SiMa.ai slashes Physical AI deployment to days with Palette Neat agentic development tool

SiMa.ai cuts physical AI deployment from months to days with agentic developer tooling

SiMa.ai cuts physical AI deployment from months to days with agentic developer tooling Artificial intelligence chip startup SiMa Technologies Inc. launched Palette Neat today, a purpose-built agentic AI development environment for creating applications that connect the physical world to AI models. The new tool can be paired readily with the company's Modalix MLSoC system-on-module or the company's new PCIe companion card, allowing developers to rapidly build apps that can see, learn, adapt and interact with the real world. This is the vision of physical AI, where artificial intelligence models are used to power robotics, autonomous cars, drones, industrial machines, aerospace platforms, smart vision and more. "SiMa.ai is an AI software company that builds its own silicon," said founder and Chief Executive Krishna Rangasayee. "Today, we are delivering the industry's first agentic development environment for Physical AI." Rangasayee explained that using Palette Neat, today's developers will be able to take their knowledge and simply speak or type their ideas directly to the AI agent, which will take their abstract thoughts and build them directly into low-level compute code. This eliminates months of labor traditionally spent wiring up or porting and integrating applications onto new silicon. Often, developers spend months or weeks attempting to take new ideas and map them onto new silicon every time a company upgrades or offers a new form factor. Neat is designed to do away with this pain point by allowing developers to write what they need in natural language commands and build entire systems, enabling engineers to focus on system-level differentiation and cleaning up nuance. The agentic environment handles the heavy lifting by mapping application code directly to silicon, shrinking the development cycle time. SiMa said this lets them reuse application code and preserve 90% of their legacy software investment from other platforms without needing to rewire everything. Providing developers with alternatives to Nvidia in edge AI The company's Modalix SoM can run multiple large language models concurrently alongside vision and sensor models, all under 10 watts. This provides it the power to provide edge-AI for physical AI deployments in situations where it can act as a drop-in replacement for similar Nvidia Corp. system-on-module form factors and does not require a board redesign. Rangasayee argues that using Neat and Modalix could help developers break free from the market chokehold that graphics processing units have on the physical AI inference development market when scaling physical AI. Most developers learn to build for the Nvidia ecosystem because most cloud-based GPU hardware is CUDA-based, and Nvidia already holds almost 39% of edge AI, second only to Qualcomm Inc. at roughly 20%. Developers tend to gravitate towards the tooling that they already know and what they have on hand. Providing them with agentic platforms that allow them to migrate to a new system without needing to sit down and learn something altogether new allows them to prototype and experiment without spending years or months to train themselves to see benefits. "Until now, developers lacked a seamless alternative optimized for Physical AI performance," Rangasayee said. That's why Modalix was designed "pin-for-pin" to match Nvidia's Orin SoM. "Rather than trying to retrofit a power-hungry data center GPU for the edge, we deliver energy-efficient performance in stark contrast to the incumbent." SiMa is betting that developers will want the opportunity to have their software and models run on different hardware that can manage different opportunities for scalability, performance-per-watt, thermal envelopes and dynamic demands. By providing a different option from the incumbents, when the market is heavily dominated by Nvidia, the company is hoping to carve out some of the customer base by providing the tools that allow them to migrate.

SiMa.ai Launches Palette Neat, Industry's First Agentic Environment for Physical AI; Slashes Development from Months to Days

Palette Neat, alongside the production-ready, pin-compatible Modalix™ SoM, dismantles the legacy GPU moat to scale Physical AI SiMa.ai today launched Palette Neat™, the industry's first agentic development environment for Physical AI that collapses complex application timelines from months to days. The open source, purpose-built, integrated development environment combines a Physical AI execution library and 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 unmatched performance-per-watt for high-demand Physical AI workloads across robotics, automotive, drones, industrial automation, aerospace and defense, 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 innovative agentic workflow to abstract away low-level compute complexity, eliminating the months of labor traditionally spent on porting and integrating applications to new silicon. Key advantages of Palette Neat include: * A New Development Paradigm: Use AI to deploy Physical AI. Developers use natural language commands to build entire systems, enabling engineering teams to focus strictly 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 to a fraction of the timeline. Developers can seamlessly reuse existing application code, preserving approximately 90% of their legacy software investment. * Frictionless Platform Migration: Palette Neat, alongside the pin-compatible SoM and new PCIe companion card, dismantles the incumbent GPU moat to scale Physical AI while reducing cost, time, and engineering risk of switching hardware platforms. The full-production Modalix SoM runs multiple Large Language Models (LLMs) concurrently alongside vision and sensor models -- all under 10W and purpose-built from the silicon up for Physical AI deployment. Designed as a pin-compatible drop-in replacement for the incumbent NVIDIA SoM form factor, it requires no carrier board redesign. Together, Palette Neat and Modalix SoM eliminate the engineering friction of adopting new AI hardware, requiring developers to integrate a completely new architecture or rewrite their entire software stack. Availability and Resources: * Palette Neat Open Source: Access on GitHub. * Palette Neat Documentation: Get started at the Developer Center. * Modalix MLSoC SoM: Read the full hardware specification. * Upcoming Webinar: Register for the June 30 event on "Scaling Physical AI."