Razor Labs

What a three days at International Mining and Resources Conference (IMARC) 2026! 🇦🇺 Sydney has been buzzing with conversations, new connections, and some exciting moments for Razor Labs. From announcing our partnership with Wabtec Digital Mine to seeing our Co-Founder & CTO, Michael Zolotov, and Co-Founder & CEO, Raz Roditti, on stage, there's been plenty happening both on and off the exhibition floor. Three days of meeting mining professionals, demonstrating DataMind AI™, and sharing ideas about the future of predictive maintenance. This is what IMARC is all about. Thank you to everyone who visited Booth Q31, and to our Razor Labs team for all the work! Raz Roditti Michael Zolotov Andrew K. Yanai Ganis Martins Elemo #IMARC2026 #RazorLabs #Mining #PredictiveMaintenance #DataMindAI #MiningTechnology

What a quarter it’s been for Razor Labs! Q3 brought exciting progress across our global mining operations, with new customer wins, expanding deployments, and real results from the field. From customer success stories showing how early fault detection is preventing costly downtime, to our webinar with Austmine and Electra Mining Africa 2026, it’s been a busy few months for our teams. And with International Mining and Resources Conference (IMARC) 2026 just around the corner, there’s even more to look forward to! Catch up on all the highlights in our Q3 newsletter: https://bit.ly/4B3xjbd #Mining #PredictiveMaintenance #AssetReliability #DataMindAI #RazorLabs

DataMind AI™ detected developing engine bearing wear on a Komatsu 930E before it progressed into a much more serious engine failure. Oil analysis showed elevated lead and soot levels, while other engine parameters remained within expected ranges. By connecting the oil results with operational data, DataMind AI identified progressive bearing wear linked to soot-driven oil degradation, giving the maintenance team time to intervene before further engine damage occurred. The result: an estimated 7 days of unplanned downtime prevented and approximately $200K in potential engine rebuild costs avoided. A good example of how combining multiple equipment signals can reveal what individual data points may not. Read the full case study: https://bit.ly/46NWyAu #Mining #MobileFleet #PredictiveMaintenance #HaulTrucks #AssetReliability #DataMindAI #RazorLabs

The mine prevented 2 days of downtime and avoided $60,000 in losses. Catastrophic failures don't happen suddenly. The warning signs appear long before the downtime. DataMind AI™ detected a differential lube pressure fault on a CAT 793D weeks before it escalated into a shutdown event. Turn early signals into action before production pays the price.

Not every critical fault triggers an alarm. And by the time some alarms appear, the damage is already done. A real scenario at a mine we work with, avoided a costly failure when DataMind AI™ identified a differential lube pressure fault on a CAT 793D that the OEM monitoring system failed to flag. The Result: • 2 days downtime prevented • $60,000 saved DataMind AI finds weak signals LONG BEFORE they become major failures.

⚠ $22,000 In Savings & 16 hours of unplanned downtime avoided. DataMind AI detected progressive brake pressure degradation in a Epiroc LHD Loader, identifying a worn slack adjuster and preventing brake failure. Turn early signals into action before production pays the price. Razor Labs.