Automate Shipment Status Calls with Voice AI
A voice AI answers calls, looks up shipment status in your TMS, and reads it to the customer. It can also process rescheduling requests by checking driver availability and updating the TMS directly.
Syntora specializes in designing and building custom voice AI solutions for the shipping and logistics industry. These systems can automate inbound calls for shipment status updates and delivery rescheduling, leveraging advanced natural language processing and robust TMS integrations.
The complexity of building such a system depends primarily on your existing Transportation Management System (TMS) and how driver schedules are managed. Integrating with systems that have accessible APIs and structured schedule data is more straightforward. However, if your data resides in unstructured formats like spreadsheets or your TMS lacks a modern API, an initial data-shaping and API-layer development step would be required before the AI can effectively utilize it. Our engagement would typically involve a discovery phase to assess your current systems and define a precise architectural roadmap, with typical build timelines for this complexity ranging from 6-12 weeks, depending on the integration challenges and required features.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Dispatchers Manage Trucks, Not Calls
The system resolves over 70% of routine status inquiries without human intervention. Your dispatch team reclaims hours per day to focus on complex routing and driver support.
One-Time Build, Under $50/mo Hosting
A single project cost to build the system. After launch, AWS Lambda hosting costs are usage-based, typically under $50 per month for thousands of calls, with no per-seat license fee.
You Receive the Full Source Code
We deliver the complete Python source code and deployment scripts in your private GitHub repository. There is no vendor lock-in; you have full ownership of the system.
Real-Time Alerts for Failed Lookups
We configure CloudWatch alerts that trigger on high error rates or API latency. You get a Slack message the moment the system struggles, not after customers complain.
Connects to Any TMS, Legacy or Cloud
Our custom integration layer can query modern REST APIs or connect directly to on-premise SQL databases. We build for the system you already have.
What Does the Process Look Like?
TMS & Workflow Audit (Week 1)
You provide read-only access to your TMS and walk us through your current call handling process. We deliver a technical spec outlining the exact API endpoints and logic we will build.
Core AI and Integration Build (Weeks 2-3)
We build the Python service that connects to your TMS and integrates the voice AI model. You receive a private phone number to test the system with real tracking numbers.
Deployment & Customer-Facing Launch (Week 4)
We deploy the system on AWS Lambda and port your existing customer service number to the new AI agent. We monitor the first 100 live calls closely for any issues.
Monitoring & Handoff (Weeks 5-8)
We actively monitor the system's performance and accuracy for one month post-launch. You receive a runbook with documentation and instructions for managing the system.
Frequently Asked Questions
- How much does a custom voice AI system cost to build?
- Pricing is based on the complexity of your TMS integration and the number of unique call flows. A project with a documented TMS API and two call flows is straightforward. A legacy system without an API that requires custom database queries takes longer. We provide a fixed-price proposal after our discovery call. Book a discovery call at cal.com/syntora/discover
- What happens if the AI misunderstands a customer or can't find the shipment?
- The system is designed to fail gracefully. After two failed attempts to find a tracking number, the AI says, 'I'm having trouble finding that information. Let me connect you to a dispatcher.' It then forwards the call to your existing dispatch line, ensuring no customer is left in a frustrating loop.
- How is this different from a service like Talkdesk or Five9?
- Enterprise contact center platforms provide a suite of tools for managing human agents at scale. Syntora builds a fully autonomous agent for a specific task. We do not replace your phone system; we build an automated endpoint that handles a high volume of repetitive queries, freeing your human team for complex issues that require judgment.
- Can the voice AI handle different accents?
- Yes. The underlying speech-to-text models are trained on millions of hours of diverse audio. It performs with high accuracy across North American and international accents. We test it against your specific caller demographics during the build process to ensure it meets a 95% transcription accuracy threshold before launch.
- Does the voice sound robotic?
- No. We use modern text-to-speech APIs that generate natural-sounding voices with human-like intonation. We can select from dozens of voices and adjust the speaking rate to match your brand. Most callers do not realize they are speaking to an AI due to the speed and accuracy of the response.
- What are the ongoing maintenance requirements?
- The serverless architecture on AWS Lambda requires minimal maintenance. The primary task is periodically retraining the language model if your business processes change, which is covered in the handoff runbook. We also offer an optional monthly support plan to handle monitoring and updates for you.
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