SoftServe

My team and I have spent enough time on commissioning floors to know where the budget actually disappears. Not in the engineering, as you might expect, but in the days spent debugging both robot programs and PLC logic against machines that are already installed. Karol Suchoń and I wrote about a different way of doing this. Test the control logic and motion programs against a high-fidelity digital twin first, edge cases included, and the physical commissioning becomes verification rather than discovery. In practice that’s typically a 30–50% reduction in commissioning time. What surprised me most was how much simulation speed changes behavior. On the Krones project, runs went from 3–4 hours to about 30 minutes. Engineers who used to think carefully about which scenario was worth a run started trying things just to see. That shift in habit is where the real value showed up. Full article here: https://lnkd.in/dkJH7wxT

Robots handle the hazardous inspection work, cutting work-at-height risk by 60–90%, while your experts stay in control of every decision. How much of your inspection work still puts people at risk?

A connected digital thread transforms manufacturing performance. 30–50% faster commissioning, 90% quicker simulations, and better decision-making from design through operations. Learn how.

Manufacturers need a fully connected digital continuum. While many vendors offer point solutions, SoftServe integrates simulation, operational digital twins, and virtual commissioning into one seamless digital thread that links design, automation, and operations. See the difference.

Robots now take on routine inspections across onshore, offshore, and subsea assets, completing them 30% faster and cutting more than 20% of manual effort. See how Physical AI makes energy operations safer and more efficient.

Physical AI is one of the most overpromised and underdelivered categories in enterprise technology. Few organizations have it running in production. The failure mode is predictable. Vision models meet industrial variability: sensor drift, occlusions, asset geometries never captured in training data. Inference latency that was acceptable in development becomes a problem on Jetson or IGX at the edge. The model degrades. The pilot stalls. The organizations that succeed bring engineering depth, secure infrastructure, and real-time observability to their NVIDIA investments. We have deployed Physical AI in real operational environments: nuclear plant design at TerraPower, liquid-cooling infrastructure at Global Switch, autonomous materials handling at Toyota Material Handling Europe, and fluid dynamics at Krones. Those deployments taught us that the NVIDIA AI foundation, with its synthetic data pipelines and physics-informed models, only survives real operations when paired with secure OT/IT infrastructure and real-time observability across distributed edge systems. For customers who need Physical AI to work at scale, that combination is likely to prove the most effective and valuable approach, and it is where Cisco and Splunk can contribute most. At NVIDIA GTC Berlin this month, we are exploring how these three layers integrate in practice. For Cisco and Splunk sales teams carrying Physical AI accounts - this is the conversation worth having. Details in the first comment.