Reduce Construction Site Incidents with Custom AI

Most construction SMBs rely on project management software like Procore or Autodesk Build for safety documentation. These platforms are excellent systems of record, but their safety modules are entirely dependent on manual human input. They can confirm a safety checklist was submitted, but they have no way of verifying if the site conditions match the report. This creates a gap between reported compliance and objective reality.

To fill this gap, many firms adopt checklist apps like SiteDocs or SafetyCulture (iAuditor). These digitize the paperwork but suffer from the same fundamental limitation. A crew member can check a box stating they are wearing safety glasses while, in reality, they are not. A supervisor, stretched thin across multiple job sites, might upload 50 daily progress photos to a shared drive, but no one has the time to scrutinize every image for subtle hazards like a frayed extension cord or improperly stacked materials.

Consider a 25-person general contractor whose site supervisor manages two active projects. They conduct a morning safety meeting and a brief walkthrough. For the rest of the day, safety relies on workers filling out digital forms. An apprentice rushing a task removes their hard hat in a designated hard-hat zone. A daily progress photo captures this violation, but the image is simply filed away in Procore. The data proving the risk exists, but it sits dormant, unanalyzed, and unactionable. The incident only comes to light after an accident, not before.

The structural problem is that these off-the-shelf tools are built for structured data entry, not unstructured data analysis. Their architecture is designed around forms and database fields. They lack the native computer vision and language processing capabilities needed to analyze the photos, videos, and free-text comments that contain the most valuable safety information. They are passive repositories, not the active, automated monitoring systems that SMBs need to prevent incidents.

The first step would be a data audit. Syntora would review where your safety-related data currently lives. This involves mapping out photo storage locations (Procore, OneDrive, etc.), the format of daily reports (PDFs, text entries), and your historical incident data, like OSHA 300 logs. This audit identifies the 3-5 most frequent and preventable hazards, which become the initial focus for the AI model. You would receive a scope document detailing the approach and data requirements.

The technical core of the system would be an event-driven pipeline on AWS Lambda. For image analysis, a computer vision model like YOLOv8 would be trained to detect specific objects and conditions, such as the presence or absence of hard hats and safety vests. For text analysis, the Claude API would parse daily logs and near-miss reports to extract structured information about recurring issues, such as mentions of "slippery surfaces" or "equipment malfunction." This dual approach covers both visual and documented risks.

The system is designed to integrate, not replace. A lightweight FastAPI service would act as the brain, receiving data from your existing tools via webhooks. When a photo is uploaded to Procore, the service analyzes it and, if a hazard is found, pushes an alert directly back into your workflow. This could be an email to the safety manager with the annotated photo or a high-priority task created in your project management system. You receive the full source code, a runbook for maintenance, and the system runs in your own cloud account.