AI Automation for Subcontractor Management

Small construction businesses often manage subs with a mix of Outlook, Excel, and a project management tool like Procore. Procore is excellent for high-level project tracking but its document management is a generic file store. The system does not automatically parse a subcontractor's PDF bid to extract line items, scope exclusions, and insurance certificate expiry dates. That data remains locked in the document, requiring hours of manual entry.

Consider a general contractor with 5 active projects. For a single project, you might receive 3-5 bids for each of 15 trades, resulting in over 50 PDF bids to review. Your project coordinator spends hours opening each PDF, manually keying line items into an Excel bid leveling sheet, and cross-referencing against the scope of work. They must also check each sub's COI and W-9 forms for compliance, a task that often falls through the cracks until payment is due. A single data entry error in a line item can cost thousands on the project margin.

The structural issue is that project management software is built for human data entry, not machine-driven data extraction. The architecture of tools like Procore or Buildertrend assumes a person will read a document and then type the relevant data into a structured field. They do not have built-in optical character recognition (OCR) or Large Language Model (LLM) endpoints designed to parse the unstructured text and tables common in subcontractor proposals and insurance forms.

The first step is an audit of your current subcontractor documents. Syntora would analyze 20-30 sample bids, COIs, and invoices to map out the common data formats and variations. We would also map the API endpoints of your project management system to understand exactly how data needs to be structured for ingestion. This audit produces a clear data schema and a fixed-scope build plan.

We would build a document processing pipeline using AWS Lambda and the Claude 3 API. When a new bid arrives in a designated email inbox, a Lambda function triggers. The function uses Claude's vision capabilities to extract structured data from the PDF attachment, identifying line items, total cost, and key dates. Pydantic models validate the extracted data against the schema defined in discovery, ensuring data quality. For a 10-page PDF bid, this extraction and validation process would take under 60 seconds.

The delivered system is a serverless application running in your own AWS account. Extracted data is written directly into your project management system, populating bid leveling sheets and subcontractor compliance records automatically. You receive the full Python source code in your GitHub repository, a runbook for maintenance, and a dashboard to monitor processing volume and costs, which are typically under $150 per month. The system can handle over 500 documents per month without performance degradation.