Automate Safety Compliance Docs for Your Construction Crew

Superintendents often use a mix of Google Sheets and reminders to track certifications. A spreadsheet can list expiration dates, but it cannot automatically verify a new PDF is the correct OSHA 30 card, not a blurry photo of a lunch receipt. This manual verification takes the project manager hours each week.

For a 25-person crew, that is at least 25 certification cards, plus daily toolbox talks and weekly equipment inspection forms. A superintendent for a regional concrete company spent every Friday afternoon manually checking 125 documents against a master spreadsheet. If a single new hire's certification was missing, the entire crew could be at risk of a stop-work order during a surprise OSHA inspection.

Off-the-shelf document management systems like Dropbox or Box offer storage, but lack the intelligence to read the content of the documents. They cannot extract an expiration date from a PDF of an OSHA card or confirm that all 25 crew members signed the daily safety briefing. This leaves the final, critical verification step entirely manual and prone to human error.

Syntora would approach the problem by first conducting a detailed discovery phase to understand your specific document types, existing workflows, and integration requirements. The technical engagement would begin by integrating with your chosen document sources, such as Google Drive or Procore, leveraging their robust APIs. A Python-based document processing pipeline, using PyMuPDF, would be developed to efficiently extract text and image data from every file. This initial phase would also involve processing your historical documents to create a baseline dataset for training the AI models, which the client would need to provide.

The core AI architecture would utilize the Claude API to build sophisticated classifiers for identifying various document types (e.g., 'OSHA 10', 'Fall Protection Cert', 'Toolbox Talk Sign-in'). Concurrently, a Claude-powered extractor would be engineered to accurately pull critical entities like 'Employee Name', 'Issue Date', and 'Expiration Date'. Drawing from our experience with similar high-accuracy document processing in adjacent domains like finance, this methodology is capable of achieving reliable extraction for structured safety documents. The extracted data would then be structured and stored in a Supabase database. The entire processing logic would be encapsulated within a robust FastAPI application.

This FastAPI service would be deployed on AWS Lambda, providing a highly scalable and cost-effective serverless execution environment. New document uploads to the source folder would automatically trigger the Lambda function, for example, via an S3 event, ensuring near real-time processing. A typical engagement would also include the development of a user-friendly front-end dashboard, potentially built with Vercel, to offer a comprehensive compliance overview. This dashboard would track all employees, their required certifications, and current status. The system would be designed to run automated daily checks and could send proactive notifications via email or Twilio's API, alerting stakeholders to documents nearing expiration, within a client-defined timeframe. Typical build timelines for this complexity range from 4-8 weeks, depending on the number of document types and integration points.