Build Custom AI for Safety Compliance or Hire a Consultant?

Small construction firms often use project management tools like Procore or Autodesk Build for safety documentation. These platforms are great for storage and access but offer limited automation. Their built-in analytics can count incidents but cannot read the unstructured text in a Job Safety Analysis (JSA) or an incident report to identify recurring risks or near-miss patterns. You can store 500 JSA PDFs in Procore, but a safety manager still has to open and read each one to find trends.

Consider a 25-person general contractor managing three job sites. Each day, foremen fill out a JSA on a tablet using an app like GoCanvas or a fillable PDF. These reports are emailed or uploaded to a shared drive. The safety manager's job is to review them for compliance, but they are swamped. A report might mention "improper ladder use" or "fall hazard near excavation," but unless that specific checkbox is ticked, the risk is buried in text. The manager spends 2-3 hours daily just reading, missing the fact that the same "fall hazard" near-miss has appeared on two different sites in one week.

The core problem is that generic project management software and form-builder apps are designed as databases, not language analysis tools. They store data in structured fields but treat text descriptions as inert blobs of information. They lack the Large Language Model (LLM) components needed to parse, categorize, and connect related concepts across hundreds of documents. Adding this capability is not a feature they can just "turn on"; it requires a fundamentally different architecture built for processing natural language.

This manual review process leads to reactive, not proactive, safety management. Real risks are only identified after an incident occurs, not when they first appear as near-misses in daily reports. The administrative burden also means safety managers spend time on paperwork instead of being on-site coaching crews, which is where they provide the most value.

The first step is an audit of your existing safety documents. Syntora would review a sample of 50-100 of your JSAs, incident reports, and toolbox talks to understand the format, language, and key risks you track. This discovery process defines the specific categories the AI needs to identify, like "fall protection," "electrical hazard," or "PPE non-compliance." You receive a clear data map before any code is written.

We would build a document processing pipeline using Python and the Claude API. When a new safety report PDF is uploaded, an AWS Lambda function triggers. The pipeline extracts the text, and the Claude API parses it to identify and categorize risks, noting the project, date, and personnel involved. FastAPI would expose an API endpoint that your team could use to upload documents directly, returning structured JSON data in under 5 seconds. All categorized data would be stored in a Supabase Postgres database for easy querying.

The final system is a simple dashboard built on Vercel that your safety manager uses. It shows a real-time feed of categorized risks from all job sites, with trends and alerts for recurring issues. For example, the system could automatically flag if "improper ladder use" is mentioned more than 3 times in a single week across the company. You get the full source code, a runbook for maintenance, and an architecture that costs under $50 per month to run on AWS.