A leading pest control company faced growing challenges in managing vast volumes of loT sensor data generated around the clock. Manual interpretation was no longer scalable, and unclear alarms made prioritization difficult - increasing costs and slowing response times.

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Using machine learning, we developed a chain of models that automatically detect changes in movement patterns, classify activities as pertinent or non-pertinent, and rank them to support smarter prioritization and faster decision-making.

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Value for the client:

• Uniform, objective analysis framework enabling scalable operations
• Freed up analyst time for complex cases and key clients
• Consistent efficiency handling large data volumes
• Reduced dependency on individual expertise

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