Monitor Construction Site Safety Compliance in Real-Time with AI

Most general contractors rely on safety management software like Procore, HammerTech, or SiteDocs. These platforms are excellent for logging observations, managing safety reports, and tracking incidents after they occur. A site manager does a walkthrough, spots a violation, takes a photo, and logs it. The failure mode is the time lag; a subcontractor could work without proper fall protection for 3 hours before it is noticed on a scheduled audit.

Off-the-shelf AI camera systems from vendors like Verkada offer basic person and vehicle detection, but their models are generic. They cannot reliably distinguish between a worker wearing a yellow hard hat and one without, or identify a temporary, newly-established exclusion zone around a crane lift. These systems are not trained on the specific visual context of a dynamic construction site, leading to a high rate of false positives and missed events.

Consider a scenario where a crew begins work on an upper floor before safety netting is fully installed. The safety manager is on the other side of a 10-acre site dealing with a delivery. The violation persists for 90 minutes. With the manual process, the risk is invisible until the manager's next physical inspection. By then, the opportunity for immediate correction is gone, and the event becomes a reactive report instead of a prevented incident.

The structural problem is that project management software is built as a system of record, not a system of real-time action. Its architecture is designed for storing user-submitted forms and photos, not for processing and interpreting continuous video streams. A solution requires an entirely different technical stack designed for video ingestion, edge computing, and low-latency alerting.

An engagement would begin with a site audit. Syntora would map your existing camera locations, assess network bandwidth, and work with your safety manager to define a precise list of detectable violations. We would define what constitutes an actionable alert: a 10-second video clip sent via SMS to a specific supervisor, or an observation automatically logged in Procore with tagged images. This discovery phase produces a clear technical specification.

The technical approach would use a computer vision model like YOLOv8, fine-tuned on data from your specific sites. To ensure real-time performance, this model would run on a small on-site server or edge device, processing video streams locally with latency under 500ms. When a violation is confirmed, the edge device sends a small data packet containing the event details to an AWS Lambda function. This serverless architecture keeps cloud costs low (typically under $50/month) and uses a FastAPI interface to integrate with third-party services like Twilio for SMS alerts or the Procore API for incident logging.

The delivered system plugs into your existing cameras and management software. Your site managers receive immediate, actionable alerts on their phones. You get a simple dashboard to review flagged events, helping to identify recurring issues or teams that may need additional training. You receive the full source code, the trained model weights, and a runbook detailing how to maintain the system and retrain the model with new data.