Custom AI Safety Inspections for Construction Firms

Most construction firms try off-the-shelf tools like SafetyCulture (iAuditor) or Procore's native safety module. These platforms are excellent for digital checklists but treat photos as simple attachments. They cannot analyze an image to see if a worker is wearing the correct gloves or if temporary wiring is properly secured. The responsibility for identifying the hazard remains entirely on the human inspector.

A 40-employee firm specializing in historical building restoration faces unique hazards. Standard software has a checklist for "fall protection," but it cannot tell the difference between a modern harness and one rated for their specific scaffolding. A foreman uploads a photo of the site, checks a box, and the report is green. But an AI system can be trained to recognize the specific harness model and flag it as non-compliant, a distinction generic software cannot make. This firm was getting fined for subtle violations that passed their standard digital inspection.

These platforms are built for mass-market compliance, not operational intelligence. Their data models are rigid. You can add custom fields, but you cannot add custom logic that runs on an image. To them, an image is just a block of pixels. You cannot query, "Show me all inspections from the last month where rebar caps were missing in the photo." You can only see reports where a human manually tagged that issue.

Syntora would approach this problem by starting with an in-depth discovery of your existing safety checklists and available historical incident photos. This initial phase would define a precise taxonomy of 20-50 specific hazards the AI needs to identify. A Python script using the Pillow library would then preprocess images, normalizing them for consistent analysis and flagging low-light or blurry photos that could yield poor results.

Syntora would develop a FastAPI endpoint designed to accept image uploads. This endpoint would then send each image, accompanied by a tailored prompt, to the Claude 3 Vision API. This process enables the AI to identify specific compliance issues from your custom list, such as missing PPE, incorrect tool usage, or environmental hazards. The identified findings would be stored as structured JSON in a Supabase Postgres database, creating a clear link between each finding, its specific photo, and the relevant job site.

The FastAPI service would be deployed as a serverless function on AWS Lambda, providing a scalable and cost-effective operational foundation. Syntora would also build a simple, mobile-friendly web interface using Vercel, accessible by foremen directly from any smartphone without requiring app store installation.

Upon completion of an inspection, a separate Python function would generate a comprehensive PDF report with annotated images highlighting the identified hazards. This report would then be automatically emailed to designated project managers and pushed directly to the Procore Daily Log API, streamlining documentation and reporting workflows. We have experience building similar document processing pipelines using Claude API for financial documents, and the same pattern applies effectively to construction safety documents.