Automate Safety Inspections and Compliance Reporting with AI

Small construction firms often rely on a mix of generic safety apps and project management software. A supervisor might use an app like Safesite or iAuditor on a tablet. These tools are digital clipboards that replace paper, but they do not reduce the manual data entry workload or analyze visual evidence. The supervisor still has to tap through dozens of checkpoints, and a photo of a worker without a hard hat is just a file attachment the app cannot interpret.

Project management systems like Procore or BuilderTrend have safety modules, but these are fundamentally just forms and document storage. They require structured data input. To log an issue, a supervisor must manually find the right photo, create a new observation, select the issue type from a dropdown menu, write a description, and assign it. The system is a passive database, not an active analysis tool.

Consider this common scenario: a site supervisor for a 15-person crew takes 75 photos during their daily walk-through and fills out a paper checklist. At the end of the day, they spend 45 minutes reviewing every photo, typing up notes from the checklist into an email, and attaching the relevant images. This daily administrative tax pulls them away from managing the actual site work, and subtle violations in photos are easily missed during a cursory review.

The structural problem is that off-the-shelf software is built for structured data entry. These platforms cannot interpret the content of unstructured data like images or handwritten notes. Their architecture is designed to store information that a human has already identified and categorized, not to perform the identification itself. This forces your most valuable field personnel to act as low-level data entry clerks.

An engagement would begin with a thorough audit of your current safety process. Syntora would review your specific paper forms, analyze a sample of 100-200 site photos to assess image quality and common issues, and map your existing reporting workflow. The outcome of this audit is a clear plan identifying the 3-5 most critical safety violations that can be reliably detected with computer vision.

The technical approach involves fine-tuning a computer vision model like YOLOv8 on your specific site photos to spot hazards such as missing PPE or fall risks. For checklists, the Claude API would parse submitted text, whether from a scanned document or a photo of a handwritten form. The entire workflow would be built as a series of serverless functions on AWS Lambda, triggered when new files are uploaded to an S3 bucket. This event-driven architecture means you only pay for compute time when an inspection is being processed, keeping hosting costs under $50 per month.

The final system delivers a daily summary report directly to your project manager's inbox. This report includes links to photos with flagged violations and pre-filled text to create observations in your existing PM software. You also receive a simple dashboard, built on Supabase, that visualizes safety trends across projects. You get the full Python source code, a detailed runbook, and a system built to feed your current tools, not replace them.