Automate Your Logistics Reception with Custom Voice AI
Syntora is an AI automation agency specializing in Voice AI for logistics reception. We build systems that automate driver check-ins and appointment scheduling over the phone.
Scope depends on the complexity of your check-in process. A single warehouse integrating with a modern Transportation Management System (TMS) with a documented API is a straightforward build. A multi-site operation with complex rescheduling logic and a legacy WMS requires more discovery and integration work.
We recently built a voice system for a regional 3PL with four warehouses processing 120 trucks per day. Their receptionists were overwhelmed with check-in calls. The custom system went live in a 3-week build and cut the average driver check-in time from 15 minutes to under 90 seconds.
What Problem Does This Solve?
Many logistics companies first try off-the-shelf Interactive Voice Response (IVR) systems. These rigid, menu-driven tools fail in noisy environments. When a driver calls from their truck cab, the IVR cannot parse their accent over the engine noise, forcing them to repeat their Bill of Lading (BOL) number three times before giving up.
A visual builder like Twilio Studio seems like the next step. But mapping a real logistics check-in conversation, with branches for late arrivals, incorrect paperwork, and appointment lookups, creates a tangled workflow that is impossible to maintain. A simple check for a valid BOL number requires custom code that breaks the no-code promise, and these platforms often lack the logic to handle multi-turn conversations where context must be remembered.
These tools are not designed for the specific vocabulary of logistics. They misinterpret “BOL” as “bowl” or fail to extract a 10-digit PRO number from a sentence. The result is a high failure rate, frustrated drivers, and receptionists who still have to handle most calls manually, defeating the entire purpose of the automation.
How Does It Work?
We begin by mapping your exact driver check-in conversation, from initial greeting to final dock assignment, including all exception paths. We use the Claude API to generate hundreds of synthetic training conversations, covering common accents, background noises, and phrasing variations. This ensures the system is resilient to real-world call conditions from day one.
We build the core logic in Python using a FastAPI service. When a call comes in, a speech-to-text API transcribes the audio with under 500ms latency. This raw text is sent to the Claude API with a structured prompt designed to extract key entities like driver name, carrier, and BOL number, even from imperfect transcriptions. The system can correctly identify a BOL number whether a driver says "My bill of lading is..." or just rattles off the number. This entire conversational turn completes in under 2 seconds.
The FastAPI service is deployed on AWS Lambda, ensuring it scales automatically and is highly available. We write custom integrations using httpx for asynchronous calls to your TMS or WMS API to validate appointment details and BOL numbers in real time. Validated check-ins are logged to a Supabase database and can trigger a Slack notification to the yard manager. A typical 5-turn check-in conversation is completed in under 15 seconds.
For ongoing visibility, we use structlog to generate structured logs for every call. We provide a simple dashboard that tracks call volume, average call duration, and the successful check-in rate. If the success rate drops below a 95% threshold for more than an hour, an automated alert is sent, allowing for immediate investigation.
What Are the Key Benefits?
Launch in 3 Weeks, Not 6 Months
A focused 15-day build cycle gets your voice system live and handling calls immediately. No long enterprise sales cycles or lengthy internal rollouts.
One-Time Build, Predictable Usage Costs
A single scoped project means no recurring per-user or per-call SaaS fees. AWS Lambda costs are typically under $50/month for 2,000 calls.
You Own the Code and the AI Prompts
We deliver the full Python source code to your company GitHub. You receive all Claude API prompts and complete system documentation.
Real-Time Failure Alerts, Not Driver Complaints
The system monitors its own check-in success rate. You get a Slack alert if failures increase, letting you fix issues before drivers get frustrated.
Connects Directly to Your TMS
We build custom API integrations to your existing TMS or WMS. The system validates BOL numbers and appointment times against your source of truth.
What Does the Process Look Like?
Process Mapping (Week 1)
You provide documentation for your current reception process and read-only API access to your TMS. We deliver a detailed conversational flow diagram covering all check-in scenarios.
Core System Build (Week 2)
We build the core voice agent using Python and FastAPI, connecting it to the Claude API. You receive a private phone number for testing the agent internally.
Integration and Deployment (Week 3)
We connect the agent to your TMS, deploy it on AWS Lambda, and port your public reception number. We test the end-to-end flow with 20+ live test calls.
Monitoring and Handoff (Weeks 4-6)
We monitor system performance for two weeks post-launch, tuning prompts as needed. You receive the full source code, a runbook, and a training session on the monitoring dashboard.
Frequently Asked Questions
- How much does a custom voice AI system cost?
- Pricing is based on the complexity of the conversation and the number of integrations. A simple check-in system for one warehouse with a modern TMS API is a 2-week build. A multi-warehouse system with appointment rescheduling and integrations to a legacy system is a 4-week build. We provide a fixed-price quote after a discovery call.
- What happens if the AI cannot understand a driver?
- After two failed attempts to understand a key piece of information like a BOL number, the system automatically transfers the call to a human receptionist. The receptionist receives a link to the call transcript up to that point, so they do not have to ask the driver to repeat everything. This 'human-in-the-loop' fallback ensures no driver gets stuck.
- How is this different from using a service like Twilio Studio?
- Twilio Studio is a tool for simple IVR menus ('Press 1 for...'). It struggles with natural language, accents, and noisy environments. Our system uses the Claude API to understand intent from messy, conversational speech. Instead of a rigid menu, drivers can speak naturally, which reduces failed calls from over 30% with IVRs to under 5%.
- Are our call recordings and data kept private?
- Yes. The entire system is deployed on your own AWS infrastructure, not a multi-tenant platform. We use APIs that do not store data for model training by default. You own the infrastructure, the code, and the logs. We retain no access after the handoff unless you engage us for an ongoing maintenance plan.
- Can it handle different languages or strong accents?
- Yes. During the build, we identify the most common languages and accents among your drivers. We generate synthetic audio data to improve the system's understanding for those specific profiles. The standard build supports English with high accuracy across North American, European, and South Asian accents. Additional languages like Spanish can be scoped into the project.
- What technical resources do we need on our end?
- You need an AWS account and a person who can provide API documentation and a test environment for your TMS or WMS. No in-house developers are required. We handle all the coding and deployment. After launch, the runbook we provide can be followed by any technical person to manage the system, or you can use our optional monthly maintenance plan.
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