Custom AI for Proactive Construction Hazard Identification

Most safety programs rely on paper checklists or simple digital forms in Smartsheet. These tools only capture what a supervisor actively reports. They cannot see a potential hazard in the background of a routine progress photo, creating a significant blind spot in safety monitoring.

A site supervisor for a 30-person crew is focused on a concrete pour and snaps daily progress photos. In the background of one image, a ladder is placed at an unsafe angle next to an open, un-shored trench. The supervisor's daily checklist is marked 'all clear' because his attention was elsewhere. The safety manager sees the photo the next day, hours after a near-miss incident occurred.

Larger platforms like Procore or Autodesk Construction Cloud have safety modules, but they are designed for incident logging, not proactive detection. Their analysis tools are often limited to keyword searches in text-based reports. These systems cannot interpret the visual context of a photograph to identify a combination of factors, like equipment placement and missing barriers, that create a high-risk situation.

Syntora's approach to proactive hazard identification involves a structured engagement focused on your specific safety protocols and existing data sources.

The initial phase would involve detailed discovery to understand your current photo ingestion methods and safety compliance requirements. We would audit your existing photo sources, whether an API connection to a service like CompanyCam, shared cloud storage, or other digital assets. Collaborating with your safety lead, Syntora would then develop and refine a detailed prompt for the Claude API. This prompt would incorporate specific OSHA regulations relevant to your operations, such as scaffolding safety (1926.451) or fall protection (1926.501). We have experience building document processing pipelines using the Claude API for financial documents, and the same pattern applies to analyzing images for compliance. This initial prompt engineering would involve iterating with a sample set of your historical photos to ensure accurate hazard detection.

The core of the system would be a Python service, likely deployed on AWS Lambda, designed to trigger upon the upload of a new photo. This service would securely transmit the image and the engineered prompt to the Claude API for analysis. The prompt would instruct the AI to evaluate the image as a certified safety inspector, cross-referencing against the defined OSHA codes to identify potential hazards. The AI would then return a structured JSON object, detailing any identified risks, their approximate location within the image, and the corresponding OSHA standard. Syntora would engineer this analysis pipeline to target a processing time under 45 seconds per image, depending on image complexity and API response times.

The JSON results, along with a secure link to the source image, would be stored in a Supabase database. To facilitate immediate action for high-severity issues, a webhook would be configured to send an alert to a designated Slack channel or Microsoft Teams group. This alert would include the flagged image, the specific hazard identified, and the relevant OSHA code, enabling prompt intervention by safety managers.

For monitoring and auditing, Syntora would build a simple dashboard using Vercel. This dashboard would display key operational metrics such as photos processed per day, types of hazards detected, and API response times. The underlying FastAPI service would be engineered for efficiency, with typical response times expected to be under 800ms. All analyses would be logged, creating a searchable record valuable for safety audits, compliance checks, and identifying recurring safety patterns over time.

A typical engagement for this complexity involves a 6-10 week build timeline, preceded by 2-3 weeks of discovery. The client would need to provide access to historical site photos, internal safety documentation, and dedicated time from a safety lead for collaboration and feedback. Deliverables would include the deployed, custom AI system, source code, detailed technical documentation, and training for relevant personnel.