Automate Logistics Scheduling with Custom Voice AI
Syntora is an AI automation agency specializing in custom voice AI for logistics. We build systems that let drivers report ETAs and status updates via voice call.
Scope depends on the complexity of your backend systems. A business using a single, modern Transportation Management System (TMS) with a documented REST API is a straightforward build. A company running on a legacy, on-premise ERP with no API requires building a secure data connector first, which adds to the timeline.
We recently built a system for a regional freight company with 3 dispatchers and 40 drivers. They were losing hours to manual ETA check-in calls. After a 4-week build, our system was handling over 150 driver calls per day, reducing inbound confirmation calls by 80% and freeing up dispatchers for exception handling.
What Problem Does This Solve?
Many logistics companies first try off-the-shelf Interactive Voice Response (IVR) systems from providers like RingCentral or Twilio Studio. These tools rely on DTMF tones ('press 1') or rigid keyword spotting. They fail when a driver with a noisy cab connection says, 'Hey, it's Mike, I'm about 45 minutes from the Philly drop,' instead of the required 'Status Update.' This inflexibility leads to low driver adoption and high error rates.
A common scenario involves a 25-driver freight company trying to automate ETA collection. They set up a basic IVR that asks drivers to key in a 5-digit load ID and a 4-digit ETA. Over a cellular connection, more than 30% of the numeric entries are wrong. The system can't understand a spoken load number, and drivers quickly revert to calling dispatchers directly, defeating the purpose of the tool.
General-purpose voice assistants like Google Assistant are not a solution either. They are built for consumer tasks, not for integrating with a private TMS database to query `Load ID 7891` and update its status. They lack the context for logistics jargon and cannot be configured for the specific workflows required for dispatch operations.
How Does It Work?
We start by provisioning a dedicated phone number through Twilio and connecting it to a FastAPI service deployed on AWS Lambda. When a driver calls, their speech is transcribed and analyzed by the Claude API to understand intent and extract key entities: Load ID, driver name, status, and ETA. This approach correctly interprets conversational phrases, accents, and background noise with over 95% accuracy.
The extracted information is then validated. The FastAPI application makes an asynchronous API call using httpx to your existing TMS or ERP to confirm the Load ID is active and assigned to that driver. Once validated, the system updates the load status in your database. The entire process, from the driver initiating the call to the data being written to your system, completes in under 8 seconds.
After a successful update, the system uses an AWS Polly text-to-speech service to provide a verbal confirmation to the driver, such as, 'Confirmed. Load 5-4-3-2 is updated as arriving at 14:30. Thank you.' If the system cannot understand the driver or a Load ID is invalid, it asks for clarification. This confirmation loop reduces data entry errors to less than 1%.
We deploy the entire system on your own AWS infrastructure, ensuring you own the data and the process. For up to 5,000 calls per month, infrastructure costs typically run under $50. All application events are logged using structlog and sent to AWS CloudWatch. We configure alerts that notify your team via Slack if any API call takes longer than 15 seconds or returns a server error.
What Are the Key Benefits?
Update a Load in 8 Seconds, Not 8 Minutes
Drivers report status with a single voice call. No waiting for a dispatcher. The system handles 50 concurrent calls, eliminating phone tag and hold times.
One Fixed Price, No Per-Call Fees
A one-time build cost with an optional flat monthly maintenance fee. You are not penalized with per-minute or per-call charges that rise with your business volume.
You Get the Full Source Code
We deliver the complete Python codebase to your private GitHub repository. You have zero vendor lock-in and can modify the system with any developer.
Alerts for Failed Updates, Not Angry Drivers
We use AWS CloudWatch to monitor every call. If an update fails to write to your TMS, you get a Slack alert in 60 seconds with the call details.
Connects to Your TMS, Not Ours
The system integrates directly with platforms like McLeod, TMW, and custom ERPs via their existing APIs or a secure database connector we build for you.
What Does the Process Look Like?
Systems & Workflow Audit (Week 1)
You provide read-only access to your TMS/ERP and walk us through the current scheduling process. We deliver a technical spec outlining API endpoints and data fields.
Core AI and Logic Build (Week 2)
We build the core voice recognition and scheduling logic in Python. You receive a demo link to a staging phone number to test call flows and responses.
TMS Integration & Live Deployment (Week 3)
We connect the AI to your live TMS database and deploy it on AWS Lambda. You receive the live phone number for your drivers to start using.
Monitoring & Full Handoff (Weeks 4-6)
We monitor live call volume and accuracy for two weeks, tuning as needed. You receive the full source code, AWS credentials, and a runbook for maintenance.
Frequently Asked Questions
- What factors determine the cost and timeline?
- The primary factors are integration complexity and call logic. Integrating with a modern REST API is faster than a legacy SQL database. A simple 'ETA and status' system is a 3-week build. A system with complex branching logic like 'report a maintenance issue' or 'request a fuel advance' can take 4-5 weeks. We provide a fixed-price quote after the initial discovery call.
- What happens if the AI misunderstands a driver or the call drops?
- If the AI has low confidence in the transcription, it asks the driver to repeat the information. If it fails a second time, the system flags the call recording for manual review by a dispatcher. Dropped calls are also flagged in the system log. This ensures every attempted update is captured, even if it requires a quick human follow-up.
- How is this different from using a virtual assistant service?
- Virtual assistant (VA) services are humans following a script. They do not scale instantly, have recurring hourly costs, and introduce human error. Our system is a dedicated AI that handles hundreds of calls simultaneously with 100% consistency. It integrates directly with your database for real-time updates, whereas a VA manually enters data, which is slower and less reliable.
- Can the system handle different languages or strong accents?
- Yes. We use large language models for transcription and entity extraction, which are trained on vast datasets of global audio. The system performs reliably with a wide range of English accents. For specific language needs, we can deploy models for Spanish, French, and other languages, which would be scoped during the initial audit phase of the project.
- Does this require us to change our existing TMS or ERP?
- No. The system is built to integrate with your current software. It acts as a voice interface for the tools you already use. For systems without a modern API, we build a secure, lightweight wrapper that allows our AI to communicate with your database without requiring you to migrate platforms or undertake a costly software upgrade.
- Who pays for the phone number and call costs?
- You do, but the costs are minimal. The system runs on your own Twilio and AWS accounts, which we help set up. A dedicated phone number costs about $1/month. Call costs via Twilio are typically less than one cent per minute. For a fleet of 50 drivers making several calls a day, total monthly infrastructure costs are usually under $100.
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