Build AI Workflows for Tenant Onboarding and Screening

Most property management companies rely on the screening features built into their Property Management System (PMS), like AppFolio or Buildium. These tools are great for running basic credit and criminal background checks but falter with the documents themselves. The workflow for verifying income and application details remains almost entirely manual, creating a significant bottleneck.

Consider a 20-person firm managing 500 residential units. During peak leasing season, they might get 30 applications a day. For each one, a leasing agent manually opens a PDF application, two pay stubs, and a bank statement. They spend 20 minutes transcribing names and employer details into the PMS, calculating income from the pay stubs, and scanning the bank statement for red flags. A single typo in a Social Security Number means re-running a costly background check.

This manual process is not just slow; it is inconsistent and prone to error. One agent might approve a candidate with fluctuating income while another rejects them. Good applicants get frustrated by multi-day delays and accept offers from competitors with faster processes. The bottleneck is the human effort required to bridge the gap between unstructured documents and the structured data fields in your PMS.

The structural problem is that systems like AppFolio and Yardi are designed as databases, not intelligent document processors. Their architecture prioritizes data storage and reporting over flexible, AI-driven workflows. They cannot natively ingest a non-standard pay stub, extract the year-to-date income, and cross-reference the employer name with the application form. You are forced to perform this high-volume, low-value work by hand.

The engagement starts with a discovery audit of your current tenant screening workflow. Syntora would map every step, from the moment an application hits your inbox to the final decision. We would analyze your application forms, the different types of income verification you receive, and the specific rules your most experienced leasing agents use to approve or deny candidates. This audit produces a clear blueprint for the automation.

The technical approach would involve a serverless document processing pipeline. An AWS Lambda function would trigger whenever a new application email is received. This function would use the Claude API to read and extract data from all attached documents, converting PDF applications and pay stubs into structured JSON in about 15 seconds. Pydantic data models would then validate this extracted information, ensuring every required field is present and correctly formatted before it moves to the decision engine.

The core logic would be a FastAPI service that applies your custom screening criteria (e.g., income is at least 3x the rent, credit score above 650). The system generates a clear 'Approve', 'Reject', or 'Flag for Review' recommendation, which is then pushed directly into your PMS. The entire process, from receiving the email to updating your system, would complete in under 60 seconds. You receive the full Python source code, a deployment runbook, and a dashboard to monitor accuracy, which we target to keep above 99%.