Stop Missing Sales Calls. Get an AI Agent That Closes.

Most online retailers handle inbound calls with a basic interactive voice response (IVR) system. These phone trees force customers into predefined buckets, which fails when they have specific product questions. A customer asking, "Does this dining table come in walnut?" will hang up after hearing "Press 1 for sales, Press 2 for support." That potential sale is lost.

To solve this, some businesses hire offshore call centers. But this introduces high costs and inconsistent quality. A $15/hour agent reading a script cannot answer detailed technical questions, leading to escalations and delays. For a store getting 100 calls per day, this can exceed $40,000 per month in salary and management overhead, and agents still require extensive training on the product catalog.

A D2C furniture store we worked with faced this exact problem. A customer called on a Saturday, when the small sales team was offline, to ask if a specific bookshelf could support 50 pounds per shelf. The IVR routed them to a voicemail box. The customer gave up and purchased a similar item from a larger competitor that day.

Syntora's approach to implementing voice AI for ecommerce sales calls begins with a thorough data ingestion strategy. We would start by auditing the client's existing product catalog, typically accessible via their ecommerce platform's API (e.g., Shopify, Magento, or custom systems). All product descriptions, specifications, and relevant FAQs would be processed and indexed into a vector database, commonly leveraging Supabase with the `pg_vector` extension for efficient semantic search capabilities on product data.

The core of the system would be a Python-based service built with FastAPI, designed to manage the entire call logic. This service would integrate a low-latency speech-to-text model for near real-time transcription of caller audio. We have extensive experience building document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern would be applied here: the transcribed text, enriched with context from the Supabase vector store, would be fed to the Claude 3 Sonnet API to generate accurate, contextually relevant responses.

For deployment, the FastAPI service would be containerized using Docker and deployed on AWS Lambda. This serverless architecture optimizes operational costs by only consuming compute resources when the agent is actively handling a call. Amazon Connect would be utilized to manage inbound phone numbers and route calls to the Lambda function for processing by the AI.

A critical component of this service is intelligent lead qualification. When the AI detects high purchase intent based on conversational cues, it would initiate a warm transfer to a human sales agent using the Twilio API. This process ensures seamless handoffs to your team. Concurrently, a complete transcript of the AI-caller interaction would be posted to a designated Slack channel via a webhook, providing sales agents with immediate context before engaging with the customer. The delivered system would be architected for scalability, designed to handle varying call volumes efficiently.