Stop Missing Tenant Calls. Build a Custom AI Voice Assistant.

Most property management companies start with a standard Interactive Voice Response (IVR) system from a provider like RingCentral. These systems use rigid "press 1 for maintenance, press 2 for leasing" menus. A tenant with a complex issue, like a broken heater who also needs to ask about rent, gets stuck and has to hang up. The IVR cannot understand natural language, leading to tenant frustration and abandoned calls.

To solve this, many hire a third-party answering service. These services solve the 24/7 coverage problem but introduce delays and errors. They cost between $1.50 and $3.00 per minute and can only take a message. The operator cannot access AppFolio or Buildium to see tenant history or create a work order directly. This creates a 12-hour data entry backlog for your staff the next morning.

A regional PMC with 800 units paid an answering service over $2,000 per month. A tenant called at 10 PM about a leak. The service took a message that was not flagged as urgent. The property manager saw it at 9 AM, 11 hours later. The delay turned a minor plumbing fix into a significant water damage claim because the system could not distinguish a drip from a flood.

Syntora's approach would begin with a discovery phase. We would analyze your historical call logs and maintenance tickets to identify the most frequent tenant inquiries. This data is crucial for understanding your specific operational language and common issues like "HVAC," "water heater," or "appliance repair."

We would design and build the core voice assistant logic using Python with the FastAPI framework. This application would integrate with a cloud telephony API, such as Twilio, to manage incoming and outgoing calls. The system would transcribe the caller's speech in real-time. This transcript would then be sent to a large language model API, like Claude, which is adept at identifying the caller's intent (e.g., "maintenance_request" or "leasing_inquiry"). Syntora has extensive experience building document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to handling property management inquiries.

Once the caller's intent is classified, a specific, automated action is triggered. For maintenance requests, a Python function would connect directly to your property management software's API (e.g., AppFolio or Buildium) to create a new work order, including the full call transcript. For leasing inquiries, the system could retrieve unit availability and automatically send the caller a scheduling link via SMS. The entire application would be deployed on serverless infrastructure like AWS Lambda, which is designed for cost-effective scaling.

To provide operational visibility, we would configure structured logging using `structlog`, pushing data to a Supabase database. This enables a dashboard to monitor call volume, intent classification, and any API error rates. Should a call's intent be ambiguous, the system would be designed to automatically forward it to your designated on-call emergency line and log the transcript for human review. A typical build and deployment engagement of this complexity can range from 4 to 8 weeks, depending on the depth of PMS integration and the number of distinct intent flows required. The client would need to provide access to historical call data, relevant PMS API documentation, and define key internal escalation processes. The deliverables would include a fully deployed, custom voice AI system, comprehensive documentation, and knowledge transfer to your team.