Voice-driven workflows for gloved hands and busy eyes
Warehouses, factory floors, field service, commercial kitchens: wherever workers wear gloves, climb ladders, or keep their eyes on the task, voice beats touch. VoxRT runs the whole loop on rugged handhelds and fixed stations — no coverage dead zones, no cloud round-trip in the middle of a pick.
Why the floor runs on local voice
Hands stay on the work
Checklists advance, quantities confirm, and defects log without removing gloves, climbing down, or looking away — the task keeps its rhythm.
Works in coverage dead zones
Steel buildings, cold storage, basements, remote sites — the places industrial work happens are the places connectivity dies. Local recognition doesn't notice.
Latency that keeps pace with work
A picker confirms hundreds of times a shift. Recognition that answers like a button press keeps the pace; a cloud round-trip in every confirmation breaks it.
Voice on the floor
Structured, repetitive, hands-busy — the workflows voice was made for.
Warehouse & picking"Bin four-two-seven, quantity six"
Pick confirmations spoken into a headset and captured as structured data — hands never leave the tote.
Quality inspectionDefects narrated in place
Inspectors describe what they see as they see it; entries land as structured records, not voice memos to retype.
Maintenance & repair"Next step" · "Repeat"
Procedures read step-by-step and advanced by voice — up a ladder, inside a cabinet, under a machine.
Food serviceKitchen displays, dirty hands
Line stations bumping and recalling orders by voice — no touchscreen taps between the gloves and the food.
Labs & cleanroomsSterile sample handling
Observations and protocol steps logged by voice where gloves and sterility make keyboards impossible.
Construction & utilitiesReadings from the pole
Checklist items and meter readings confirmed hands-free at height, in the field, off the grid.
From workflow to voice loop
Choose commands and phrases per workflow
A compact command vocabulary (keyword spotting) for confirmations and navigation; speech-to-intent where entries carry values — bins, quantities, defect codes — as structured slots.
We tune models for your floor
Conveyor noise, forklift horns, freezer fans, your workforce's languages and your actual vocabulary — models are trained and thresholds set against the environment they'll work in.
Deploy to the devices you already issue
Rugged Android handhelds (Android 8.0+) and fixed Linux stations, with wired or Bluetooth headsets — fully functional offline.
Feed structured events into your systems
Detections and intents arrive as events in your application, which forwards them to the WMS, QMS, or CMMS the same way a barcode scan would.
What's under the hood
The runtime's performance numbers are measured and published — see the benchmark methodology. Command sets and intent models are tuned per deployment.
Pipeline. Headset or device microphone → on-device VAD (so recognition runs only on speech) → keyword spotter and/or speech-to-intent model → structured events with confidence scores into your application, then on to WMS/QMS/CMMS. Everything executes locally on the device CPU.
Platforms. iOS 16+ (Swift Package Manager) and Android 8.0+/API 26 (JitPack/Gradle), plus Linux aarch64 for device-class hardware — validated on Raspberry Pi 3/4/5 and Pi Zero 2 W, NVIDIA Jetson Nano and Orin Nano, AWS Graviton and similar Cortex-A53/A55 boards — shipped as a single ~480 KB shared library with Python, Node.js, Go, C/C++ and Rust wrappers.
Licensing. Command sets and intent models are tuned per deployment as a one-time engagement; the runtime carries no per-device or per-scan fees, which matters when a fleet is thousands of handhelds — see licensing.
Industrial voice, answered
Does it hold up in warehouse and factory noise?
Noise robustness is engineered per deployment: models are trained with heavy noise augmentation, tuned against your floor's actual sound, and paired with close-talking headset microphones. Per-command thresholds then set the miss/false-trigger balance your workflow needs.
What happens in coverage dead zones?
Nothing — recognition is local, so freezers, steel racking, and basements don't interrupt the workflow. Your application queues the resulting events and syncs to the WMS whenever connectivity returns.
How does voice data get into our WMS or QMS?
The SDK emits events — commands, intents with slot values, confidences — inside your application on the device. From there they enter your systems through the same integration path as barcode scans or form entries.
Can it handle a multilingual workforce?
Command sets and intent models are trained for the languages your teams actually speak — that's part of the per-deployment tuning engagement rather than a fixed language list.