Deploy a Voice AI System for Your Warehouse in 4 Weeks
A small logistics business should hire a consultant when pick-and-pack errors exceed 3% or training new staff takes weeks. This happens when your warehouse management system (WMS) lacks hands-free, language-agnostic voice commands for inventory updates.
The build scope depends on your existing WMS and the number of custom commands needed. Integrating with a modern, API-first WMS like ShipHero is faster than connecting to a legacy, on-premise system with limited documentation. The complexity lies in training a model that understands industry-specific jargon and works reliably in a noisy warehouse environment.
We built a voice system for a 15-person third-party logistics (3PL) company. Their team was using paper pick lists, leading to frequent mis-picks for their 200+ clients. We deployed a system in 3 weeks that gave each picker a headset to receive orders and confirm picks verbally, reducing their error rate from 5% to under 0.5%.
What Problem Does This Solve?
Many logistics teams look at voice add-ons for their existing WMS, such as modules for NetSuite WMS or Fishbowl. These systems are rigid. They expect specific phrasing like "Location A-one-three, confirm pick" and fail with regional accents or background noise from forklifts. Customizing commands requires expensive professional services contracts and long development cycles, if it's even possible.
Other teams consider hardware-centric solutions from vendors like Honeywell (Vocollect) or Zebra. This approach forces you to buy their proprietary scanners, their headsets, and their software. You are locked into their ecosystem. The software is a closed box, making it impossible to integrate with a custom CRM or a new shipping provider's API. They are designed for 500-person warehouses, with pricing to match.
A 25-person e-commerce fulfillment center with a diverse, multilingual workforce illustrates the failure. Their standard WMS voice add-on was English-only and had a 20% error rate for Spanish-speaking staff. They reverted to paper lists, and mis-picks for simple orders cost them thousands per month in return shipping fees because pickers had to juggle a list, a scanner, and the items.
How Does It Work?
We start by analyzing your warehouse workflow and existing WMS API documentation. We record 2-3 hours of ambient warehouse noise and collect 50-100 audio samples of key commands from your actual team members. These samples, captured on the low-cost hardware we'll deploy, form our initial training dataset. We use Python with the Librosa library to process and augment this audio data for model training.
We fine-tune a pre-trained speech-to-text model like Whisper on your specific commands and acoustic environment using PyTorch. This makes the model robust to your warehouse's background noise and your team's unique accents. The core logic is a FastAPI application that takes the transcribed text, maps it to WMS actions (e.g., "confirm pick 5 units"), and validates the command against the open order. Invalid commands get a "please repeat" response in under 300ms.
The FastAPI service is containerized with Docker and deployed to AWS Lambda for serverless execution, costing pennies per 1,000 commands. It integrates directly with your WMS via its REST API, using httpx for asynchronous calls to update inventory counts in real-time. Each warehouse worker gets a simple web app on a ruggedized tablet, connected to a standard Bluetooth headset. The entire build takes 3-4 weeks.
We use structlog for structured, JSON-formatted logs of every command and its outcome. We build a simple dashboard in Supabase that tracks the command recognition accuracy rate and flags any command with more than a 15% failure rate for review. This allows us to identify and add new phrasing or retrain the model on specific users who are having trouble, ensuring accuracy stays above 99%.
What Are the Key Benefits?
Hands-Free in 3 Weeks, Not 6 Months
We deploy a production-ready system in 15-20 business days. Avoid the quarter-long sales cycles and implementation queues of large WMS vendors.
No Per-User Fees, Ever
You pay a one-time build cost. The system runs on AWS Lambda, typically costing under $50/month for a 20-person team, regardless of headcount.
You Own the Code and the AI Model
We deliver the full Python source code and the trained model files to your company's GitHub. You have zero vendor lock-in.
Adapts to Your Team and Warehouse
The system monitors recognition failures. We can easily retrain the model to understand new accents, slang, or increased background noise as your operations change.
Works With Your Existing WMS
Our system connects to any WMS with a documented API, from ShipHero to Fishbowl. No need to migrate your core inventory management software.
What Does the Process Look Like?
Workflow & Audio Audit (Week 1)
You provide read-only API access to your WMS and we record audio samples on-site. You receive a technical brief outlining the integration points and required commands.
Model Training & API Build (Week 2)
We fine-tune the speech recognition model and build the core API logic. You receive a link to a staging environment to test command recognition.
Deployment & On-Site Testing (Week 3)
We deploy the system to AWS and integrate with your live WMS. We spend a day on-site with your team, testing the hardware and workflow.
Monitoring & Handoff (Week 4)
We monitor performance for one week post-launch, making adjustments as needed. You receive the full source code, documentation, and a runbook for maintenance.
Frequently Asked Questions
- How is the project cost determined?
- Cost is based on two factors: the complexity of your WMS integration and the number of custom voice commands required. A system that only needs to confirm picks and check stock levels will be simpler than one that also needs to handle bin transfers and cycle counts. We provide a fixed-price quote after the initial discovery call, so you know the full cost upfront.
- What happens if the internet goes down in the warehouse?
- The system is designed for intermittent connectivity. The local web app on the worker's device can queue commands and sync them once the connection is restored. For critical actions, we can implement an audio alert that notifies the user the command is queued and not yet confirmed in the WMS. This prevents data loss during Wi-Fi outages.
- How is this different from a Vocollect or Zebra voice solution?
- Those are hardware-first systems that lock you into their ecosystem of scanners and proprietary software. Syntora builds a software-only solution using Python and AWS that runs on any standard mobile device and Bluetooth headset. You are not tied to any hardware vendor, and you own the code, allowing for unlimited customization and integration with other business systems.
- Does it work with different languages and accents?
- Yes. We fine-tune the AI model on audio recordings from your actual team. This makes it highly accurate for the specific accents and languages spoken in your warehouse, including Spanish or Creole. The system can even be trained to handle code-switching, where a worker might mix languages in a single command. Standard off-the-shelf systems typically fail at this.
- What kind of hardware do we need to buy?
- You do not need any proprietary hardware. The system runs on any modern Android tablet or phone and connects to standard, industrial-grade Bluetooth headsets. We typically recommend a specific model of ruggedized tablet and a noise-canceling headset that costs around $350 per worker, but you are free to source your own hardware.
- How do we add new commands after the project is done?
- The system is built to be extensible. The runbook we provide includes instructions for adding new commands. It involves adding a new route to the FastAPI application and updating the logic. A mid-level developer can typically add a new command in a few hours. For clients on our monthly maintenance plan, we handle these changes for you.
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