Build a Custom AI Tenant Screening Workflow

Most property management companies use the screening tools built into their PMS, like AppFolio or Buildium. These tools are rigid checklists. You can set a minimum credit score or a fixed income-to-rent ratio, but you cannot create nuanced rules that reflect your portfolio's actual risk. They cannot, for example, weigh a strong rental history against a slightly lower credit score, forcing a manual review for every edge case.

To compensate, leasing agents revert to a manual process. Consider a 10-person firm managing 300 units. An applicant submits a PDF form, which an agent then manually types into a separate service like TransUnion SmartMove. The agent downloads the report, opens the applicant's pay stub PDF to calculate income, and eyeballs everything against a mental checklist. This takes 30-45 minutes of error-prone work for every single applicant, creating a significant bottleneck.

The structural problem is that off-the-shelf PMS platforms are designed to be generic. Their screening modules use a fixed data model to serve the widest possible market, which prevents you from adding custom logic that reflects your business. You cannot inject your own code to parse a non-standard income document or query a third-party API for rental history. You are locked into their predefined workflow, forcing your most valuable team members to spend their time on low-value data entry.

The engagement would begin by mapping your exact tenant screening criteria. Syntora would work with your team to document every data point and decision rule, from initial application to final approval. We'd audit your current PMS, identifying what data is available via its API and what needs to be sourced from documents. This discovery phase produces a clear architectural plan before any code is written.

The core of the system would be a FastAPI service hosted on AWS Lambda, which is cost-effective for event-driven workloads. When an application is received, the service uses the Claude API to parse documents like pay stubs and bank statements, extracting structured data like income and employer details. Pydantic models validate this data before it is used to call credit and background check APIs. All information is then fed into a rules engine that generates a final score and recommendation based on your specific logic.

The delivered system pushes a summary report and a recommendation (approve, deny, review) into a custom field within your existing PMS. Your team works from the same interface they use today, but with the repetitive work already done. You receive the full Python source code in your GitHub, a runbook for maintenance, and a monitoring dashboard built on Vercel. Hosting costs for this architecture are typically under $50 per month.