Build Production-Grade Automation for Site Reporting

Construction teams often start with form builders connected to a spreadsheet via internal automation. This works for simple data entry. But when a report includes 15 high-resolution photos of a concrete pour, the workflow fails on attachment size limits or burns through the monthly execution quota in a week. The automations cannot resize images or perform optical character recognition on a scanned delivery ticket.

A project manager for a specialty subcontractor with 8 crews receives 8 different daily reports. He uses an automation tool to flag reports that are missing a signature. The tool's conditional logic is binary; it can check if the signature box is filled, but it cannot analyze photos to confirm safety harnesses were worn correctly. The PM spends two hours every morning manually opening each report, downloading 10-20 photos per report, and visually checking for compliance, defeating the purpose of the automation.

These point-and-click platforms are built for generic business tasks, not physical-world data. They treat an image as a file to be moved, not as a source of information. They cannot run the custom Python code needed to measure the stockpile volume from a drone photo or read serial numbers from equipment tags. This forces teams into manual review for any task requiring actual analysis.

Syntora would begin by working with you to identify the specific sources of your site reports, which could include connecting to a Procore API endpoint, monitoring a shared Google Drive folder, or integrating with an email inbox. For data persistence, we would typically use Supabase to store both raw reports and the structured data extracted from them. This approach creates a permanent, queryable record of all submitted reports.

The core of the system would be a FastAPI service designed to orchestrate the data extraction and processing. For text-based reports, the Claude API would be employed to identify and pull out structured information, such as man-hours, materials used, and reported safety incidents. Syntora has built document processing pipelines using Claude API for similar tasks with financial documents, and the same pattern applies to construction industry documents. For image analysis, Python's Pillow library could be used to preprocess photos (e.g., resize, compress) before running them through custom models for tasks like detecting personal protective equipment.

The FastAPI service would typically be deployed as a container on AWS Lambda, using an event-driven architecture that triggers processing only when new reports are uploaded. This design helps optimize hosting costs by only consuming resources during active processing. The extracted data would then be written directly into your chosen project management system and, where applicable, summary financial data could be pushed to QuickBooks via its API. Syntora focuses on building efficient integrations to ensure data flows accurately to relevant platforms.

Monitoring and error handling are integral parts of the system design. Every processing job would be logged using structlog for debugging. If a report fails to parse or an external API experiences an outage, the system would be configured to send notifications to a designated channel, such as Slack, including the report ID and error details. This proactive approach ensures timely intervention and data integrity.

A typical engagement for a system of this complexity, designed for a new document type and integrations, generally spans 8 to 12 weeks. Syntora's deliverables would include the fully deployed, documented automation system, complete with source code and operational instructions. Clients would need to provide access to relevant data sources, APIs, and subject matter expertise to define data extraction rules and validation logic.