Case Studies
Results in the Real World
Representative examples of how we've applied AI to solve complex operational problems across our three core industries.
* Client details anonymized per NDA.
Reducing Unplanned Downtime at a Surface Mining Operation
A large surface mining client was experiencing costly unplanned equipment failures on their primary haul fleet — averaging 14 unplanned downtime events per quarter.
Built a real-time anomaly detection system using sensor telemetry from 42 haul trucks. Deployed an LSTM-based time-series model trained on historical failure signatures, integrated into the existing dispatch system via a REST API.
68% reduction in unplanned downtime events over 6 months. Estimated ROI achieved in under 90 days of production deployment.
Accelerating Variant Classification in a Clinical Genomics Pipeline
A genomics laboratory was manually reviewing thousands of variant calls per week, creating a significant bottleneck in their clinical reporting workflow.
Developed a multi-label classification model trained on ClinVar data and proprietary lab annotations. Integrated with the LIMS via N8n workflow automation, auto-routing high-confidence calls and flagging edge cases for expert review.
83% of variants now auto-classified with >97% accuracy. Analyst review time reduced by 60%, enabling 3x throughput.
AI-Powered Scheduling Optimization for Field Technician Dispatch
A utility field services company was manually scheduling 200+ daily technician dispatches, resulting in inefficient routing, SLA misses, and high fuel costs.
Built a constraint-based optimization engine augmented with an LLM layer for natural-language job prioritization. Wrapped in an N8n orchestration layer that ingested work orders from ServiceMax and pushed optimized schedules back in real time.
22% reduction in average drive time. SLA compliance improved from 78% to 94%. First-time fix rate up 15%.
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