Redefining sensor performance with advanced physical AI and signal processing.
Updated
January 8, 2026 6:32 PM
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Robot with human features, equipped with a visual sensor. PHOTO: UNSPLASH
Atomathic, the company once known as Neural Propulsion Systems, is stepping into the spotlight with a bold claim: its new AI platforms can help machines “see the invisible”. With the commercial launch of AIDAR™ and AISIR™, the company says it is opening a new chapter for physical AI, AI sensing and advanced sensor technology across automotive, aviation, defense, robotics and semiconductor manufacturing.
The idea behind these platforms is simple yet ambitious. Machines gather enormous amounts of signal data, yet they still struggle to understand the faint, fast or hidden details that matter most when making decisions. Atomathic says its software closes that gap. By applying AI signal processing directly to raw physical signals, the company aims to help sensors pick up subtle patterns that traditional systems miss, enabling faster reactions and more confident autonomous system performance.
"To realize the promise of physical AI, machines must achieve greater autonomy, precision and real-time decision-making—and Atomathic is defining that future," said Dr. Behrooz Rezvani, Founder and CEO of Atomathic. "We make the invisible visible. Our technology fuses the rigor of mathematics with the power of AI to transform how sensors and machines interact with the world—unlocking capabilities once thought to be theoretical. What can be imagined mathematically can now be realized physically."
This technical shift is powered by Atomathic’s deeper mathematical framework. The core of its approach is a method called hyperdefinition technology, which uses the Atomic Norm and fast computational techniques to map sparse physical signals. In simple terms, it pulls clarity out of chaos. This enables ultra-high-resolution signal visualization in real time—something the company claims has never been achieved at this scale in real-time sensing.
AIDAR and AISIR are already being trialled and integrated across multiple sectors and they’re designed to work with a broad range of hardware. That hardware-agnostic design is poised to matter even more as industries shift toward richer, more detailed sensing. Analysts expect the automotive sensor market to surge in the coming years, with radar imaging, next-gen ADAS systems and high-precision machine perception playing increasingly central roles.
Atomathic’s technology comes from a tight-knit team with deep roots in mathematics, machine intelligence and AI research, drawing talent from institutions such as Caltech, UCLA, Stanford and the Technical University of Munich. After seven years of development, the company is ready to show its progress publicly, starting with demonstrations at CES 2026 in Las Vegas.
Suppose the future of autonomy depends on machines perceiving the world with far greater fidelity. In that case, Atomathic is betting that the next leap forward won’t come from more hardware, but from rethinking the math behind the signal—and redefining what physical AI can do.
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Rethinking 3D modelling for a world that generates too much, too quickly.
Updated
January 8, 2026 6:32 PM
A hologram in the franchise Star Wars, in Walt Disney World Resort, Orlando. PHOTO: UNSPLASH
MicroCloud Hologram Inc. (NASDAQ: HOLO), a technology service provider recognized for its holography and imaging systems, is now expanding into a more advanced realm: a quantum-driven 3D intelligent model. The goal is to generate detailed 3D models and images with far less manual effort — a need that has only grown as industries flood the world with more visual data every year.
The concept is straightforward, even if the technology behind it isn’t. Traditional 3D modeling workflows are slow, fragmented and depend on large teams to clean datasets, train models, adjust parameters and fine-tune every output. HOLO is trying to close that gap by combining quantum computing with AI-powered 3D modeling, enabling the system to process massive datasets quickly and automatically produce high-precision 3D assets with much less human involvement.
To achieve this, the company developed a distributed architecture comprising of several specialized subsystems. One subsystem collects and cleans raw visual data from different sources. Another uses quantum deep learning to understand patterns in that data. A third converts the trained model into ready-to-use 3D assets based on user inputs. Additional modules manage visualization, secure data storage and system-wide protection — all supported by quantum-level encryption. Each subsystem runs in its own container and communicates through encrypted interfaces, allowing flexible upgrades and scaling without disrupting the entire system.
Why this matters: Industries ranging from gaming and film to manufacturing, simulation and digital twins are rapidly increasing their reliance on 3D content. The real bottleneck isn’t creativity — it’s time. Producing accurate, high-quality 3D assets still requires a huge amount of manual processing. HOLO’s approach attempts to lighten that workload by utilizing quantum tools to speed up data processing, model training, generation and scaling, while keeping user data secure.
According to the company, the system’s biggest advantages include its ability to handle massive datasets more efficiently, generate precise 3D models with fewer manual steps, and scale easily thanks to its modular, quantum-optimized design. Whether quantum computing will become a mainstream part of 3D production remains an open question. Still, the model shows how companies are beginning to rethink traditional 3D workflows as demand for high-quality digital content continues to surge.