We don't deal in demos and slide decks. Here is the work we have done, the challenges our clients faced, and the numbers that prove it.
Richards Bay Minerals, a world-leading heavy mineral sands producer, faced a recurring and costly problem: calcrete blockages forming in their pond pump operations. These blockages were invisible until they caused full pipeline blockage events — each capable of shutting down operations for up to 4 days at a time, at enormous cost to production and maintenance schedules.
Detection was entirely manual and operator-dependent. The risk of a missed event was always present. The team needed a system that could watch the ponds continuously — and respond faster than any human could.
Excite-Data deployed an In-Sight Vision computer vision system tailored to the specific visual signature of calcrete formation. Over 40,000 training images were captured and annotated across varying lighting, weather, and operational conditions at the site. A deep learning detection model was trained and optimised for edge deployment directly at the pond.
The system integrates with the existing PLC infrastructure — triggering immediate alarms and automated responses the moment calcrete is detected, with a response time under 1 second.
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Talk to UsAt a high-throughput aluminium hot rolling facility, the cropping of ingot heads and tails was performed manually by operators making real-time judgement calls. The process was inconsistent — operators working under pressure would leave excessive material margins to be safe, resulting in significant over-cropping on every single pass.
With the volume of ingots processed per day, even a conservative estimate of excess material loss per pass compounded into millions of rands in annual material write-off. The team needed a deterministic, vision-based system that could locate the crop eye exactly — and trigger the cut at precisely the right point, every time.
Excite-Data built and deployed an automated crop eye detection system using a trained computer vision model that identifies the crop eye position in real time from live camera feeds on the rolling line. The model was integrated directly into the existing mill control system, providing automated cut point recommendations — and ultimately, automated triggers — with a model detection accuracy exceeding 99%.
The system removes operator variability from the equation entirely. Every pass is cut at the same optimal point, recovering up to 50kg of material per side per pass.
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Talk to UsRoyal Bafokeng Platinum's operation in the North West province was experiencing frequent, unplanned equipment breakdowns. The reactive maintenance model in place was costly: high callout fees, long parts lead times, and unplanned production stoppages were compounding to erode operating margins month over month.
The team had access to sensor data from their critical assets but no way to interpret that data in real time, detect early warning signals, or act before a failure event occurred. They needed to shift from reactive to predictive.
Excite-Data deployed an In-Sight Analytics predictive maintenance platform for four critical assets. The system ingests live sensor data from each machine, runs it through a suite of ML models trained on historical failure signatures, and generates real-time health scores with early warning alerts.
The platform surfaces SPC (Statistical Process Control) charts, anomaly detection alerts, and asset trend analysis through a custom operations dashboard — giving maintenance teams the visibility to schedule interventions during planned windows rather than scrambling during unplanned ones.
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Talk to UsExxaro Resources, one of South Africa's largest coal producers and a significant investor in renewable energy, required a robust data integration and analytics solution to consolidate operational data from multiple sources across their sites.
Their data landscape was fragmented — disparate systems, varying formats, and a lack of a unified view made it difficult for operational teams to make fast, data-driven decisions. The business needed a single source of truth that could aggregate, clean, and surface operational insights in near real time.
Excite-Data built a comprehensive data engineering and analytics solution that unified data from multiple operational systems into a governed, centralised platform. Custom ETL pipelines were developed to ingest from 40+ data sources including PLCs, SCADA systems, SAP, and plant historians.
The solution included custom analytics dashboards, automated reporting, and a data governance framework — giving operational and management teams a live, reliable view of performance across the operation.
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