Build a Voice AI Agent to Answer Insurance Policy Questions
The best voice AI platform for policy questions is a custom-built agent using a speech-to-text API. It connects directly to your agency management system for instant, accurate answers.
The project scope depends on your Agency Management System (AMS). An agency with a modern, API-accessible AMS is a straightforward build. A firm using a legacy desktop application without an API requires building a knowledge base from policy documents, which limits the agent to general, non-customer-specific questions.
We built a voice agent for a regional insurance agency with 8 agents handling over 300 policy calls per week. The system went live in 4 weeks and now automates 70% of their inbound tier-one questions, like coverage checks and payment status, freeing agents for complex claims and new business.
What Problem Does This Solve?
Most agencies first try to solve this with their phone system's Interactive Voice Response (IVR). Standard IVRs use rigid, menu-based trees ('press 1 for sales'). They cannot understand natural language or answer a dynamic question like, 'Am I covered for a cracked windshield?'. They only route calls, often after frustrating the customer, which increases the agent's workload, not decreases it.
Next, they might try an off-the-shelf voicebot from a CRM or marketing platform. These tools are good at answering general FAQs from a pre-written script but fail the moment a customer asks a question about their specific policy. The bot can say 'our auto policies often include glass coverage,' but it cannot access your AMS to confirm if that specific customer's policy does. This lack of deep system integration means any important call gets escalated to a human.
A 12-person agency we worked with had this exact issue. A customer called asking if their homeowners policy covered a tree falling on their fence. The bot gave a generic answer about 'Acts of God' and coverage riders. The customer had to be transferred to an agent who then spent 3 minutes logging into the AMS, searching the policy number, and reading the relevant clause. The bot added a step instead of removing one.
How Does It Work?
Our process starts by connecting to your telephony provider (like Twilio) and getting read-only API access to your Agency Management System. We analyze the last 90 days of call logs and support tickets to identify the 5-10 most common and time-consuming policy questions your agents handle.
We build the core agent as a Python service using FastAPI, deployed on AWS Lambda for efficiency. When a call comes in, we use a service like Deepgram for real-time speech-to-text transcription with latency under 2 seconds. The transcribed text is sent to the Claude 3 Sonnet API, which performs intent recognition to classify the caller's question (e.g., 'coverage_check', 'billing_inquiry', 'update_address').
Once the intent is known, the FastAPI service queries your AMS API for the caller's specific policy details. The relevant text from the policy document is passed as context to a second Claude API call, which generates a precise, natural language answer. This answer is converted back to audio using a text-to-speech API and delivered to the customer. The entire interaction, from question to answer, takes less than 5 seconds.
If the AI's confidence in an answer is below 95% or the caller says 'speak to an agent,' the system automatically transfers the call to the live agent queue. Every transcript and AI response is logged in a Supabase database. This provides a clear audit trail and a data source for identifying new question types to automate, ensuring the system improves over time.
What Are the Key Benefits?
Answer Policy Questions in 5 Seconds
Customers get instant, accurate answers 24/7 without waiting on hold. The entire process from speech recognition to final audio response completes in under 5 seconds.
Pay for Usage, Not Per Agent Seat
A one-time build cost and minimal monthly cloud fees, typically under $50. This avoids the expensive per-seat licenses of large contact center platforms.
You Own the AI and its Knowledge
We deliver the complete Python source code to your GitHub. As your policies change, the system can be updated by any developer without vendor dependency.
Self-Logging for Continuous Improvement
Every call transcript and AI response is logged to a Supabase table. You can review failed queries to identify new question types to support.
Connects Directly to Your AMS
The agent pulls real-time data from your existing Agency Management System. It works with modern AMS platforms like Vertafore, Applied Epic, or any system with an API.
What Does the Process Look Like?
Discovery & Call Log Analysis (Week 1)
You provide read-only access to your telephony system and 90 days of call logs. We identify the top 5-10 repetitive policy questions to target for automation.
Core Agent Build (Week 2)
We build the core FastAPI service and integrate the speech-to-text and language models. You receive a private phone number to test the agent's conversational abilities.
AMS Integration & Testing (Week 3)
We connect the agent to your Agency Management System API to pull live policy data. You receive a test environment to validate answers against real customer scenarios.
Launch & Monitoring (Week 4+)
We deploy the system to production and monitor its performance for 30 days. You receive the full source code, documentation, and a runbook for ongoing maintenance.
Frequently Asked Questions
- What factors determine the cost and timeline for this build?
- The primary factor is your Agency Management System's API. A modern REST API allows for a 3-4 week build. Older systems requiring custom data extraction might extend the timeline. The number of distinct policy question types we need to handle also affects scope. We define this in the first week of discovery.
- What happens when the Voice AI misunderstands or can't answer a question?
- The system is designed to fail gracefully. If the AI's confidence score for an answer is below 95%, or if a user says 'human' or 'agent' twice, the call is automatically transferred to your main agent queue. The full transcript of the failed interaction is flagged for review in the Supabase log.
- How is this different from a platform like Five9 or Talkdesk?
- Five9 and Talkdesk are comprehensive contact center platforms with per-agent, per-month pricing. They are designed for managing human teams. Syntora builds a single-purpose AI agent that handles a specific, high-volume task. It's a targeted surgical build, not a replacement for your entire phone system or call center software.
- How is sensitive customer policy information handled?
- Customer data is never stored long-term by our system. The AI agent fetches policy details from your AMS in real-time for each call, uses it to generate an answer, and then discards it. All data in transit is encrypted. The system logs metadata (call time, question type) but not personally identifiable information.
- Can the system handle different accents and languages?
- Yes. The speech-to-text models we use, like Deepgram, are trained on thousands of hours of audio and perform well with a wide variety of accents. For multilingual support, we can configure the system to detect the caller's language and respond accordingly, though this adds complexity and time to the initial build.
- What if our Agency Management System is an old desktop app with no API?
- This is a significant constraint. In these cases, we build a knowledge base from your standard policy documents. The agent can answer general questions but cannot look up customer-specific details. It functions more like an interactive FAQ. This is less powerful than a fully integrated agent but can still offload many common calls.
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