Build a Custom Tenant Screening System with AI

Most small firms rely on the tenant screening features inside their property management software like AppFolio or Buildium. These tools are great for running credit checks and background reports through integrated partners like TransUnion. However, they treat income verification as a manual checklist item: 'Did the applicant upload a pay stub?' The software cannot read the document to confirm the applicant's stated income actually matches the net pay.

Consider a 10-person firm managing 300 units. A leasing agent receives an application with two pay stubs and a bank statement. They must manually open each PDF, find the net pay, annualize it, and check it against the 3x rent requirement. This process takes 15-20 minutes per application and is prone to human error, especially during busy leasing seasons. If an applicant is self-employed and submits a tax return, the agent might not know which line item on a Schedule C represents verifiable income, leading to inconsistent decisions.

The problem is that platforms like Yardi and AppFolio are systems of record, not systems of intelligence. Their architecture is designed to store structured data like lease dates and rent payments, not to interpret unstructured data from PDFs and images. They offer API integrations for third-party credit bureaus, but they lack the internal document processing capabilities to automate the most time-consuming part of screening: income and employment verification. They are built to be databases with user interfaces, not data processing engines.

This manual bottleneck slows down the leasing cycle, leaving units vacant longer. It also introduces Fair Housing compliance risk. When different leasing agents interpret documents differently, it can lead to inconsistent application of screening criteria, creating the appearance of discrimination even when none is intended. The lack of automation turns a critical, risk-mitigating process into a low-wage data entry task.

The engagement would begin with an audit of your current screening workflow. Syntora would map every step, from application submission to decision, and review the document types you handle. This discovery phase produces a clear data schema and an integration plan for your existing property management software, ensuring the final system fits your process.

The core of the system would be a Python service using the Claude API for document intelligence. Claude API is particularly effective at extracting structured data from unstructured documents like pay stubs and bank statements. This service would be deployed on AWS Lambda for cost-effective, event-driven processing. A FastAPI endpoint would receive uploaded documents, send them to Claude for analysis, and use Pydantic for data validation. A single application with 3 documents would typically be processed in under 60 seconds.

The final deliverable is an automated workflow that connects to your existing application process. The system extracts financial data, flags discrepancies, and presents a summarized report to your leasing agent within your property management platform. This report highlights red flags like NSF fees, reducing review time from 20 minutes to under 2 minutes. The system can handle a peak of 50 concurrent applications, and monthly hosting costs on AWS Lambda would be under $50 for a firm processing 200 applications per month.