Case Study
2 Min Read
A European industrial IoT firm specializing in predictive maintenance tools for manufacturing equipment across automotive and heavy machinery sectors.
The company had amassed large volumes of sensor data (vibration, pressure, acoustic) from factory machines but couldn’t use it due to:
Unstructured raw logs with poor labeling
Lack of failure-specific annotations
Frequent anomalies and inconsistencies in time-series records
Savvy Strat developed a custom curation and annotation pipeline for time-series sensor data:
Cleaned and structured 2+ TB of raw data, aligning it with machine IDs, fault codes, and operational timelines
Collaborated with OEM experts to define accurate failure conditions and normal baselines
Annotated patterns leading to mechanical wear, part failure, and anomalous vibrations across machine types
Validated and tagged sequences for ML modeling, creating a high-precision training set for supervised learning
Fault detection accuracy increased from 68% to 91%
Reduced false positives in alerts by 42%
Enabled real-time deployment of ML models on factory edge devices
Helped the client secure a new industrial partner by demonstrating predictive uptime with annotated datasets
"The depth with which Savvy Strat understood our data—without needing handholding—was astonishing. We now have a dataset that even our engineers trust."
— CTO, Predictive Maintenance Solutions Company
