Building real-time AI for drones, robots, and edge devices has always come with a hidden tax: months spent wrestling with low-level hardware integration before you ever see your model actually run. Cryptic pipeline errors, brittle handoffs between processors, and manual debugging eat up the time that should go into the actual application.

This tech brief introduces Palette Neat, a new open-source framework for the SiMa.ai Modalix chip built to close that gap. Instead of forcing developers to learn the chip's low-level plumbing — memory layouts, data formatting, hardware-specific quirks — it offers a small set of clean, consistent abstractions that let you describe what you want your application to do, not how to wire it together by hand.

The result, according to SiMa.ai: teams moving from model assets to a fully working, deployed Physical AI application in days, and in many cases hours, rather than the months such projects typically require.

Inside, you'll find how the framework rethinks the developer experience around speed, clarity, and debuggability — and why that shift matters for anyone trying to ship real-time, multimodal AI on constrained hardware.

If you've ever lost weeks to integration work before your model even ran, this brief is worth ten minutes of your time.