Replace Your Answering Service with a Custom AI Agent
Small property management firms should hire agencies that provide full source code and use production-grade APIs. Key considerations include the tech stack, data ownership, and a clear maintenance plan without per-call fees.
We built a voice AI agent for a 15-person firm in Chicago managing 400 residential units. They were spending over $1,200 a month on a live answering service that frequently misclassified calls. We deployed their custom AI handler in 3 weeks, reducing after-hours calls to on-call staff by 90% and cutting their monthly cost to under $50.
The system's complexity depends on your operational specifics. A firm with a standard set of emergency protocols and a modern property management platform like AppFolio is a straightforward build. A firm with unique rules for commercial vs. residential units or integrations into a custom-built tenant portal requires more discovery and development.
What Problem Does This Solve?
Most property management firms start with a live answering service like AnswerConnect. This approach fails because agents lack property-specific context. They follow a generic script, so a burst pipe and a slow drip are both logged as 'water issue,' forcing your on-call tech to triage everything. This model is also expensive, with per-minute or per-call fees that penalize you for high call volumes during a storm or heatwave.
A common failure scenario involves a tenant calling at 2 AM about a broken HVAC unit during a heat advisory. The answering service agent, seeing no fire or flood, logs it as a non-emergency work order. The property manager wakes up to an angry tenant, a one-star Google review, and a potential liability. This happens because the answering service has no way to check local weather advisories or your specific lease agreements regarding habitability.
Off-the-shelf IVR or chatbot builders cannot solve this because they lack deep integration capabilities. They can record a message or send an email, but they cannot create a prioritized work order in Buildium, check the tenant's contact history in your database, or execute conditional logic like, 'if the call is about a water leak, ask if they are in a multi-floor building.' They are information collectors, not action-takers.
How Does It Work?
Our process begins by connecting to your property management software's API, whether it is AppFolio, Buildium, or another platform. We use httpx to build a resilient client that pulls tenant, property, and maintenance history data. This data is cached in a Supabase Postgres database, giving the AI agent the context it needs to have an intelligent conversation, for example, by confirming the tenant's unit number automatically.
We build the core voice agent using Twilio for the phone number and real-time audio streaming. The audio is fed to a FastAPI application running on AWS Lambda. Inside the application, we use Anthropic's Claude 3 Sonnet API to handle transcription, natural language understanding, and response generation. The model follows a strict, prompt-engineered chain of thought to classify the issue against your firm’s specific emergency protocols. The median response latency is under 800ms, so tenants are not left waiting in silence.
Once an issue is classified, the system takes action. A P1 emergency like a gas leak triggers an immediate API call to PagerDuty to alert the on-call technician. Simultaneously, a high-priority work order is created in your property management system with the call transcript attached. Non-emergencies (like a request for a new furnace filter) generate a standard work order for the next business day. This triage logic is written in Python and is easily adaptable as your business rules change.
For monitoring, every call and decision is logged using structlog, producing structured JSON logs that are sent to AWS CloudWatch. We configure CloudWatch Alarms to send a Slack notification if the API's error rate exceeds 2% or if call processing time surpasses 2.5 seconds. The entire infrastructure for handling 300 calls a month typically costs less than $50 in AWS fees, a fraction of the cost of a traditional answering service.
What Are the Key Benefits?
Launch in 3 Weeks, Triage in 3 Seconds
Go from project kickoff to a live, production-ready AI agent in 15 business days. The system classifies and routes emergency calls in under 3 seconds.
One-Time Build, Pennies Per Call
A single fixed-price engagement to build the system. Your ongoing costs are for the underlying APIs and AWS Lambda, typically under $0.20 per call.
You Own The Code and The Logic
We deliver the full Python source code to your GitHub repository. The triage logic and AI prompts are yours to modify as your business evolves.
Proactive Failure and Latency Alerts
We configure monitoring in AWS CloudWatch to alert you via Slack or email if the system fails or becomes slow. No more silent failures.
Direct Integration With AppFolio
The system creates work orders directly in your existing property management software. No manual data entry or switching between applications is needed.
What Does the Process Look Like?
Protocol and API Audit (Week 1)
You provide read-only API access to your property management system and a copy of your emergency protocols. We deliver a technical specification detailing the call flows.
Core Agent Build (Week 2)
We build the FastAPI service that connects Twilio and the Claude API. You receive a private phone number to test the conversation and classification logic.
Integration and Deployment (Week 3)
We connect the agent to your live property management system and escalation tools. You receive the final production phone number and system credentials.
Monitoring and Handoff (Weeks 4-8)
We monitor live call performance and fine-tune the AI prompts. At the end of the period, you receive the full source code, a runbook, and a final handoff session.
Frequently Asked Questions
- What affects the project cost and timeline?
- The main factors are the number of integrations and the complexity of your emergency rules. A standard build connecting to a platform like AppFolio with a single set of protocols takes 3-4 weeks. Needing to support multiple languages or connect to a custom-built database would increase the scope and timeline. We provide a fixed price after the initial discovery call.
- What happens if the AI misunderstands a call or the system goes down?
- If the AI's classification confidence is below 85%, the call transcript and audio are flagged for human review. If the core AWS Lambda service fails, Twilio is configured with a backup number, automatically forwarding the call to a person on your team. This ensures a human is always the ultimate fallback and no emergency call is ever dropped entirely.
- How is this different from using a service like CallRail with AI features?
- CallRail's AI provides marketing analytics, like spotting keywords in sales calls. It is a passive analysis tool. Syntora builds an active agent that performs tasks. It runs property-specific logic, decides if an issue is a true emergency based on your custom rules, creates a work order in AppFolio, and pages an on-call technician via PagerDuty.
- How is tenant data handled securely?
- The system runs in your own AWS account, giving you full control. Tenant information is pulled from your property management software's API in real-time for each call and is not stored long-term by the agent. All API keys and secrets are encrypted and managed in AWS Secrets Manager, not in the code. We architect for data minimization.
- Can the AI handle our firm’s unique emergency definitions?
- Yes, this is the system's primary purpose. We convert your existing PDF or Word document of emergency protocols into a detailed prompt for the Claude API. The AI learns your specific rules, like a broken AC being an emergency only when the outside temperature is above 80 degrees, or that specific keywords require an immediate escalation.
- What does ongoing maintenance involve after the handoff?
- The system is designed for low maintenance. The main task is updating your emergency protocols in a Supabase table if your policies change. The provided runbook documents this process. We also recommend a brief prompt-tuning session every 6-12 months using recent call data to ensure continued accuracy, which is a two-hour task a developer can perform.
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