GlobalData
7 min read
In this exclusive interview, Tomer Srulevich, Chief Business Officer at Razor Labs, shares how the company’s award-winning DataMind AI™ platform is transforming mining operations. Srulevich discusses the journey from overcoming data integration challenges to delivering predictive, AI-driven maintenance that reduces downtime, boosts safety, and maximizes asset life.
Mining Technology: Congratulations on winning the Innovation award in the Equipment Diagnostics category! What does this recognition mean for Razor Labs and your team?
Tomer Srulevich: It’s a proud moment for the entire team. This award recognizes the years we’ve spent developing, deploying, and refining AI solutions tailored for mining’s unique challenges. It’s not just a technology win — it’s a validation of the impact we’re delivering to the field. Our work eliminates blind spots, prevents breakdowns, and makes sites safer and more efficient. This acknowledgment also energizes us to keep pushing the boundaries of what AI can do in heavy industry.
Mining Technology: Can you elaborate on the vision behind DataMind AI™ and how it aligns with Razor Labs’ overall mission in the mining industry?
Tomer Srulevich: From the start, our vision was to replace reactive maintenance with predictive, AI-driven action — not just alerts, but full root cause insights and prescriptive steps. DataMind AI™ is built to be a single out-of-the-box solution that integrates with existing infrastructure or stands on its own. We aim to reduce unplanned downtime, maximize asset life, and give teams total visibility across critical equipment — whether that’s pumps, mills, crushers, or conveyors. It's our way of embedding smart, real-time decision-making directly into site operations.
Mining Technology: DataMind AI™ clearly demonstrated significant cost savings and operational efficiency. How do you measure the success of your AI solutions in real-world applications?
Tomer Srulevich: We measure success where it matters most: downtime avoided, dollars saved, and reliability increased. For example, at an iron ore site, we identified rapid pump bearing deterioration that traditional inspection missed — saving the customer secondary damage and costly unplanned shutdowns. In another case, we uncovered electrical fluting in a conveyor motor bearing at a coal mine, enabling early intervention that extended the equipment’s lifespan. These outcomes saved hundreds of thousands of dollars — and just as critically, they prevented safety risks and disruptions to throughput.
Mining Technology: What were some of the key challenges you faced while developing DataMind AI™, and how did you overcome them?
Tomer Srulevich: One of the biggest hurdles was integrating fragmented, siloed data from vibration sensors, SCADA, and handheld tools into a coherent, AI-readable format. We tackled this by building a Sensor Fusion architecture — combining temperature, pressure, current, vibration, oil, and visual data into a unified model. Another challenge was adapting to low-speed equipment like kilns and stackers, where traditional vibration tools struggle. Our custom envelope demodulation and edge computing made it possible to detect issues at sub-100 RPM — something the market lacked until now.
