Automate Tenant Screening with Custom AI Engineering

Most SMB property managers rely on the built-in screening tools within their property management software, like AppFolio or Buildium. These tools offer basic credit and background checks but treat applicant documents as simple attachments. They cannot read a PDF pay stub or a bank statement to verify income. This forces leasing agents to manually open dozens of files per vacancy, find the year-to-date income, and type it back into the system.

For example, consider a manager with 200 units who gets 40 applications for a desirable apartment. They have to open 40 sets of documents. One applicant submits a photo of a pay stub, another a 12-page bank statement PDF. The leasing agent spends hours on low-value data entry, increasing the risk of transcription errors that could lead to a bad placement. The process is slow, inconsistent, and does not scale during busy leasing seasons.

The structural problem is that these platforms are designed as closed ecosystems. Their business model relies on selling their own integrated screening services, not on providing open APIs for best-in-class document processing. You are locked into their pre-defined workflow. You cannot add a more advanced fraud detection step or automatically cross-reference an applicant's stated employer with their bank deposits. This architectural rigidity prevents true automation of the most time-consuming part of the screening process.

We would begin with a complete audit of your current tenant application and screening process. This involves mapping every step from initial inquiry to lease signing, identifying the specific documents you require, and cataloging the third-party services you use. The deliverable from this phase is a clear process flow diagram and a technical requirements document that defines the scope before any code is written.

The core of the system would be a FastAPI service hosted on AWS Lambda, designed to be event-driven and low-cost. When a new application is submitted in your property management software, a webhook triggers the service. The Claude API parses the attached documents, such as pay stubs and bank statements, to extract key data points like gross monthly income, employer name, and dates. We use Pydantic for data validation to ensure every piece of information is correctly formatted before it proceeds. This serverless architecture typically costs under $50/month to run for up to 10,000 processed applications.

The delivered system integrates back into your existing software. The extracted data, a calculated debt-to-income ratio, and a summary of any flagged risks are written to custom fields on the applicant's record. You receive the full Python source code in your own GitHub repository, a runbook for maintenance, and a simple monitoring dashboard built on Vercel. A typical 3-week build cycle ensures rapid deployment of a system that can generate a risk profile from over 50 data points and return a result in under 2 seconds.