Build a Custom AI for Rent Collection & Reconciliation

Most property management software like AppFolio or Yardi have built-in payment processing, but their reconciliation tools fail when payments arrive from outside their native portals. A tenant might pay via Zelle, a direct bank transfer, or a physical check. These payments appear as cryptic lines on a bank statement, like "ACH Transfer from J. Smith," with no unit number. The PMS cannot automatically match this to the open charge for unit 4B, forcing your 12-person accounting team to manually investigate dozens of exceptions.

In practice, your team downloads a CSV from the bank with 1,500 transactions. Most match, but 300 are ambiguous. One says "Zelle Payment from 555-123-4567." An accountant spends 5 minutes looking up the phone number in the tenant directory to manually apply the payment. Another is a wire transfer from an LLC for a commercial tenant, but the amount is wrong. The team spends hours on calls to sort these out.

The structural problem is that PMS platforms are closed ecosystems. Their reconciliation tools are designed only for payments made through their own gateways. They are not built to intelligently parse external, unstructured data from bank statements. Their architecture lacks the flexible matching logic required to handle the real-world messiness of how tenants actually pay. They are rigid databases, not intelligent document processing systems.

This manual work introduces a 3-5 day lag in closing the books each month and leads to errors like misapplied payments. The cost is not just the 40+ hours of accounting time; it is the delayed financial reporting and the friction with tenants caused by inaccurate records.

The first step would be an audit of your payment sources and reconciliation workflow. Syntora would review 3 months of bank statements, payment processor reports, and your chart of accounts in your PMS. The goal is to map every payment type to a reconciliation rule. You would receive a scope document detailing the matching logic, from simple name and unit matching to more complex heuristics for partial payments.

The core of the solution would be a Python service running on AWS Lambda. When a new bank statement is available, the service uses the Claude API to parse each transaction line, extracting the sender, amount, and date. The service then queries your PMS API and a Supabase database of known tenant aliases to find a match. A FastAPI service would expose an endpoint for a simple web interface where accountants can review low-confidence matches. A 1,500-line statement would be processed in under 90 seconds.

The delivered system is a secure, automated pipeline that runs on a daily schedule. It posts high-confidence matches directly to your PMS and flags exceptions in a simple dashboard for your team to review. You receive the full Python source code, a runbook for maintenance, and the cloud infrastructure costs less than $50/month to run.