Edge AI Model Optimization Platform

Robotics and IoT companies struggle to run frontier AI models on edge devices with limited compute. General Instinct optimizes large models for on-device deployment, enabling real-time inference without cloud dependency. Target users are robotics companies, manufacturers, and IoT startups.

Edge AI is genuinely heating up right now — NVIDIA's Jetson ecosystem, the rise of purpose-built edge chips from Qualcomm and Apple, and tightening data sovereignty regulations are all pushing compute toward the device. The closest incumbent is Qualcomm's AI Model Efficiency Toolkit and Neural Processing SDK, though neither is purpose-built for the robotics and IoT workflow; there's also real overlap with what Hailo and Axelera offer on the hardware side, which means the optimization layer remains fragmented and contested. The $10k–50k/mo revenue band is plausible for a deep-tech tool targeting companies with hardware BOM costs already in the hundreds of thousands, where inference latency is a genuine engineering constraint and willingness to pay is high — but customer count will be small and sales cycles will be long, so hitting that band requires landing maybe 5–10 serious enterprise contracts, not 500 self-serve signups. The biggest risk is that chip vendors, particularly Qualcomm and NVIDIA, simply bundle better optimization tooling into their own SDKs and commoditize the independent layer entirely, which they have both the incentive and the engineering resources to do.