Build a Custom Voice AI for Tenant Complaint Triage
Hire an agency that builds custom Python systems using speech-to-text APIs and an LLM for triage. The system transcribes voicemails, categorizes the issue, and creates a ticket in your property management software.
A build's scope depends on your operational complexity. A firm with one phone line, standard issue types, and a single property management system (PMS) like AppFolio is a straightforward project. Integrating multiple phone systems or adding logic for sentiment analysis requires a more detailed discovery phase.
We built a system for a 15-person property management firm handling 400 units that received 60 tenant voicemails daily. Our system, deployed in 3 weeks, now processes 95% of these calls without human review. This reduced the average ticket creation time from 5 minutes to under 30 seconds.
What Problem Does This Solve?
Property management teams often start with their phone provider's standard voicemail, like RingCentral. This forces a 100% manual review process where a manager must listen to every message, decipher the issue, and manually create a ticket. This is slow, prone to data entry errors, and messages about urgent issues like leaks can sit for hours.
Off-the-shelf 'conversation intelligence' tools like CallRail can transcribe calls and tag keywords, but the logic is brittle. The system might tag 'leaky faucet' but miss a tenant saying 'water is dripping all over my kitchen counter'. A frantic tenant describing a leak near an electrical outlet gets the same medium-priority tag as a dripping sink because the system cannot infer contextual urgency.
You cannot solve this with a no-code tool. A Zapier workflow connecting a transcription service to your PMS gets expensive. A single voicemail burns 4-5 tasks: transcribe, summarize with an LLM, categorize, and create a ticket. At 60 voicemails per day, that's over 8,000 tasks and a bill exceeding $300 a month for one fragile workflow with no sophisticated error handling.
How Does It Work?
First, we ingest voicemails directly from your phone system's API, such as Twilio. New audio files are saved to an AWS S3 bucket. An AWS Lambda function triggers on each new file and sends it to a speech-to-text service for transcription. A typical 60-second voicemail is transcribed with word-level timestamps in under 15 seconds.
The resulting text is passed to the Claude 3 Sonnet API. We use a detailed prompt that instructs the model to act as an expert property management dispatcher. It extracts the property address, unit number, tenant name, and all distinct issues. It then categorizes each issue (e.g., Maintenance-Urgent, Billing-Inquiry) and assigns a priority level based on the content. This LLM call completes in under 2 seconds.
Next, the structured data from Claude is used to perform actions via a FastAPI service. For a multi-issue call, the system creates separate, correctly routed tickets in your PMS using its native API. We write custom Python clients with httpx for direct integration with AppFolio, Buildium, or Yardi. The API interaction to create a ticket and get a confirmation ID takes less than 200ms.
Every step of the process is logged to a Supabase database, creating a complete audit trail. We include a simple dashboard showing processing volumes, common complaint types, and any voicemails that failed. Failures trigger a Slack alert with the audio file and transcription for manual review. Monthly hosting costs for this entire infrastructure are typically under $50.
What Are the Key Benefits?
Go from Voicemail to Ticket in 30 Seconds
Your property managers stop listening to messages and start solving problems. The system creates a categorized, prioritized ticket in your PMS before a human could even open their inbox.
One-Time Build Cost, Not Per-Minute Fees
A single fixed-price engagement with predictable, low monthly hosting costs on AWS. This avoids the variable fees of SaaS platforms that charge per call or per minute.
You Get the Full Source Code
We deliver the complete Python codebase to your company's GitHub repository. You are not locked into a vendor and have full control over the system's future.
Alerts for Calls That Need a Human
Get a Slack notification for the 5% of calls the AI can't confidently parse. The alert includes the audio and transcription for immediate manual intervention.
Direct Integration With Your PMS
We build direct API connections to AppFolio, Buildium, and Yardi. Tickets are created natively with the correct priority and assignment, eliminating all manual data entry.
What Does the Process Look Like?
System Scoping (Week 1)
You provide read-only API access to your phone system and PMS. We map your ticket categories and routing rules into a system design document that you approve before any code is written.
Core Engine Build (Week 2)
We build the transcription and categorization logic using Python and the Claude API. You receive a test script to submit sample audio files and verify the categorized JSON output.
Integration and Deployment (Week 3)
We connect the engine to your PMS API and deploy the entire system on AWS Lambda. You receive credentials to a live staging environment for end-to-end testing.
Monitoring and Handoff (Week 4+)
The system goes live. We monitor performance and accuracy for two weeks, then hand off the system to you with a runbook detailing the architecture, monitoring, and alert handling.
Frequently Asked Questions
- How much does this type of AI automation project cost?
- Pricing depends on the number of integrations and the complexity of your routing logic. A system for one phone line connecting to one PMS like AppFolio is a standard 3-week build. Adding features like multi-language support adds to the scope. We provide a fixed-price quote after a discovery call where we review your exact needs.
- What happens if our property management software API is down?
- The system uses an AWS SQS dead-letter queue. If a PMS API call fails after three automated retries, the ticket data is sent to this queue and an alert is sent to Slack. When your PMS is back online, we can re-process the failed jobs from the queue with a single command. No tenant messages are lost due to temporary outages.
- How is this different from using a 24/7 answering service?
- Answering services use humans, which brings high variable costs, delays, and errors, especially after hours. They follow a rigid script and cannot dynamically create tickets with urgency. Our system runs instantly for a fixed cost and uses an LLM to understand nuance directly from the tenant's words, not just basic keywords.
- How accurate is the transcription and issue categorization?
- Modern speech-to-text APIs achieve over 95% word accuracy for clear audio. The issue categorization accuracy is typically above 90% for routing to the correct department. We fine-tune the Claude API prompt during the build using recordings of your actual tenant voicemails to maximize its accuracy for your specific complaint types.
- Does this system handle languages other than English?
- The base system is designed for North American English. While the underlying services support multiple languages, the triage logic requires language-specific prompt engineering and testing. Supporting another language like Spanish would be scoped as an additional component in the project. We can test transcription accuracy with your sample audio files during discovery.
- How is sensitive tenant information like names and unit numbers handled?
- We design the system to minimize PII storage. Audio files in the AWS S3 bucket can be automatically deleted after successful processing. This leaves only the anonymized transcription and categorized data in your Supabase logs for analysis. All data in transit uses TLS encryption, and you retain full control over your data and infrastructure.
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