Automate Tenant Onboarding and Screening with Custom AI

Property management companies often rely on the tenant screening modules within platforms like AppFolio or Buildium. These tools are effective for credit checks and background reports but fall short on income verification for non-W2 applicants. The built-in document parsers are rigid, designed to find gross pay on a standard pay stub. They cannot interpret a freelancer's bank statement or a small business owner's profit-and-loss statement.

Consider a 15-person property management firm that receives 200 applications a month. When an applicant with multiple gig-economy jobs submits three months of bank statements, the workflow in AppFolio breaks. A leasing agent must manually download the 15-page PDF, scan line-by-line for deposits, ignore transfers between accounts, sum the relevant income using a calculator, and then type the result back into a note field. This 25-minute manual task happens for every single non-standard applicant, creating a significant bottleneck and a high chance of data entry errors.

Some firms try to patch this with third-party verification services, but these add another subscription fee per applicant and another dashboard for your team to manage. They also introduce delays, as the verification happens outside your core property management system. The data doesn't sync back cleanly, forcing more manual copy-pasting.

The structural issue is that platforms like AppFolio are built for mass-market efficiency, not for edge cases. Their data models are fixed. You cannot teach their system your specific rule for calculating a variable income. You are forced to build a manual process around the software's limitations, turning your skilled leasing agents into low-value data entry clerks.

The first step is always a discovery audit of your current tenant screening process. Syntora would review every document type you accept, from pay stubs to offer letters to bank statements. We map out your exact business rules for income qualification, such as 'average monthly deposits over the last 3 months must be 3x the rent.' You receive a scope document that details the technical plan and confirms all logic before any code is written.

The core of the system would be a FastAPI service deployed on AWS Lambda for low-cost, high-availability processing. When a new application is submitted, your property management platform sends the document package to a secure Syntora endpoint. The Claude API reads the documents, identifies the document type, and extracts all relevant numbers, dates, and names. We've used this exact pattern to process complex financial documents in other industries, and it applies directly to rental applications. Python logic then applies your specific business rules to the extracted data, calculates the final verified income, and checks for red flags.

The delivered system posts the results directly back into your property management software via its API. A leasing agent sees a clear output, like 'Verified Income: $6,250/mo. Meets 3x Rule: Yes. Notes: Consistent deposits from Doordash.' The full source code, a deployment runbook, and a monitoring dashboard are handed over to you. The system typically costs under $30/month to operate for processing up to 500 applications.