Automate Property Maintenance Issue Tracking with AI

Most property management companies rely on the built-in maintenance tools within their Property Management System (PMS), like AppFolio or Buildium. These platforms are excellent for tracking work orders once they exist, but they do little to automate the initial, critical step: triage. A tenant's email saying "my sink is backed up again" lands in a general inbox, where it waits for a human to read it, decide if it's a plumber or a handyman job, and manually create the work order in the PMS.

Consider this scenario: at 10 PM on a Friday, a tenant emails, "There's a weird smell and some water on the floor by the water heater." To a busy property manager scanning their inbox, this might not immediately register as a critical emergency. They might flag it for Monday morning. But an AI, trained to recognize keywords like "water on the floor" and "water heater," would instantly classify it as a high-priority leak risk. This manual triage step is a significant operational bottleneck and a source of liability. The core issue is that systems like Yardi or AppFolio are databases with forms; they are not language-processing engines.

The structural problem is that off-the-shelf software is designed for manual data entry. It cannot intelligently parse the unstructured text of a tenant's message. You cannot configure a rule in Buildium that says, "If an email contains the words 'sparking' and 'outlet,' immediately text the on-call electrician." This limitation forces property managers into a reactive loop, spending hours each day reading emails, copying information, and making judgment calls that an AI could handle more quickly and consistently.

The engagement would begin with an audit of your current maintenance workflow. Syntora would map every channel where tenants submit requests—email, SMS, portal messages—and document your vendor assignment rules. This discovery phase produces a clear technical specification that defines the classification logic (e.g., plumbing vs. electrical vs. HVAC) and the exact data to be extracted. You approve this plan before any code is written.

We would build a system using a series of AWS Lambda functions written in Python. When an email or text arrives, a webhook triggers a function that passes the message content to the Claude API. A carefully engineered prompt instructs Claude to extract the unit number, issue description, tenant contact information, and urgency level, returning the data as structured JSON. Another function then uses this JSON to query a Supabase database of vendor rules and contacts, dispatching a notification to the correct professional via Twilio SMS. We've used this exact pattern to process unstructured financial documents, and it applies directly to parsing tenant communications.

The delivered system is a background process that feeds structured, triaged work orders directly into your existing PMS or a dedicated dashboard. For high-urgency issues, it can send an immediate alert to a property manager for final confirmation, creating a human-in-the-loop safeguard. You receive the complete source code, a runbook for maintenance, and a system that operates for under $50 per month in cloud hosting costs.