Build an AI System to Track Subcontractor Performance

Most small construction firms rely on a combination of project management software like Procore or Autodesk Construction Cloud and accounting software like QuickBooks. These tools are excellent systems of record, but they do not communicate intelligently. Procore can store daily logs and photos of job site progress, but it cannot automatically read a subcontractor's PDF invoice and validate the line items against that visual evidence.

In practice, this forces manual, error-prone work. Consider a project manager at a 15-person general contracting firm. They receive a payment application from their HVAC subcontractor claiming 50% completion. To verify this, the PM must open the PDF, then find the corresponding daily logs in Procore, sift through photos sent via email, and cross-reference the original contract terms. This process takes 30-45 minutes per application. With 12 subcontractors on a single job, that is over a full day of administrative work every month.

The structural problem is that off-the-shelf software operates in silos. An integration tool can push an invoice from one system to another, but it cannot understand the content of that invoice. It cannot read the 'linear feet of ductwork installed' line item, compare it to a daily log entry, and flag a 15% discrepancy. Solving this requires custom logic that can parse domain-specific documents and enforce your specific business rules, something platform-based tools are not designed to do.

We would start by auditing your current subcontractor payment workflow. The first step is to collect document samples, including payment applications (like AIA G702/G703), change orders, and daily reports from at least 5 different subcontractors. This audit identifies the critical data points that need to be extracted from each document and defines the rules for automated verification. You receive a clear map of the proposed logic before any code is written.

The technical core of the system would be a document processing pipeline built with Python and deployed on AWS Lambda for cost-effective, event-driven processing. We would use the Claude API to parse unstructured text, tables, and figures from PDFs and scanned documents. A simple FastAPI service would provide an endpoint for your team to upload new documents or for the system to ingest them from an email inbox. We have built similar Claude API document extraction systems for financial documents, and the same pattern of identifying and extracting key-value pairs applies directly to construction payment applications.

The delivered system integrates directly into your existing software. When an invoice is processed, the system extracts the line items, compares them to progress documented in your project management tool, and generates a verification report in seconds. A project manager gets a summary of findings with any discrepancies clearly flagged, allowing them to approve or reject the payment in under 2 minutes. Once approved, the system can automatically create the corresponding bill in QuickBooks.