Build a Custom AI System to Monitor Construction Site Safety

Many construction firms use the safety modules in Procore or Autodesk Build. These are excellent systems for logging manual observations, but they are entirely reactive. A safety manager must spot an issue, open the app, and log it. The software will not proactively find a pattern connecting three near-miss reports about ladder usage on Site A to an actual fall on Site B. The data is stored, but not analyzed.

Off-the-shelf AI tools like Smartvid.io exist, but they are often priced for enterprise-scale projects and have high recurring fees. A 25-person general contractor running four projects simultaneously cannot justify the cost. These tools also come with a fixed set of detectable hazards. If your team specializes in a trade with unique risks, like improper shoring in excavation, a generic model trained on general construction sites will miss your most critical issues.

Consider a 30-person GC where superintendents upload 50 photos per day from three different job sites. A safety manager back in the office is tasked with reviewing these 150 photos. They spend over an hour scrolling through images, trying to spot missing guardrails or frayed electrical cords. It is tedious work, and they eventually miss a photo showing a worker standing on the top step of a ladder. The evidence of the risk was in the data, but no one had the time or attention to catch it before an incident occurred.

The structural issue is that these existing tools are closed platforms. They are built to solve a general problem for a mass market, not your specific safety workflow. You cannot feed their risk scores into your own analytics dashboards or connect their alerts to a non-supported tool your team uses. You are locked into their pre-defined system, which may not align with your specific safety program.

Syntora would start with a data audit of your current safety documentation. We would review your daily log templates, incident forms, and a sample of at least 500 site photos from past projects. This audit clarifies what data is structured, which hazards are most common, and whether your photo quality is sufficient for training a model. You receive a brief written plan detailing the exact risks the system would be built to detect.

For analyzing images, the approach would involve fine-tuning a computer vision model like YOLOv8 on your photos to spot specific issues like missing hard hats or improper ladder placement. For text, a FastAPI service would use the Claude API to parse daily logs and flag language indicating near-misses or unsafe conditions. All identified risks and corresponding data would be stored in a Supabase database, which provides a simple and effective backend.

The delivered system would be an automated daily safety briefing. Every morning, an email or Slack message is sent to designated managers summarizing the key risks identified from the previous day's data. The summary includes direct links to the photos or log entries that triggered the alert. This entire system runs on AWS Lambda, typically costing under $50 per month, and you receive all the source code.