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Arizona Tribune - InHand Networks Introduces Edge-Based, On-Site AI Decision-Making for Safety Management at Large Construction Sites

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InHand Networks Introduces Edge-Based, On-Site AI Decision-Making for Safety Management at Large Construction Sites
InHand Networks Introduces Edge-Based, On-Site AI Decision-Making for Safety Management at Large Construction Sites

InHand Networks Introduces Edge-Based, On-Site AI Decision-Making for Safety Management at Large Construction Sites

TECHNOLOGY 29.01.2026

On-Site Video Analytics Helps Teams Identify Risks and Trigger Alerts Faster - Without Relying on Continuous Cloud Video Streaming

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CHANTILLY, VIRGINIA / ACCESS Newswire / January 29, 2026 / InHand Networks today introduced an edge-based approach to construction-site safety management that enables on-site AI decision-making from live video. As large job sites expand in footprint and complexity - with shifting work phases, overlapping crews and equipment, and frequently changing restricted zones - safety risks can emerge across distributed areas and escalate quickly.

While video surveillance is now standard on many major construction sites, improved visibility alone does not guarantee better prevention. The challenge is often response latency: manual review, confirmation, and escalation can take time. Cloud-only video analytics can also be constrained by job-site realities such as uneven connectivity, variable bandwidth, and changing camera coverage.

InHand's approach is designed to bring the decision loop closer to where hazards occur by processing video streams on site. By running AI analysis locally, the system is intended to support near real-time detection of common safety risks - such as PPE compliance issues, restricted-zone intrusion, and smoke/fire indicators - and to trigger event-based actions, including notifications through management dashboards or mobile workflows. Relevant event evidence can be retained for post-incident review, safety training, and reporting.

Why edge-based, on-site decision-making matters

  • Faster response: reduces delays between detection and intervention

  • More resilient operations: keeps safety detection running even when connectivity fluctuates

  • Lower bandwidth dependency: avoids continuously uploading every camera stream

  • Flexible deployment: supports coverage expansion as projects and zones change over time

Reference hardware

A reference implementation for on-site video analytics is InHand's EC5550 AI Edge Computer, an industrial-grade hardware device designed to run AI inference locally for multi-camera video analysis in demanding environments. By processing video on site, the EC5550 is designed to help reduce reliance on continuous cloud streaming and support day-to-day operations over long project timelines.

Learn more

To learn how edge-based, on-site AI decision-making can integrate with construction safety workflows, visit https://www.inhand.com/en/support/blogs/ai-construction-safety-edge-decision/.

About InHand Networks

InHand Networks is a leading IoT solutions provider founded in 2001, dedicated to driving digital transformation across industries and empowering customers to unlock their full potential and achieve accelerated growth.

We specialize in delivering industrial-grade connectivity solutions for diverse sectors, such as business networks, industrial IoT, digital energy, smart commerce, and mobility. Our comprehensive product portfolio and services cater to various applications worldwide, including smart manufacturing, smart grid, intelligent transportation, smart retail, etc. With a global footprint spanning over 60 countries, we serve customers in the United States, France, Germany, the United Kingdom, Italy, China, and beyond.

Learn more: www.inhand.com

Media Contact
Eleanor Chen
Marketing & Communications
[email protected]

SOURCE: InHand Networks



View the original press release on ACCESS Newswire

T.Perez--AT

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