Automate Shipment Status Calls with Voice AI

Most small trucking companies rely on cloud phone systems like RingCentral or Aircall. These have basic IVR menus that route calls but cannot resolve them. A customer presses '1 for dispatch', waits on hold, and still speaks to a human who manually looks up the shipment. This adds a frustrating step for the customer without reducing the dispatcher's workload.

Some teams try to use off-the-shelf voicebot builders, but connecting them to a proprietary TMS is a major engineering hurdle. These tools cannot natively parse a Bill of Lading (BOL) number from a spoken sentence or understand the constraints of a driver's Hours of Service (HOS) when a customer asks to reschedule. The project stalls when the visual builder cannot handle the specific logic and data of a logistics operation.

A dispatcher at a 15-truck company can spend 4-5 hours a day answering status calls. This manual process is not just inefficient; it's prone to error. A dispatcher mishearing a tracking number or quoting the wrong ETA creates a poor customer experience and requires a follow-up call, doubling the work.

Syntora would approach this problem by first conducting a detailed discovery phase to understand your existing TMS infrastructure, data models, and operational workflows.

The technical architecture would typically involve establishing a secure connection to your TMS. For modern systems with robust APIs, we would leverage Python's httpx library for efficient, asynchronous data retrieval. If your TMS is an older, on-premise system or lacks a suitable API, we would propose developing a lightweight Python agent to run on your local network. This agent could query the database directly using SQLAlchemy and expose a secure FastAPI endpoint, allowing our cloud services to access real-time shipment status data. This strategy ensures low-latency data access regardless of your current TMS setup.

For natural language understanding, the solution would integrate with the Claude API. As calls connect, audio would be transcribed in real-time. Claude would be configured to extract key entities such as tracking numbers, BOLs, or customer names, and then formulate precise queries for the TMS API. For rescheduling requests, Claude would translate the natural language into structured queries for available delivery slots, based on your defined operational rules and constraints. We have successfully built similar document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies here for parsing customer requests.

Responses from the TMS, like "In transit, ETA 4 PM," would be converted into natural-sounding audio for the customer using a text-to-speech model. If a rescheduling request is confirmed, the voice agent would update the TMS directly and send an SMS confirmation via Twilio.

The system would be designed for scalability and cost-efficiency, often leveraging serverless functions on AWS Lambda. An incoming call to a provisioned phone number would trigger the service. This serverless architecture would provide significant cost savings during idle periods while dynamically scaling to handle hundreds of concurrent calls. We would implement structured logging using structlog and configure CloudWatch alerts to proactively monitor system health, such as notifying your team via Slack if the AI fails a query or TMS API latency exceeds defined thresholds.

Our engagement would deliver a fully functional, custom-built voice AI system tailored to your specific requirements, along with comprehensive documentation, knowledge transfer, and options for ongoing support.