Automate Tenant Screening with Custom AI
Yes, AI can fully automate tenant screening for property management companies. It processes applications, verifies documents, and flags risks in under two minutes.
The system's complexity depends on the number of data sources and the specific risk factors you check. Integrating with a single property management platform like AppFolio and a credit bureau is a standard build. Pulling from multiple MLS sources and public records requires more extensive data mapping.
We built a screening system for a 15-person property management firm handling 800 residential units. They processed 100 applications a month, with each taking a leasing agent 45 minutes of manual work. The AI workflow went live in four weeks, reducing screening time to under 90 seconds per applicant.
What Problem Does This Solve?
Property management platforms like Buildium offer built-in screening, but their rule engines are rigid. You can set a minimum credit score, but you cannot create a nuanced rule like "flag applicants with a credit score below 650 unless their income-to-rent ratio is above 4x." This forces leasing agents to manually review every borderline case, defeating the purpose of automation.
A property manager with 500 units tried using Jotform to pre-screen applicants, connected to a Google Sheet via a webhook. But verifying income required the agent to manually open uploaded pay stubs, find the gross pay, and calculate the monthly total. This step alone took 10-15 minutes per application. When they received 20 applications for a single vacancy, one agent lost an entire afternoon just verifying income documents.
These pre-built tools cannot process unstructured data. A pay stub, a bank statement, or a prior landlord reference letter is just a PDF file to them. They can check boxes and run credit scores, but they cannot read and understand the contents of documents. This is the manual bottleneck that only custom AI systems can solve because they require document-parsing models.
How Does It Work?
We connect to your property management system's API (AppFolio, RentManager) and ingest the applicant data. For document processing, we use the Claude API's vision capabilities to extract key-value pairs from uploaded pay stubs and bank statements. The system identifies applicant name, employer, pay period, and gross income with over 98% accuracy from PDFs and JPEGs.
We build the screening logic as a Python service using FastAPI. This service takes applicant data and extracted document information as input. It runs a series of checks: calculates income-to-rent ratio, verifies employment history, and flags custom risks you define. For a client managing student housing, we built a check to verify guarantor income, processing 2 documents per applicant in under 3 seconds. The entire workflow is deployed as a serverless function on AWS Lambda.
The FastAPI endpoint is triggered by a webhook from your application source. Once the screening completes (typically in under 1500ms), the result is pushed back into your property management platform. We write a concise summary ('Approved,' 'Denied') and detailed notes ('Income verified. Credit 680. Flag: one late payment in 12 months.') to a custom field. Results are also logged to a Supabase table for historical analysis.
The whole system costs under $40 per month to run on AWS for up to 500 screenings. We use structlog for structured JSON logging, which feeds into AWS CloudWatch. If the Claude API fails to parse a document or an API key expires, CloudWatch triggers an alarm that sends a Slack notification, ensuring any issue is addressed within minutes.
What Are the Key Benefits?
Screen Applicants in 90 Seconds, Not 45 Minutes
Our system processes an entire application, including document verification, in under two minutes. Free up your leasing agents to focus on showings and resident relations.
Pay for a Build, Not Per Screening
A single project cost replaces variable per-applicant fees from third-party services. Your AWS hosting costs remain low and predictable, regardless of application volume.
You Own the Code and the Logic
You get the full Python source code in your private GitHub repository. Your custom screening rules are yours to modify, not locked in a vendor's system.
Alerts for Failures, Not Guesswork
We configure AWS CloudWatch alerts that notify us via Slack if a screening fails. You know instantly if a document is unreadable or an API is down.
Works with AppFolio, Buildium, and More
The system integrates directly with your existing property management software using webhooks and APIs. No new dashboards for your team to learn.
What Does the Process Look Like?
Week 1: Scoping and Access
You provide your current screening checklist and grant read-only API access to your property management software. We map the entire workflow and confirm data points.
Weeks 2-3: Core System Build
We write the Python code for document parsing and rule-based decisioning. You receive a link to a staging environment to test with sample applications.
Week 4: Integration and Go-Live
We connect the system to your live application source and property management platform. We monitor the first 30 live screenings side-by-side with your team.
Weeks 5-8: Monitoring and Handoff
We monitor system performance and parsing accuracy for 30 days post-launch. You receive a runbook, full documentation, and the complete source code.
Frequently Asked Questions
- How much does a custom tenant screening system cost?
- Pricing depends on the number of integrations and the complexity of your screening rules. A system for a single property management platform with standard income and credit checks is a 4-week build. Integrating multiple data sources or custom fraud detection models adds to the scope. We provide a fixed-price quote after our initial discovery call.
- What happens if an applicant's pay stub is unreadable?
- The system is designed to fail gracefully. If the Claude API cannot extract income data with high confidence after two attempts, it flags the application for manual review. Your leasing agent receives a notification in their primary system with a direct link to the document, so they only handle exceptions, not every single application.
- How is this different from using the screening built into AppFolio?
- AppFolio's screening is a good starting point for credit and background checks, but it cannot read documents. It treats a pay stub as a simple file attachment. Our system uses AI to read the PDF, verify the income matches the application, and run custom calculations, eliminating the most time-consuming manual step in the process.
- What about applicant data privacy and security?
- We build on your own AWS account, so you control the data. Applicant information is processed in-memory on AWS Lambda and is not stored long-term, except for the final decision log in your Supabase database. All data is encrypted in transit and at rest. We can sign a DPA to help you navigate compliance requirements.
- How accurate is the AI at reading documents?
- Our systems using the Claude API typically achieve over 98% accuracy on standard pay stubs and bank statements. For unusual formats, accuracy might be lower on the first try. The system flags any low-confidence extractions for manual verification, and we can fine-tune the prompts to handle specific document layouts from large local employers.
- What kind of support is available after the 30-day monitoring period?
- We offer a simple monthly retainer for ongoing support and maintenance. This covers bug fixes, dependency updates, and minor adjustments to your screening rules. For major new features, like adding a new integration, we scope that as a separate project. The goal is a system that runs reliably without constant intervention.
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