Automatically Identify Safety Risks on Your Construction Sites

Many small construction firms rely on project management software like Procore or BuilderTrend for safety documentation. These platforms are excellent for storing digital forms and running safety meetings, but their function is reactive. A supervisor manually fills out a checklist, confirming compliance after the fact. The software cannot analyze a photo and flag that a subcontractor is on scaffolding without fall protection; it can only store the form that says he has it.

In practice, this means safety oversight is entirely dependent on manual review. An owner or project manager has to scroll through hundreds of daily photos uploaded to Google Drive or Dropbox from multiple sites. A critical violation, like a damaged ladder or an unsecured trench, might sit in a photo folder for 24 hours before anyone with the authority to fix it sees it. This lag between an event and its detection creates significant liability.

Off-the-shelf AI camera systems exist, but they often require expensive, proprietary hardware and high monthly subscription fees. They are designed for large-scale commercial projects, not a 15-person crew running three residential builds. The systems are also rigid. If you want to add a check for a specific risk unique to your work, like ensuring proper ventilation for interior finishing, you cannot train the model. You are limited to their pre-built hazard detectors.

The fundamental issue is that existing tools are built for documentation, not real-time analysis. Their architecture is designed to store structured data from forms, not to process and interpret unstructured data like images. This forces small firms into a manual review process that is slow, inconsistent, and fails to prevent risks before they become incidents.

The first step is to audit your existing data flow. Syntora would start by reviewing a week's worth of job site photos and daily reports to understand what you currently capture and where the highest-risk patterns are. This audit establishes a baseline for what is visually detectable and which specific OSHA standards are most relevant to your operations. You receive a document outlining the 5-10 key risks the AI would be trained to spot.

The technical approach would be a serverless pipeline on AWS. Photos uploaded from the field to a designated S3 bucket would automatically trigger an AWS Lambda function. This function uses computer vision models, like Amazon Rekognition or a fine-tuned YOLO model, to scan each image for predefined hazards. For text-based daily logs, another function would use the Claude API to parse the text and flag keywords related to near-misses or safety concerns. All findings are stored in a Supabase database.

The delivered system would be a simple web dashboard that displays a daily risk score for each job site, with links to the specific photos that triggered an alert. The system sends a single summary email each afternoon to the owner or safety manager. This workflow requires no new apps for your field crew; they continue uploading photos as they always have. You receive the full source code, the dashboard, and a runbook for system maintenance.