Automate Rent Collection and Reconciliation with Custom AI

Property management platforms like AppFolio and Buildium offer basic payment matching. This works when a tenant pays through the portal, creating a clean transaction. The system fails with outside payments. If a tenant's Zelle name is 'Jenny B' but their lease is under 'Jennifer Brown', the default matching logic breaks. The software cannot connect the two, forcing manual intervention.

Consider a 50-unit portfolio at the beginning of the month. Forty-five payments reconcile automatically. But five are exceptions. One is a paper check deposited by a tenant with the memo 'For Apt 3C'. Another is a Zelle payment from 'Cool Designs LLC' for a tenant who runs a business. A third is a partial payment of $1200 on an $1800 rent. Each exception forces the property manager to open bank statements and accounting software side-by-side to manually investigate. This is easily 10 hours of low-value work every month.

The structural problem is that these platforms are designed as all-in-one systems of record, not flexible data processing engines. Their APIs allow for basic data synchronization but not for injecting complex, custom logic. You cannot create a rule that says 'If a payment from Cool Designs LLC arrives, associate it with John Smith at 123 Elm St'. The systems lack the Natural Language Processing needed to understand the immense variety of real-world bank memos.

The result is a recurring bottleneck. The property manager or bookkeeper spends the first week of every month chasing these mismatches. Financial reports for owners are delayed pending reconciliation. This process creates a constant risk of misapplying a payment, which leads to difficult tenant conversations and erodes trust.

The first step is a workflow audit. Syntora would map your current reconciliation process, identifying every bank account, payment type, and the API capabilities of your existing property management software. This discovery phase produces a data flow diagram that shows exactly how payments will be ingested, parsed, matched, and recorded. You approve this plan before any development begins.

The core of the system would be an AWS Lambda function, written in Python, that runs on a schedule. The function would use a service like Plaid to securely fetch new bank transactions. For each transaction, the Claude API parses the unstructured description to extract potential tenant names, addresses, or unit numbers. This structured data is then compared against a tenant roster from your property management system, which is cached in a Supabase database for fast lookups. The system uses fuzzy string matching to handle name variations like 'Jon Smith' vs 'Jonathan Smith'.

The delivered system runs automatically every morning at 7 AM. It posts matched payments directly to tenant ledgers in your property management software via its API. The property manager receives a daily email summary: '48 payments automatically reconciled, 2 require review.' For the two exceptions, you would log into a simple dashboard, hosted on Vercel, to approve the system's suggested match. This turns a multi-hour manual task into a 5-minute daily check.