Intelligent Token Compression for Multi-Modal Agents

Tokens are expensive. You don't need all of them to get the information across — text is full of filler, and video is full of spatial redundancy. At scale, this context bloat turns into millions lost on inference. To solve this, we've built millisecond-scale, accurate token pruning models that drop irrelevant, noisy tokens that clutter your context. Pied Piper works across both text and vision tokens. Our experiments show that we not only save you cost, but can also improve performance on long-context tasks by dropping unnecessary context that gets in the way. The best part? You only pay when you save tokens.