Automate Your Tenant Screening Workflow with Custom AI

Most small property managers use the built-in screening tools in platforms like AppFolio or Buildium. These tools are effective for running a one-click credit check, but they fall short on comprehensive income verification. The systems cannot read a PDF pay stub or a bank statement, forcing property managers to manually calculate debt-to-income ratios and verify employment history themselves.

Consider a manager with 50 units who receives 10 applications for one apartment. Each applicant submits a PDF application, two recent pay stubs, and a bank statement. The manager opens 40 separate files, manually typing names and employer details, then uses a calculator to verify that each applicant's stated income matches their documents. This takes 15-20 minutes per applicant, assuming no discrepancies are found.

When a discrepancy does appear, like a pay stub showing a different employer than the application, the built-in software does not flag it. The background check runs on the submitted data only. The manager must now pause the process to email the applicant for clarification, adding days to the screening timeline while other qualified candidates find other housing.

The structural problem is that all-in-one property management platforms are designed for breadth, not depth. They cannot invest in a sophisticated document intelligence feature because it serves only one part of their user base. Their architecture is not designed to interpret unstructured data from varied sources like a scanned W-2 or a screenshot of a bank statement.

Syntora would start with an audit of your current tenant screening workflow. We would map out every step, from how you receive applications to the specific criteria you use for approval. This discovery process defines your exact decision logic, including income requirements, credit score thresholds, and red flags from eviction history.

The core of the system would be an AI document processing pipeline built with Python and the Claude API. When an application package arrives, an AWS Lambda function sends the PDFs to the Claude API for data extraction, achieving over 99% accuracy. This structured data is then used to automatically call credit and background check APIs like TransUnion or Checkr. The entire build would take 3-4 weeks.

The final deliverable is a simple web dashboard where you see a verified summary for each applicant, ranked by your custom criteria. The system flags discrepancies automatically, showing you exactly where an application and a bank statement differ. Hosting costs on AWS are typically under $50 per month, and the system processes a full application in under 90 seconds.