SiMa.ai, a leader in Physical AI, has launched Palette Neat™, described as the industry’s first agentic development environment for Physical AI, designed to reduce complex application timelines from months to days.

The open-source, purpose-built integrated development environment combines a Physical AI execution library with an agent workflow layer for productivity-focused agentic development. When paired with the full-production Modalix™ MLSoC™ System-on-Module (SoM), or its new PCIe companion card form factor, the unified platform delivers performance-per-watt for high-demand Physical AI workloads across robotics, automotive, drones, industrial automation, aerospace and defence, smart vision and healthcare.

“SiMa.ai is an AI software company that builds its own silicon,” said Krishna Rangasayee, Founder and CEO of SiMa.ai. “Today, we are delivering the industry’s first agentic development environment for Physical AI. Together, Palette Neat and our pin-compatible SoM dismantle the incumbent GPU moat, allowing developers to design systems in plain English and develop them in days — and in many cases, hours.”

Palette Neat uses a natural-language interface and an agentic workflow to abstract low-level compute complexity. This reduces the months of work traditionally required to port and integrate applications to new silicon.

Key advantages of Palette Neat include a new development paradigm: Developers can use natural-language commands to build entire systems, enabling engineering teams to focus on system-level differentiation for both new and legacy applications.

From months to days or hours: The agentic environment autonomously builds and maps applications directly to silicon, shrinking development cycles significantly. Developers can also reuse existing application code, preserving approximately 90 per cent of their legacy software investment.

Frictionless platform migration: Palette Neat, along with the pin-compatible SoM and new PCIe companion card, is designed to dismantle the incumbent GPU moat while reducing the cost, time and engineering risk involved in switching hardware platforms.

The full-production Modalix SoM runs multiple Large Language Models (LLMs) concurrently alongside vision and sensor models, all under 10W. Built from the silicon up for Physical AI deployment, it is designed as a pin-compatible drop-in replacement for the incumbent NVIDIA SoM form factor and requires no carrier board redesign.

Together, Palette Neat and Modalix SoM aim to reduce the engineering friction involved in adopting new AI hardware, without requiring developers to integrate a completely new architecture or rewrite their entire software stack.

Palette Neat is available as an open-source platform on GitHub. Documentation is available through the Developer Centre, while the full hardware specification for the Modalix MLSoC SoM is also available. SiMa.ai will also host a webinar on 30 June titled “Scaling Physical AI”. Founder and CEO Krishna Rangasayee has further shared insights in his blog, “Dismantling the GPU Moat to Scale Physical AI”.