Our Data Science solutions surface subtle signals
in your machine data so you can prevent
disruptions, boost efficiency, and operate ahead.
Industrial downtime costs businesses $50 billion every year—and most of it is preventable. Even small disruptions can spiral into delays, inefficiencies, and nonstop firefighting.
The problem isn’t a lack of warning. It’s that the early signals go unnoticed. Spur Insights empowers teams to get ahead of issues before they escalate—so performance stays high, surprises stay low, and growth stays on track.
Smarter Machines,
Sharper Advantage
You can’t make older machines faster, but you can make them smarter. Spur integrates directly into your machines to detect inefficiencies and anomalies in real time, giving OEMs and operators a new edge in performance, quality, and sustainability.
Insights That Close the Skills Gap
Performance That Knows
No Off Days
Real results from real manufacturers.
Real results from real manufacturers.
ORBIS embedded Spur’s auto-diagnostics into their injection molding machines, which produce reusable packaging. The result: over 1,000 kWh saved per machine per day and scrap rates cut in half. Efficiency and sustainability, unlocked.
By detecting failures early and automating maintenance, Spur helped a leading machine builder in the printing and converting industry reduce part failures by 74%. That meant lower warranty costs and a stronger customer experience.
Milacron used predictive maintenanceto reduce machine downtime by 15%, opening new revenue opportunities through smarter service contracts.
Our solutions are built on proprietary, field-tested technologies that uncover
what others overlook—turning raw machine data into real-time intelligence you
can act on.
Learning Twins
Our learning twins focus on behavioral change rather than raw data. By continuously analyzing how performance evolves, we catch meaningful deviations early—even when metrics appear “within spec.” It’s adaptive, context-aware, and built for real-world operations.
Wave Field Analysis
Our Wave Field Analysis technology leverages advanced vibration and ultrasound signal processing to identify performance anomalies. By detecting meaningful shifts through accessible sensor data, we eliminate the need for expensive, high-resolution hardware—without sacrificing insight.
iPID Technology
iPID (Inverse Proportional Integral Derivatives) applies inverse control theory to analyze internal machine regulation patterns. Subtle shifts in control loop behavior signal wear and inefficiency—giving you early visibility into mechanical issues without additional instrumentation.
Federated Learning
Our distributed machine learning approach keeps sensitive data local—training models directly at the machine level. This enables fast, secure intelligence across machines and sites without moving or centralizing raw data.
Giovanni Spitale
President, Customer Service & Support
“Our mission is to maximize the value our customers get from their investment. A day of downtime can cost millions, so reducing pump failure risk is a win-win—ensuring efficiency for customers while optimizing Milacron’s spare parts strategy.”
Matthew Dulcey
Chief Technology Officer at Procentec
“In 2019 we were seeking vendors for a secure remote platform for monitoring OT networks with our PROCENTEC Atlas diagnostic solution.” The Data Science team from SpurInsights were the perfect fit. “Working with a flexible, highly educated and motivated team allowed us to create a completely new business model from a scratch.”
Nikolaus Gaebler
Senior Vice President of Supply Chain Management at Hitachi Energy
Hitachi Energy partnered with Spur Insights to develop Bocca, an AI-based supply chain optimisation solution. “Bocca uncovered hidden supply chain dependencies, enabling us to address risks proactively and strengthen our resilience. We are now better equipped to navigate disruptions and make smarter, data-backed decisions.”
Laszlo Ivan
Director R&D and Engineering
Coperion Process Solutions
“I truly appreciate the seamless teamwork. With these promising results, we’ve opened Pandora’s box, and there’s no turning back. We’re now looking to scale these algorithms across our entire installed base, even legacy machines. This project proved that powerful predictive maintenance doesn’t rely on perfect conditions—just the right approach.”
Tanja Karrer
Head of Controls Engineering at Coperion
Spur Insights helped us develop valuable service ideas for our customers. “They worked closely with our engineers to deeply understand our machines, processes, and the real-world challenges users face—especially when adjusting complex machine settings. The close collaboration between their Data Science team and our engineers was key to building a successful AI-based solution.”
Most Data Science projects drag on with vague goals and uncertain results. Spur Insights
starts with clarity, validating what’s possible up front, reducing risk, and building only
what works.
We’ve honed a fast, focused methodology that cuts risk and confirms real value early.
Assess data, model behaviors and confirm business value.
Get a clear decision on whether to proceed-based on technical feasibility and real business value.
Preprocess data, explore key patterns, and build the best-fit model.
Uncover anomalies, quantify efficiency, and identify root causes.
Expand across machines, integrate systems, and fine-tune for growth.
