How to Choose a Voice AI Provider for Your Warehouse
A small logistics company should prioritize total cost of ownership over per-seat SaaS fees. They must also confirm the provider can integrate directly with their existing warehouse management system.
Unlike large enterprises, a small warehouse cannot absorb a six-figure implementation fee for a rigid, off-the-shelf system. The right provider builds a system that maps to your existing picking process and connects to your specific WMS, whether it is a modern API or a legacy SQL database. The goal is a production system, not a science project or a forklift upgrade.
We built a custom voice picking system for a 15-person 3PL that was struggling with a 12% mis-pick rate. Their team used paper lists to process 5,000 orders per month. We built and deployed a system in 4 weeks that ran on standard Android phones. Their mis-pick rate dropped to under 1% within the first month.
What Problem Does This Solve?
Most logistics companies first look at established voice picking systems like Zebra or Honeywell Voice. These are built for 500,000 square foot distribution centers and come with five-figure hardware costs and mandatory per-user annual licensing. The software is rigid; changing a workflow requires a new statement of work and weeks of waiting. It is complete overkill for a 20-person team.
The next step is often trying to build something with general-purpose tools. A team might try connecting a standard speech-to-text API, like Google's, to a mobile app. This fails in a real warehouse. These APIs are trained on conversational language, not SKU numbers and bin locations. With background noise from forklifts and conveyor belts, recognition accuracy for a command like "pick three of B-X-7-5" drops below 80%.
This exact scenario happened with a regional food distributor. They built a prototype using a generic voice API. The 2-second processing lag between a picker speaking a command and getting a confirmation added over 30 minutes of dead time to each picker's shift. The constant recognition errors forced them to revert to paper pick lists after just one week.
How Does It Work?
Our process starts with your Warehouse Management System (WMS). We connect directly to its data source, typically a PostgreSQL or MS SQL database, often using a read-only replica we set up in AWS RDS. We map your exact pick path logic into a state machine managed by a Python application. We also record 30 minutes of audio from your loudest warehouse aisle to fine-tune a speech recognition model.
We build the core voice processing engine as a FastAPI service deployed on AWS Lambda. For speech recognition, we use a provider like Deepgram, which is designed for noisy industrial environments and has features for tuning vocabulary. This allows the system to correctly distinguish between "Aisle B-1-2" and "Aisle D-1-2" with over 99% accuracy. The entire command-response loop, from the picker speaking to the audio confirmation, takes less than 400ms.
The system runs as a simple web application on any standard Android phone, paired with a noise-canceling Bluetooth headset. There is no proprietary hardware to buy. Pickers receive audible instructions, speak their confirmations, and the FastAPI service updates the WMS in real-time via an API call or a direct database write. The application state for 10 concurrent pickers uses less than 256MB of memory and costs under $40 per month to host.
For monitoring, we use `structlog` to send structured JSON logs to AWS CloudWatch. We set up specific alarms that trigger a Slack notification if API latency exceeds 800ms or if the rate of invalid commands from a specific picker exceeds 5% in an hour. This helps spot issues with hardware or training before they impact fulfillment.
What Are the Key Benefits?
Live in 4 Weeks, Not 6 Months
From WMS audit to on-floor deployment in a single 4-week build cycle. Your team starts picking faster immediately, without a quarter-long implementation project.
Own Your System, No Per-User Fees
A one-time fixed-price build. You get the full source code and pay only for minimal AWS hosting, not a recurring per-seat license that penalizes growth.
Runs on Hardware You Already Own
The system works on any modern Android phone and a standard Bluetooth headset. No need to purchase thousands of dollars in proprietary voice terminals.
Proactive Error Monitoring
We configure alerts in AWS CloudWatch that notify you of high error rates or latency. You find out about a faulty headset or network dead spot in minutes.
Direct WMS Integration
We connect directly to your existing WMS, whether it's Fishbowl, NetSuite, or a custom-built SQL database. Your data stays in your system of record.
What Does the Process Look Like?
Week 1: WMS Audit & Workflow Mapping
You provide read-only access to your WMS. We map your entire picking process, document the database schema, and define the exact voice commands needed.
Week 2: Core Engine & Voice Model Build
We build the FastAPI application and fine-tune the speech recognition model with your warehouse audio. You receive a demo video of the system in action.
Week 3: Integration & On-Floor Testing
We connect the voice engine to your WMS and deploy the app to a test device. Your lead picker uses the system to pick real orders on the warehouse floor.
Week 4: Launch, Handoff & Monitoring
The system goes live for your entire team. We monitor performance for 30 days and then hand over the GitHub repository, AWS credentials, and a full runbook.
Frequently Asked Questions
- What factors affect the cost and timeline?
- The primary factors are WMS integration complexity (a well-documented REST API is faster than reverse-engineering a legacy database) and the number of exception workflows we need to build, such as handling backorders or damaged items. A standard pick-and-confirm workflow is a 4-week build. More complex logic can extend the timeline.
- What happens if a picker's headset dies or the Wi-Fi drops?
- The application is designed to handle temporary disconnects. It queues commands locally on the device and syncs with the server once connectivity is restored. If a headset fails, the picker can switch to using the phone's built-in microphone or swap to a backup headset in seconds without losing their place in the pick list.
- How is this different from an off-the-shelf system like Honeywell Voice?
- Honeywell sells a closed ecosystem of proprietary hardware and licensed software with high per-user fees. We build custom software that runs on standard Android phones. You own the code, it's tailored to your exact workflow, and there are no recurring license costs. It is a capital investment, not an operational expense.
- What kind of accuracy can we expect in our noisy warehouse?
- We consistently achieve over 99% command recognition accuracy. We do this by using speech models trained for industrial noise and by constraining the vocabulary. The system only listens for valid commands like bin locations, quantities, and confirmations. It is not trying to understand general conversation, which dramatically improves accuracy.
- Does this work with barcode scanning?
- Yes. We can build workflows that combine voice and scanning. A common pattern is to use voice for navigation and confirmation, but use the phone's camera or a connected Bluetooth scanner for lot number or serial number capture. This combines the speed of hands-free operation with the accuracy of scanning for critical data entry.
- What does the maintenance plan cover after the first 30 days?
- The optional flat monthly plan covers hosting costs, system monitoring, and critical bug fixes. It also includes regular updates to Python libraries and OS packages to address security vulnerabilities. It does not include new feature development, which would be scoped as a new fixed-price project.
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