Framewise

Framewise cuts robot perception costs by 80-90%+ by never sending the same scene twice. Instead of flooding a VLM with redundant frames, it uses pixel-level and semantic change detection to gate inference - only the changed region gets sent, only when something actually moved. On change frames, a fast draft model (Gemma-27B) answers first; if uncertain, it escalates to Qwen-72B. The result: large-model accuracy where it matters, near-zero cost everywhere else. It implements RAG, reranking, Tavily for more context for robot, streaming frames and measured speculative post draft routing based on certainty (confidence driven).