Text feature to preserve specific sensitive or structured content from compression
- Compression statistics dashboard providing tokens saved and compression ratio per request