Build an AI Agent to Handle Your Inbound Support Calls
AI agents use speech-to-text to transcribe calls in real time and understand customer intent. They answer common questions instantly and escalate complex issues to a human with a full summary.
Syntora designs AI agents that use real-time speech-to-text and large language models like Claude API to handle inbound customer support calls. These agents can understand customer intent, answer common questions, and escalate complex issues with detailed summaries to human staff. The technical architecture for such a system involves a custom FastAPI application integrating transcription services and backend systems.
The system's complexity depends on the number of backend integrations required. An agent designed to answer questions solely from a knowledge base is a more straightforward build. One that needs to look up order statuses in an e-commerce platform like Shopify and reschedule appointments in a booking system like Calendly requires more intricate API work and business logic.
What Problem Does This Solve?
Most small businesses start with a basic Interactive Voice Response (IVR) system. These rigid phone trees force customers into predefined menus, failing to understand natural language. A customer saying "I want to know where my package is" gets stuck in a loop if there is no "Track Order" option, leading to hang-ups and frustration.
Trying to solve this, some teams use text-based chatbot platforms for voice, but this approach is flawed. These tools rely on brittle, rule-based logic that breaks when a customer interrupts or asks two questions at once. For example, a query like "What is your return policy, and can I also use a gift card?" forces an immediate, context-less handoff to a human who has to start the conversation over.
A 12-person home services company used a phone tree that required callers to navigate three menus just to reschedule. This system could not connect to their scheduling software, so the owner spent hours each day returning voicemails. This manual process introduced booking errors and delayed responses to urgent customer needs.
How Would Syntora Approach This?
Syntora would start by auditing your existing support data, including recent support tickets, call recordings, or relevant knowledge base documents. We would use the Claude API to identify and categorize the most frequent customer intents, such as 'track order,' 'request refund,' or 'book appointment.' This foundational analysis helps us map the agent's knowledge base and define specific interaction flows.
A custom Python application would be engineered using the FastAPI framework to manage the agent's core logic and API integrations. We would integrate a real-time speech-to-text transcription service that processes call audio. This transcribed text would then be sent to the Claude API for intent recognition and extraction of key entities, such as an order number or customer name. The agent would securely connect to your specific backend systems, like a CRM or ERP, using async API calls via httpx to retrieve or update data.
For recognized issues, the agent would generate an appropriate, context-aware response, convert it to speech, and reply to the customer. If the customer's intent is unclear, outside the defined scope, or they explicitly request to speak with a human, the system would escalate the call. It would package the full call transcript and any identified intent into a detailed message for a human agent, typically delivered via Slack or a Zendesk ticket.
The FastAPI service would typically be deployed on AWS Lambda, offering a scalable and cost-effective hosting environment. Every agent interaction would be logged using structlog for detailed monitoring and auditing. Syntora would implement automated alerts, for example, if the agent's confidence score for intent recognition consistently drops below a defined threshold, signaling a need for review and tuning.
What Are the Key Benefits?
Answer 60% of Calls Instantly
The AI agent handles common queries like order status and appointment booking in under 90 seconds, freeing up your team for complex issues.
Pay for Results, Not for Seats
A one-time build fee and usage-based cloud costs. Avoid paying per agent for a platform you only partially use.
You Get the Keys and the Blueprints
We deliver the complete Python source code to your GitHub repository, plus a runbook on how to update knowledge base files.
Know About Problems Before Customers Do
Get automated Slack alerts if the agent fails to understand callers three times in a row, with links to the call transcripts for review.
Connects Directly to Your Business Tools
We build direct API integrations to your CRM, ERP, or scheduling software. The agent can read and write real data to resolve issues.
What Does the Process Look Like?
Knowledge Ingestion (Week 1)
You provide access to support docs, past tickets, or call recordings. We analyze them to define the top 15 intents the agent must handle.
Agent Build and Integration (Week 2)
We build the core voice agent with Python and FastAPI, and connect it to your key systems. You receive a dedicated phone number for testing.
Internal Testing and Tuning (Week 3)
Your team makes test calls to the agent to find edge cases and provide feedback. We refine the prompts and knowledge base based on these calls.
Go-Live and Monitoring (Week 4)
We switch your public support number to the agent. We monitor every call for the first 30 days to ensure performance and hand off the system with full documentation.
Frequently Asked Questions
- What factors determine the project cost and timeline?
- Cost depends on two factors: the number of unique customer intents and the number of API integrations. An agent that only answers questions from a knowledge base is a 2-week build. An agent that needs to look up order status in Shopify and book appointments in Calendly might take 4 weeks. We scope this on our discovery call.
- What happens when the AI agent misunderstands a customer?
- The agent is designed to escalate gracefully. If it fails to understand the request twice, it says 'I'm having trouble with that, let me get a human for you' and transfers the call. A full transcript is sent to your support team so they have context and do not have to ask the customer to repeat themselves.
- How is this different from using a service like RingCentral's IVR?
- RingCentral's IVR uses a rigid 'press-1-for-X' menu. It cannot understand natural language. Our AI agent lets customers speak normally, like 'I need to check on my order from last Tuesday'. It understands context and can have a real conversation, drastically reducing caller frustration and hang-ups compared to a phone tree.
- How do we update the agent's knowledge?
- We build a simple process for you. For knowledge base updates, you just edit a text file in a shared folder, and the agent ingests the changes automatically overnight. For new intents, like handling a new product line query, that requires a small code change, which is covered under our flat monthly maintenance plan.
- Does the agent work with different accents and languages?
- The transcription models we use are trained on a massive global dataset and handle a wide range of English accents effectively. For other languages, we can build agents in Spanish, French, and German. Each additional language is scoped as a separate component of the build, as it requires distinct tuning and prompt engineering.
- What do we need to have in place before starting?
- You need a digital phone number provider that allows call forwarding, like Twilio or Aircall. You also need a structured knowledge base, even if it's a collection of Google Docs, that covers common customer questions. If you do not have documents, we can work directly from three months of call transcripts or support tickets.
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