Automate Subcontractor Tracking and Payment Processing with Custom AI

Most general contractors use Procore or Autodesk Construction Cloud as a central repository. These platforms are excellent for collecting data, but they lack the intelligence to analyze it. A project manager can see a subcontractor's daily log, but the platform cannot automatically read the text to flag that the crew was on-site for only 4 hours or that critical materials failed to arrive. The verification is still a manual, human task.

In practice, this creates a major bottleneck in the payment cycle. Consider a 25-person general contractor managing 15 projects. A plumbing subcontractor submits a pay application for 40% completion. The project manager must now manually hunt for daily logs in Procore, cross-reference them with site photos sent via email, and check the original contract terms stored in a separate folder to verify the claim. This manual process takes hours per application, delays payments, and strains subcontractor relationships.

The accounting side, often managed in QuickBooks, is completely disconnected from field progress. An accounts payable clerk sees an approved invoice but has no way to independently verify if the '50% framing complete' milestone was actually met. They rely entirely on the PM's approval, which can lead to overpayments if a busy PM rubber-stamps an application without a thorough check.

The structural problem is that project management software is built as a system of record, not a system of intelligence. Accounting software is built for ledgers, not physical progress. The architectural gap between these systems forces manual reconciliation. No off-the-shelf integration can read unstructured text in a daily log, semantically compare it to line items on a pay application, and flag a discrepancy. This requires a custom-built processing layer that understands construction-specific documents.

The first step is an audit of your current documentation flow. Syntora would review your standard subcontractor agreement, typical pay application format, and daily log templates. We map how data moves from the field (photos, notes) to your project management system and finally to accounting. This audit produces a data flow diagram and identifies the key data points needed for automated verification.

The technical approach uses a Python service running on AWS Lambda, triggered whenever a new document is uploaded. The service uses the Claude API to parse unstructured text from daily logs and PDF invoices, extracting milestones, completion percentages, and quantities. We've built similar document processing pipelines for financial services firms. A FastAPI service would provide an endpoint for an at-a-glance dashboard, and Pydantic schemas would ensure all extracted data is validated before storage in a Supabase database. An entire 5-page PDF can be processed in under 5 seconds.

The delivered system pushes a 'Verification Status' (e.g., Verified, Flagged) into a custom field within your existing project management tool. A flagged invoice includes a specific reason, like 'Daily logs show 35% completion, but invoice claims 50%.' Your team works in their familiar software. You receive all source code and a system hosted on your own cloud account. A typical build takes 4-6 weeks and can process over 500 documents per day for a hosting cost under $50/month.