Industry experts believe that Xaba's innovations could revolutionize smart manufacturing by overcoming the limitations of current rigid, code-heavy automation systems.

Recognized as a significant step toward intelligent, autonomous factories, Xaba's technology is poised to enhance efficiency across various sectors, including aerospace and automotive.

Additionally, Xaba is innovating in robotic welding and large-scale 3D printing, ensuring high-quality outputs while minimizing material waste.

Xaba Inc. has successfully raised $6 million in a seed extension round to advance its development of 'synthetic brains' for industrial robots, which will enhance autonomous programming and 3D printing capabilities.

This funding round was led by Hitachi Ventures and brings Xaba's total funding to $8 million, with additional contributions from Hazelview Ventures, BDC Capital, Exposition Ventures, and Impact Venture Capital.

The startup aims to tackle the inefficiencies in industrial automation, which currently incurs $7 billion annually in programming costs, primarily due to manual logic development.

CEO Massimiliano Moruzzi highlighted that traditional robotics require extensive programming and human oversight, which limits their adaptability and scalability in real-world applications.

Xaba's team of 24 experts in AI and mechatronics is preparing to enter production, addressing the limitations faced by the 4.4 million industrial robots currently in operation worldwide.

The company's technology allows industrial machines to autonomously adapt and execute tasks, significantly reducing the need for manual programming and supervision.

The funding will expedite the deployment of AI-driven robotics and cognitive industrial control systems, targeting a massive $9 trillion market opportunity.

Xaba's platform enables manufacturers to set automation goals using natural language, leading to automated code generation for independent robot operation and real-time adaptability.

Key features of Xaba's systems include a physics-informed machine learning model for replicating real-world environments, AI-driven code generation for rapid deployment, and a cognitive control framework compatible with both legacy and modern machines.