Monitor and Flag Construction Site Safety Risks with AI

The default for site safety is a dedicated safety officer making rounds. This process is essential but limited. A single person cannot watch all high-risk areas, like leading edges on multiple floors or a crane's swing radius, at the same time. An incident can occur and go unnoticed for hours in an area the officer just inspected.

Off-the-shelf security camera systems from vendors like Verkada or Rhombus offer some AI features, but they are generic. They can detect a person's presence or movement but cannot determine if that person is wearing a hard hat or if they have crossed into a machine's designated exclusion zone. The system flags motion, but a site manager gets dozens of useless motion alerts an hour. This lack of contextual understanding creates alert fatigue, rendering the system ineffective.

Construction management platforms like Procore or Autodesk Construction Cloud are excellent for reactive safety management. A supervisor uses them to log an incident report or complete a safety checklist after the fact. These tools are systems of record, not systems of prevention. They help analyze trends from past incidents but do nothing to stop the next one from happening in the next five minutes.

The structural problem is that these tools are not built to interpret the specific, dynamic rules of a construction site. They lack the specialized computer vision models required to translate raw video into actionable safety intelligence. A generic security system sees a person; a custom-trained model sees a worker without fall protection near an unprotected edge.

The first step would be a risk audit to identify the 3-5 highest-priority, visually identifiable hazards for your sites. This could include missing hard hats, unauthorized personnel in restricted zones, or proximity to heavy machinery. Syntora would then assess your existing camera placement and quality to determine what is feasible. You receive a scope document that clearly defines the violations the system will detect and the required performance metrics.

A custom computer vision model, likely a YOLOv8 architecture, would be trained on footage from your actual job sites. This ensures the model understands your specific lighting conditions, site layouts, and equipment. The model would be wrapped in a Python-based FastAPI service that processes camera streams. When a violation is detected, the system uses Twilio to send an SMS alert with an image and timestamp to the designated supervisor. Gemini Pro would be used to generate a concise, human-readable alert message from the raw detection data.

The delivered system can run on a small on-site server like an NVIDIA Jetson for low-latency processing, or in the cloud. You receive a simple dashboard to review all flagged events, full ownership of the source code and trained models, and a runbook for maintenance. The system provides real-time alerts to the people who can act on them, integrating directly into their existing communication workflow without adding another platform to manage.