Automate Construction Compliance with a Custom AI System

Most construction firms rely on project management software like Procore. Procore is excellent for logging information, but its compliance tools are essentially digital checklists. The system can confirm a daily report was submitted, but it cannot read the report's text to identify that a subcontractor mentioned using a faulty harness. It sees a photo was uploaded, but it cannot tell if that photo shows a worker standing on the top step of a ladder, a clear OSHA violation.

A typical scenario involves a safety manager for a 30-person firm spending every Monday morning reviewing the previous week's paperwork from five job sites. They manually scan hundreds of daily log entries and photo uploads. They might spot a photo from Thursday showing a crew working in a deep trench, but they have no immediate way to confirm if the required trench box was in use. This forces them to call the superintendent, interrupting active work to verify past events. This manual process takes 8-10 hours per week and is prone to human error.

Specialized safety apps like iAuditor or Safesite digitize the forms but do not solve the core issue. They capture structured data well but still rely on busy field staff to accurately fill out checklists. These apps cannot cross-reference a completed checklist with a contradictory photo or a note in a daily log. The structural problem is that these tools are built as systems of record, not systems of intelligence. Their architecture is designed for data storage, not for automated analysis and correlation across unstructured data formats like free text and images.

The first step would be a thorough audit of your current documentation workflow. Syntora would map every source of compliance data, from daily logs in your project management system to subcontractor certifications stored as PDFs. The goal is to understand the format, frequency, and quality of each data stream. You would receive a concise data audit report that identifies the most valuable data for automation and outlines a clear technical path forward.

The core of the solution would be a Python-based processing pipeline deployed on AWS Lambda for cost-effective, event-driven execution. When a new document or photo is added to your system, it triggers the pipeline. The Claude API parses text from reports and forms, extracting key information like personnel, equipment, and safety observations. For images, a computer vision model would be used to detect the presence or absence of personal protective equipment (PPE) and identify obvious hazards. A FastAPI service acts as the central hub, managing the workflow and storing results in a Supabase database.

The delivered system would send a daily digest email or a dashboard link to your safety manager. Instead of reading hundreds of pages, they receive a short, prioritized list of potential issues, such as 'Site A, Daily Log: Mention of damaged scaffolding' or 'Site B, Photo #214: Two workers detected, only one wearing a hard hat.' These alerts can be pushed directly into your existing PM tool as an Observation, integrating seamlessly into the workflow you already have. You receive the full source code and the system runs in your own cloud account.