Automate Tenant Background Checks with a Custom AI System

Small property management firms often rely on the built-in screening tools within their Property Management Software (PMS) like AppFolio or Buildium. These tools are a start, but they offer a rigid, one-size-fits-all checklist. They cannot be configured to handle a firm's specific risk criteria, such as nuanced income verification for a freelance applicant with bank statements instead of W-2s.

The process remains highly manual. A property manager receives an application PDF via email, then must re-type the applicant's name, SSN, and employment history into the PMS to initiate the check. A single typo can invalidate the entire report, wasting time and money. The manager then has to manually synthesize data from multiple sources: the credit report from TransUnion, the application PDF itself, and maybe a separate search on a county court website for eviction records. This context-switching between tabs is inefficient and creates opportunities for error.

Consider a firm managing 300 units that receives 10 applications for a desirable property. One property manager now has to spend hours on repetitive data entry and document review. For an applicant with non-standard income documents, the built-in screener fails. The manager must manually calculate the average income from 3 months of bank statements, a low-value task that takes 20 minutes and must be done perfectly every time.

The structural issue is that PMS platforms are built for generic workflows, not custom logic. Their screening modules are designed as simple pass-throughs to credit bureaus. They lack the architecture to parse unstructured documents, connect to multiple niche data sources in parallel, or apply a firm's proprietary risk-scoring model. A business-critical workflow is stuck being performed by hand because off-the-shelf tools cannot support it.

The engagement would begin with an audit of your current tenant screening process. Syntora maps every document you collect (applications, pay stubs, bank statements) and every data source you check (credit bureaus, public records). We've built document processing pipelines using the Claude API for financial services, and the same pattern applies here. This audit defines the data extraction rules and the logic for flagging applications for review. You receive a detailed workflow diagram before the build begins.

The technical approach uses a Python service running on AWS Lambda for event-driven processing, which keeps hosting costs low (typically under $20/month). When a new application PDF arrives, the service uses the Claude API to parse and extract key data points. Claude's large context window is ideal for accurately processing multi-page, unstructured documents like bank statements. This structured data is then used to query APIs for credit reports and background checks. A FastAPI interface would wrap the service to allow for manual uploads if needed.

The delivered system integrates into your existing workflow. Forwarding an application email to a dedicated address would trigger the check automatically. The results, including a risk summary and extracted data points, would be posted as a note in the applicant's record in your PMS. You receive the full source code, a runbook for maintenance, and a simple dashboard to monitor processing volumes and success rates.