Predictive Maintenance Detail page
Predictive maintenance uses machine data and analytics to identify potential equipment issues early, helping teams prevent failures before they happen.
Our Expertise
We build predictive maintenance systems around real industrial data, not demo datasets
We help teams validate ideas early with rapid prototypes and working models
We handle architecture, ML engineering, and deployment as one connected effort
Our Software Solutions
Built predictive analytics platforms using data from multiple control systems
Developed machine learning cores for time-series analysis, image processing, and anomaly detection
Created systems that helped engineers identify equipment issues before failures occurred
Case Study
Building a Predictive Analytics Platform for Industrial Control Systems
Customer :
Schneider Electric
Domain :
Predictive Maintenance
Schneider Electric’s Innovation team wanted to build a predictive analytics product using plant data coming from multiple industrial control systems. The vision was ambitious, but the path wasn’t straightforward. The product required deep technical ownership across architecture, analytics, infrastructure, and deployment, all while working through the realities of industrial data environments.
Customer Challenge
- The product depended on data coming from multiple control systems, making integration and analysis difficult
- The engineering team needed a partner who could own everything from solutioning to deployment
- Extracting and validating industrial data required coordination across systems, teams, and infrastructure
What We Did
- Took ownership of the product development effort, including architecture, solutioning, and implementation
- Built working prototypes and iterated closely with stakeholders to refine the product direction
- Worked through complex data extraction and infrastructure challenges to make the analytics platform usable in real environments
Impact
- Helped bring a high-risk predictive analytics concept into a working, demo-ready product
- Enabled successful customer demonstrations that received strong feedback from leadership and end users
- Supported the foundation for what evolved into a larger industrial analytics product offering
Technologies Used
Based on stakeholder feedback and project metrics.
36 Months
Released In XXXX
3
People Team
2x
Development Speed
4.6
CSI Rating
Testimonials
“WonderBiz did a wonderful job in understanding the high level requirements, detailing it out & coming up with multiple solutions, along with working prototypes, working with us and our users, iterating, and developing. All this, with the speed that was expected from my side.
Because of WonderBiz, we could launch not one, but several applications to our end users! We have received rave feedback around all the applications delivered. These applications have now set a new benchmark in providing great business value and at the same time provide extraordinary User Experience with simplicity.
The team that WonderBiz provides brings with them passion, creativity, and ownership. It’s great to be working with this wonderful young talent from WonderBiz!”
Bhaskar Sinha
Director, Innovation Lab, Schneider
Building Machine Learning Foundations for Industrial Intelligence
Customer :
Schneider Electric
Domain :
Machine Learning / Predictive Maintenance
Schneider Electric was working on a long-term initiative to make industrial products smarter using Machine Learning and AI. The goal wasn’t just adding isolated features, it was about building core intelligence capabilities that future industrial applications could depend on. Since off-the-shelf solutions wouldn’t work for these use cases, the entire foundation had to be built from scratch.
Customer Challenge
- The customer needed machine learning capabilities built specifically for industrial environments and data
- Existing off-the-shelf tools were not suitable for the complexity of the applications being planned
- Schneider needed a remote engineering team that could grow into advanced ML and data engineering work over time
What We Did
- Worked on image analysis, time-series data processing, predictive analytics, and discrete data classification
- Worked on image analysis, time-series data processing, predictive analytics, and discrete data classification
- Helped with architecture decisions, ML implementation, application development, and system integration
Impact
- Built a growing ML engineering team that supported multiple industrial AI initiatives over several years
- Helped Schneider scale core machine learning modules for future industrial applications
- Enabled faster development of intelligent industrial products using custom-built ML foundations
Technologies Used
Based on stakeholder feedback and project metrics.
48 Months
Released In XXXX
7
Machine Learning Engineers
2x
Development Speed*
4.2
CSI Rating
Testimonials
“I’ve been working with WonderBiz for the last four years. If I want to describe them, then two words come to my mind: passionate and innovative
They have a rich expertise in Data Science and Machine Learning, Sensors protocol and Application Development. I have worked with them on 2 major projects: one is in the Computer Vision domain and another is Predictive Maintenance using discrete data. WonderBiz was engaged from the first day of the project. We have jointly developed Concept to working product.
WonderBiz helps us in the Machine Learning domain, IoT base architectures and App Development. They also help us in providing correct tech stack selection, system architecture and executing end-to-end implementation.
We always enjoy working with WonderBiz & I am sure we’ll continue to do that.“
Amitabha Bhattacharya
Senior Principal Technical Expert, Schneider Electric
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