Use AI Automation to Reduce Job Site Safety Incidents

Many small general contractors rely on project management software like Procore or Autodesk Build. Their safety modules are effective for logging incidents after they happen, but they are not designed for proactive risk detection. They function as a digital filing cabinet for checklist-based inspections, relying entirely on what a busy site supervisor manually enters.

For example, a 20-person framing company uses SafetyCulture (iAuditor) for daily checklists. A supervisor takes 50 photos of the site and attaches them to the report, checks off the boxes, and submits. One photo clearly shows a worker near a second-story edge without fall protection. Because the supervisor is focused on completing the form, they miss the violation. The data showing the risk exists in the photo, but SafetyCulture cannot see or interpret it. The system only knows the checklist was completed.

This reveals the core architectural limitation of these tools. They are databases with forms on top, built to store structured data entered by humans. They lack the computer vision or natural language processing capabilities needed to extract risk signals from unstructured data like images or the free-text comments in a daily log. You cannot configure a Procore report to automatically flag a photo containing a ladder set at an unsafe angle. The tools record compliance; they do not enforce it.

The result is a safety program that is fundamentally reactive. You analyze patterns from incident reports, but you cannot get ahead of the next one. The leading indicators of risk are buried in photos and text logs, and manually reviewing hundreds of photos every day is not a feasible use of a supervisor's time.

The first step is a discovery audit of your existing safety data. We would analyze a batch of your site photos and daily logs to identify the most common and highest-risk compliance issues. This audit confirms which hazards (like missing PPE or improper scaffolding) can be reliably detected with a computer vision model. You receive a clear scope document outlining what the system will monitor and the expected accuracy.

The technical approach would use a custom-trained computer vision model deployed on AWS Lambda. When a supervisor uploads photos to a designated cloud storage folder, a Lambda function triggers automatically. The model analyzes each image in under 5 seconds, checking for specific safety violations. For text-based daily logs, the Claude API would be used to parse free-text descriptions for mentions of near-misses or unsafe conditions. Pydantic ensures all extracted data is correctly structured before being stored in a Supabase database.

The delivered system provides a simple dashboard showing a feed of non-compliant images and flagged log entries. Critical alerts, like a person detected in a fall-risk zone without a harness, would trigger an immediate text message to the site supervisor and company owner. You receive the full Python source code, the trained model files, and a runbook detailing how to manage the system. The solution integrates into your workflow, it does not replace your primary project management tool.