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The client was a residential care facility for elderly patients who were at risk of falling, relying on wearable sensor fall detection for assisted living facilities to reduce incidents and response time. Each resident wore a small motion-tracking device as part of a wearable health monitoring setup that measured their movements in three directions. Their rooms were also equipped with AI in assisted living facilities that monitored temperature, humidity, light, noise levels, and whether someone was in or out of bed. The goal was to build an AI fall detection system that could automatically detect when someone had fallen and notify staff quickly. The team focused on understanding how movement patterns could indicate a fall and explored how room data could help add context in the future.
Published: September 11, 2025
The client provides a construction intelligence platform that aggregates raw construction data from a wide range of regional providers. This includes details about active and planned building projects, such as contractors, costs, timelines, and locations. The data is often incomplete or inconsistent across sources, requiring significant processing before it becomes usable. To address this, the client required an AI pipeline for construction and infrastructure data aggregation, combined with robust data engineering for PropTech to support future analytics and automation. The goal was to build a system that could handle large volumes of messy, semi-structured data, align it to a unified schema, and enrich it with geographic and financial context. The processed data would then serve as the foundation for AI for infrastructure analytics, machine learning, and client-facing reporting, showing how to build filterable construction intelligence from raw data feeds.
Published: June 24, 2025
Our client, a technology company managing large data centers, faced recurring pump failures in their cooling systems, causing costly downtimes. By deploying AI for data center operations in the form of an advanced anomaly detection system, we identified critical sensor patterns that allowed the maintenance team to address issues before they escalated. This led to a 30% reduction in false alarms and a 25% decrease in unplanned downtime.
Published: September 10, 2024