Automate Your Tenant Application Workflow with Custom AI

Many small property management businesses rely on the built-in features of their PMS, like AppFolio or Buildium. These platforms offer basic screening packages, but their logic is rigid. They can run a credit check, but they cannot intelligently parse a non-standard pay stub to verify income or flag a discrepancy between the stated employer on the application and the company name on the bank statement. The workflow is fixed, preventing you from automatically adding a custom step like verifying pet references via email.

To fill the gaps, teams turn to manual portals like TransUnion SmartMove or RentPrep. This creates a painful, multi-step process. For example, consider a 5-person firm managing 200 units. An application arrives as a PDF from Zillow. The office manager must open the PDF, then manually type the applicant's name, email, and income into AppFolio. Next, they log into a separate screening portal, re-enter the data, and run the report. The screening report, another PDF, must then be downloaded and manually attached to the applicant's record in AppFolio. This is 30 minutes of low-value work per applicant, repeated over a dozen times a week.

The structural problem is that these off-the-shelf tools are siloed systems, not an integrated workflow. The PMS manages records, and the screening tool provides data, but there is no intelligent layer connecting them. They are not engineered to handle the variability of real-world documents. They cannot be taught to read a W-2 from a local company or cross-reference bank deposits against a stated salary. This forces your most valuable asset, your team's time, into the role of human API, copying and pasting data between windows.

The first step would be a complete audit of your current application process. Syntora would map every step, from the moment an application PDF hits an inbox to the final approval status updated in your PMS. This audit identifies every manual check, every data entry point, and every rule-of-thumb your team uses to qualify tenants. The deliverable from this phase is a detailed workflow diagram that becomes the blueprint for the automation.

The technical approach would center on a FastAPI service acting as the workflow engine, running on AWS Lambda for efficiency. When an application email arrives, the service triggers. It uses the Claude 3 Sonnet API to parse the application and all attachments, like pay stubs or bank statements, extracting data into a structured format defined by Pydantic schemas. This structured data is then used to make automated API calls to your screening service and PMS. This architecture is ideal because it's event-driven, costing almost nothing when idle, and processes a full application in under 60 seconds.

The delivered system would push a summary, a processing log, and a final status of 'Approved' or 'Needs Manual Review' directly into your existing PMS. A simple dashboard, built with Streamlit and hosted on Vercel, would provide a real-time view of all applications in the pipeline. You receive the full Python source code in your GitHub, a runbook explaining how to manage the system, and complete ownership with no ongoing license fees. Hosting costs are typically under $50 per month.