Deploy an AI Agent to Handle Your Inbound Support Calls
AI agents transcribe calls in real-time to understand customer intent and access knowledge bases. They resolve common issues instantly and escalate complex tickets to human agents with full context.
Syntora provides expertise in developing AI agents for inbound customer service calls. Their approach involves detailed analysis of call patterns, structuring knowledge bases, and integrating with existing systems to deliver efficient, context-aware call handling solutions.
The scope for developing an AI call agent depends significantly on the number of distinct customer queries your business receives and the complexity of integrations required with existing CRM or ERP systems. For example, a system designed to address 10 core FAQs with a simple support platform is a more contained project than one needing to dynamically check order status in Shopify and update customer contacts in HubSpot.
Syntora works with clients to identify these specific call drivers, technical requirements, and integration points to accurately define the project scope, typical build timelines, and expected deliverables.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Answer 80% of Calls on the First Ring
The system picks up instantly and resolves common issues in under 90 seconds, eliminating hold times and freeing up your team for complex problems.
Pay for a Build, Not Per-Minute
A one-time project fee and low monthly cloud costs replace expensive per-agent or per-minute SaaS plans. You are not penalized for growing call volume.
You Own the Agent, Code, and Data
You get the full Python source code in your GitHub repository. Call transcripts are stored in your own Supabase instance, not a third-party platform.
Smart Monitoring Catches Failure First
Structured logs feed a dashboard tracking resolution rates and API errors. We set up alerts that notify us if the transcription service fails or resolution rate drops 10%.
Connects Directly to Your Business Tools
We build custom API connectors to your CRM, ERP, or scheduling software like Housecall Pro. The agent can check real order statuses or appointment times.
What Does the Process Look Like?
Call Log Analysis (Week 1)
You provide access to call recordings or ticket history. We analyze the data to identify the top 15-20 customer issues and map out conversation flows.
Core Agent Build (Week 2)
We build the voice transcription pipeline and the core conversational logic using the Claude API. You receive a demo link to test responses to common questions.
System Integration (Week 3)
We connect the agent to your CRM and other internal APIs for live data lookups. You receive a staging phone number to run end-to-end tests.
Launch and Tuning (Week 4+)
We port your main support number and go live. For the first 60 days, we monitor all conversations, tune the knowledge base, and provide a final runbook.
Frequently Asked Questions
- How much does a custom AI call agent cost and how long does it take?
- A typical build takes 3-4 weeks. The cost depends on the number of unique intents to handle and the complexity of API integrations. An agent that only answers FAQs from a knowledge base is straightforward. An agent that needs to read from and write to a custom ERP system requires more development time. We provide a fixed-price quote after our discovery call.
- What happens if the AI misunderstands a customer or an API is down?
- The agent is programmed with escalation paths. If it fails to understand a request twice, it automatically says 'Let me get a human for you' and transfers the call. If an external API like Shopify is down, the agent informs the customer it cannot look up order info right now and offers to connect them to a person or create a ticket.
- How is this different from using a service like Talkdesk or Aircall's AI features?
- Platforms like Talkdesk provide excellent AI tools within their ecosystem, but you are locked into their platform and pricing. We build a standalone system you own completely that integrates with your existing phone provider and CRM. You are not paying per-seat fees for the AI, and the source code is yours to modify forever.
- Does the AI voice sound robotic?
- We use modern text-to-speech (TTS) APIs that offer lifelike voices with natural intonation. We can select from dozens of voices and even adjust for pacing and tone to match your brand. The goal is for the customer not to immediately realize they are speaking to an AI, making the interaction smoother than with traditional robotic IVRs.
- How is sensitive customer information handled?
- The system is built on your own cloud infrastructure (AWS) and database (Supabase). Customer data is not stored or processed by Syntora's systems post-launch. We can implement redaction for sensitive data like credit card numbers during the transcription phase. You maintain full control and ownership over all conversation data.
- Can the agent handle languages other than English?
- Yes. The transcription and language model APIs we use support multiple languages, including Spanish and French. Supporting another language involves creating a separate knowledge base for that language and adding logic to detect the caller's language at the start of the call. This is a common scope extension we can discuss during discovery.
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