Deploy an AI Agent to Handle Your Inbound Support Calls

Most small businesses start with a traditional Interactive Voice Response (IVR) system. This 'press 1 for sales' approach frustrates customers because it cannot answer questions; it only routes calls. It is useless for nuanced queries like, 'I want to return an item but my order number starts with a W,' forcing every caller with a real question into a human queue.

Off-the-shelf voicebots from platforms like Twilio or Five9 seem like the next step, but they are often black boxes. Customizing their logic is difficult, and you cannot directly control the underlying language model's responses. They also charge per minute, which becomes expensive as call volume grows, punishing you for being successful.

A 20-person home services company used a basic IVR. A customer calling to reschedule had to press '2' for 'Existing Appointments' and wait for a human. The agent then spent 5 minutes gathering the customer's name and address to find the appointment in Housecall Pro. At 50 reschedule calls per day, the company lost over 4 hours of agent time just on manual data lookup.

Syntora's approach to building an AI call agent would begin with an in-depth analysis of your past call logs and support tickets. This discovery phase helps us identify the predominant customer queries and establish a core set of call drivers. This data would then be used to structure a knowledge base, typically within a Supabase Postgres database, optimized for efficient vector search. This architecture allows the AI to quickly retrieve relevant information for customer questions, such as 'what is your refund policy?'.

The core logic for the AI agent would be a Python service, often built with FastAPI. Inbound calls would be routed through an AWS Lambda function that integrates with a real-time transcription service. The transcribed text would be sent to the Claude API for intent recognition and to formulate a response using the Supabase knowledge base. Syntora has extensive experience building similar natural language processing pipelines with Claude API for document analysis and information retrieval in adjacent domains, such as financial services.

For stateful queries, like 'where is my order?', the FastAPI service would use httpx to make secure, asynchronous calls to your existing APIs, such as Shopify or an ERP system. If a customer requests to speak with a human or if the model's confidence score falls below a defined threshold, the call would be instantly transferred. For escalated calls, the human agent's screen would display a full call transcript and a summary generated by Claude, providing immediate context.

The entire system would be deployed on AWS Lambda, providing high availability and minimal server management. We would implement structlog for structured logging, pushing all interaction data to a central log store. This data would power a dashboard showing metrics like call volume and resolution rates. Syntora would typically engage in an initial 90-day period of weekly reviews to tune the knowledge base and optimize system performance. Typical monthly cloud costs for processing around 5,000 calls are estimated to be under $150.