Automate Freight Booking with a Custom Voice AI Agent
The best voice AI solution for freight booking is a custom system built using a large language model API. This approach avoids rigid call flows and integrates directly with your existing Transportation Management System (TMS).
The build complexity depends on the number of carriers and the structure of your TMS data. A brokerage with a well-documented TMS API and a standard rate confirmation process is a straightforward 3-week project. Integrating with multiple legacy carrier portals requires more complex parsing logic.
We built a voice agent for a 15-person freight brokerage handling 250 loads per week. Their dispatchers spent 10-15 minutes on the phone per booking. The AI now handles 80% of inbound carrier calls for status checks and new load offers, reducing average call time to under 90 seconds.
What Problem Does This Solve?
Many small brokerages start with traditional Interactive Voice Response (IVR) systems. These tools, often built with platforms like Twilio Studio, rely on rigid, tree-based logic. If a carrier asks an out-of-sequence question like, "What's the weight on that load?" before confirming the load number, the entire flow breaks. They struggle to understand regional accents or background noise from a truck cab, leading to high error rates and frustrated drivers who just dial zero for a human.
A 10-person brokerage tried using a generic chatbot builder to create a voice agent. The bot could handle a simple query like, "What loads are available in Texas?" but it failed when a driver asked, "Got anything coming out of Houston going to the northeast, maybe 40,000 pounds?" The bot's intent-matching system could not parse the combination of origin city, destination region, and specific weight. It defaulted to a human transfer, defeating the entire purpose of automation.
These off-the-shelf tools are not designed for the fluid, detail-rich conversations of logistics. They rely on keyword spotting and predefined paths that cannot adapt. Freight booking requires understanding context, remembering details from earlier in the conversation, and accessing real-time data from a TMS, which platform tools cannot do without extensive, brittle workarounds.
How Does It Work?
We start by connecting to your TMS API to pull active load boards and carrier data. We use the Python library httpx for asynchronous requests to keep data fresh, ensuring the agent always has the latest load information. We document every field required for a booking: load ID, pickup and delivery times, weight, equipment type, and rate. This discovery and integration phase takes 2-3 business days.
The core of the system is a Python application built with FastAPI, running on AWS Lambda. When a call comes in, we use an audio streaming service for real-time transcription. This transcript is fed to the Claude 3 Sonnet API with a detailed prompt that includes available loads from your TMS. Claude's large context window lets it handle complex, multi-turn conversations naturally. The system can answer nuanced questions, confirm details, and fill booking slots in a single interaction. The end-to-end response time is under 500ms.
Confirmed bookings and full conversation logs are written to a Supabase Postgres database. This creates a complete audit trail for every automated interaction and allows us to identify common carrier questions to refine the AI's prompts over time. The entire process, from call start to a confirmed booking written in your TMS, takes an average of 75 seconds. The resulting record is indistinguishable from one entered by a human dispatcher.
The FastAPI application is deployed as a serverless function on AWS Lambda, which is highly cost-effective. For a brokerage handling 200 calls per day, the typical monthly AWS bill is under $50. We implement structured logging with structlog, and any API error or transcription failure immediately triggers a Slack alert for instant review and troubleshooting.
What Are the Key Benefits?
Live in 3 Weeks, Not 6 Months
We deploy a production-ready voice agent in 15 business days. Your team sees immediate relief from routine calls, unlike lengthy enterprise IVR implementations.
Pay Once, Own It Forever
A single fixed-price build. No per-call charges, no per-seat licenses, and no monthly subscription fees beyond minimal cloud hosting costs.
You Get the Full Source Code
We deliver the entire Python codebase to your company's GitHub account. You have full ownership and control, with no vendor lock-in.
Handles Real-World Conversations
Our system understands accents, background noise, and non-linear questions. It uses the Claude API to have natural conversations, not follow rigid scripts.
Integrates With Your Current TMS
We connect directly to your existing Transportation Management System via its API. Your dispatchers see AI-booked loads appear instantly without changing their workflow.
What Does the Process Look Like?
Week 1: TMS Integration & Discovery
You provide read-only API access to your TMS. We map the data fields for load booking and define the core conversation flows for carrier interactions.
Week 2: Voice Agent Development
We build the core voice AI agent in Python using the Claude API and FastAPI. You receive daily progress updates and recordings of test calls.
Week 3: Deployment & Testing
We deploy the system on AWS Lambda and connect it to a dedicated phone number. Your team conducts test calls with the live agent to verify its performance.
Weeks 4-8: Monitoring & Handoff
We monitor 100% of live calls for the first month, tuning prompts for accuracy. You receive a complete runbook and full source code access for handoff.
Frequently Asked Questions
- How much does a custom voice AI agent cost?
- Pricing is fixed based on scope. Key factors include the number of unique call types (e.g., booking, status check) and the quality of your TMS API documentation. A system for a single call type with a modern REST API is a standard 3-week build. Complex projects with multiple legacy systems take longer. We provide a fixed-price quote after our discovery call.
- What happens if the AI misunderstands a carrier or a booking fails?
- If the AI cannot confirm a critical detail like a load number with 95% confidence after two attempts, it automatically transfers the call to a human dispatcher. All failed bookings trigger an instant Slack alert with the call transcript and are logged in Supabase for review. This ensures no load is ever lost and we can quickly patch the prompt logic.
- How is this different from using an offshore call center or virtual assistant?
- Virtual assistants still require manual work, operate on a separate system, and create delays in updating your TMS. Our voice agent is a software system that integrates directly with your TMS, updating it in real-time. It operates 24/7 with 100% consistency and costs a fraction of the price of round-the-clock staffing for routine calls.
- How is my sensitive load and carrier data handled?
- The entire system is deployed on your own AWS infrastructure, giving you full control. We never store your data on Syntora's systems. All data, including call transcripts and booking information, resides in your private Supabase database. We follow AWS best practices for IAM roles and access policies to ensure security.
- Does this work with my specific TMS?
- We can integrate with any TMS that offers a REST or SOAP API for reading load data and writing booking confirmations. We have built integrations for systems like AscendTMS and DAT Broker TMS. If your TMS only allows CSV exports, we can build a process to handle that, but it adds about 3 days to the project timeline as it is not real-time.
- Can the system handle different languages or heavy accents?
- The underlying transcription and language models are robust. We primarily build for North American English, which covers a wide range of regional accents effectively. For specific language needs, like Spanish, we use language-specific models. This adds a small amount of complexity but is entirely feasible. We can test performance against sample recordings you provide during discovery.
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