The collaboration between Oversonic Robotics and STMicroelectronics highlights how robotics is beginning to fill gaps traditional automation cannot.
Updated
January 23, 2026 10:41 AM
3D render of humanoid robots working in a factory assembly line. PHOTO: ADOBE STOCK
Oversonic Robotics, an Italian company known for building cognitive humanoid robots, has signed an agreement with STMicroelectronics, one of the world’s largest semiconductor manufacturers, to deploy humanoid robots inside semiconductor plants.
According to the companies, this is the first time cognitive humanoid robots will be used operationally inside semiconductor manufacturing facilities. And the first deployment has already taken place at ST’s advanced packaging and test plant in Malta.
At the center of the collaboration is RoBee, Oversonic’s humanoid robot. RoBee is designed to carry out support tasks within industrial environments, particularly where flexibility and interaction with human workers are required. In ST’s factories, the robots will assist with complex manufacturing and logistics flows linked to new semiconductor products. They are intended to work alongside existing automation systems, not replace them.
RoBee is notable for its ability to operate in environments shared with people. It is currently the only humanoid robot certified for use in both industrial and healthcare settings and is already in operation within several Italian companies. The robot is also being used in experimental hospital programs. That background helped position RoBee for deployment in tightly controlled manufacturing environments such as semiconductor plants.
Fabio Puglia, President of Oversonic Robotics, described the agreement as a milestone for deploying humanoid robots in complex industrial settings: “The partnership with STMicroelectronics is a great source of pride for us because it embodies the vision of cognitive robotics that Oversonic has brought to the industrial and healthcare markets. Being the first to introduce cognitive humanoid robots in a sophisticated production context such as semiconductors means measuring ourselves against the highest standards in terms of reliability, safety and operational continuity. This agreement represents a fundamental milestone for Oversonic and, more generally, for the industrial challenges these new machines are called to face in innovative and highly complex environments, alongside people and supporting their quality of work”.
From STMicroelectronics’ side, the use of humanoid robots is framed as part of a broader effort to manage growing manufacturing complexity. he company said RoBee will support complex tasks and help manage the intricate production flows required by newer semiconductor products. It is also expected to contribute to improved product quality and shorter manufacturing cycle times. The robots are designed to integrate with existing automation and software systems, helping improve safety and operational continuity.
In semiconductor manufacturing, precision and reliability leave little room for experimentation. Therefore, introducing humanoid robots into this environment signals a practical shift. It shows how robotics is starting to fill gaps that traditional automation has struggled to address.
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Examining the shift from fast answers to verified intelligence in enterprise AI.
Updated
January 8, 2026 6:33 PM
Startup employee reviewing business metrics on an AI-powered dashboard. PHOTO: FREEPIK
Neuron7.ai, a company that builds AI systems to help service teams resolve technical issues faster, has launched Neuro. It is a new kind of AI agent built for environments where accuracy matters more than speed. From manufacturing floors to hospital equipment rooms, Neuro is designed for situations where a wrong answer can halt operations.
What sets Neuro apart is its focus on reliability. Instead of relying solely on large language models that often produce confident but inaccurate responses, Neuro combines deterministic AI — which draws on verified, trusted data — with autonomous reasoning for more complex cases. This hybrid design helps the system provide context-aware resolutions without inventing answers or “hallucinating”, a common issue that has made many enterprises cautious about adopting agentic AI.
“Enterprise adoption of agentic AI has stalled despite massive vendor investment. Gartner predicts 40% of projects will be canceled by 2027 due to reliability concerns”, said Niken Patel, CEO and Co-Founder of Neuron7. “The root cause is hallucinations. In service operations, outcomes are binary. An issue is either resolved or it is not. Probabilistic AI that is right only 70% of the time fails 30% of your customers and that failure rate is unacceptable for mission-critical service”.
That concern shaped how Neuro was built. “We use deterministic guided fixes for known issues. No guessing, no hallucinations — and reserve autonomous AI reasoning for complex scenarios. What sets Neuro apart is knowing which mode to use. While competitors race to make agents more autonomous, we're focused on making service resolution more accurate and trusted”, Patel explained.
At the heart of Neuro is the Smart Resolution Hub, Neuron7’s central intelligence layer that consolidates service data, knowledge bases and troubleshooting workflows into one conversational experience. This means a technician can describe a problem — say, a diagnostic error in an MRI scanner — and Neuro can instantly generate a verified, step-by-step solution. If the problem hasn’t been encountered before, it can autonomously scan through thousands of internal and external data points to identify the most likely fix, all while maintaining traceability and compliance.
Neuro’s architecture also makes it practical for real-world use. It integrates seamlessly with enterprise systems such as Salesforce, Microsoft, ServiceNow and SAP, allowing companies to embed it within their existing support operations. Early users of Neuron7’s platform have reported measurable improvements — faster resolutions, higher customer satisfaction and reduced downtime — thanks to guided intelligence that scales expert-level problem solving across teams.
The timing of Neuro’s debut feels deliberate. As organizations look to move past the hype of generative AI, trust and accountability have become the new benchmarks. AI systems that can explain their reasoning and stay within verifiable boundaries are emerging as the next phase of enterprise adoption.
“The market has figured out how to build autonomous agents”, Patel said. “The unsolved problem is building accurate agents for contexts where errors have consequences. Neuro fills that gap”.
Neuron7 is building a system that knows its limits — one that reasons carefully, acts responsibly and earns trust where it matters most. In a space dominated by speculation, that discipline may well redefine what “intelligent” really means in enterprise AI.