Hands-free, eyes-free pick-by-voice handling natural voice prompts. Runs on commodity Android handhelds, rugged devices, and embedded Linux. No cloud round-trip.
On-device voice picking combines Koala Noise Suppression, Porcupine Wake Word, Rhino Speech-to-Intent, and Orca Text-to-Speech in a single local pipeline. Koala suppresses warehouse noise. Porcupine listens for the wake word. Orca speaks each pick instruction. Rhino captures the picker's check digits and picked quantity. The pipeline runs entirely on the picker's device with no audio leaving it.
Koala suppresses conveyor noise, forklift backups, fan noise, and HVAC before audio reaches voice AI engines like Porcupine and Rhino. While it's 2× more effective at suppressing real-world warehouse noise, it's still the same size, making it a fit for embedded devices, low-tier or legacy mobile devices, and rugged handhelds, leaving more than sufficient headroom for the rest of the pipeline.
Porcupine Wake Word is lightweight, accurate, customizable, and production-ready. With Porcupine, pickers can wear a Bluetooth headset connected to a rugged Android handheld for an entire shift without draining the device's battery. The wake phrase is fully customizable so it doesn't collide with everyday warehouse speech.
Most voice command systems run a two-step pipeline: speech-to-text converts audio to a transcript, then a separate NLU model parses that transcript for intent. Every step accumulates error and compounds latency. Rhino Speech-to-Intent is an end-to-end speech-to-intent engine with a single model that maps spoken audio directly to a structured intent with typed slot values. High accuracy even in noisy environments. No hallucinations. No intermediate transcript.
Speaks each pick instruction the picker hears — aisle, bay, SKU, quantity, check-digit prompt. Most high-quality TTS solutions require hundreds of megabytes of RAM. Orca TTS uses 29 MB peak memory, making Orca the only natural-sounding TTS deployable in any environment, including mobile apps with strict out-of-memory limits, and embedded devices. It leaves headroom for the rest of the pipeline and more operators. Custom pronunciation handles SKU codes, brand names, and warehouse-specific terms.
3PLs and e-commerce fulfillment operators can deploy voice picking on the rugged Android handhelds and Bluetooth headsets they already own — no proprietary Vocollect or LYDIA hardware required. The same pipeline covers replenishment and putaway, so the same device works across the operation.
Retail DCs running Manhattan, Blue Yonder, SAP EWM, Oracle WMS, or Infor can keep their system of record and add voice picking as the operator interface. Structured intents from the on-device voice picking pipeline can be integrated into any WMS.
Cold storage facilities, food & beverage warehouses, and pharma distribution centers all share three problems: freezer rooms with no signal, worker-privacy regulations on voice recording, and zero tolerance for inventory errors. The on-device pipeline addresses all three — runs offline, keeps audio local, and avoids LLM hallucinations on the quantity field.
Manufacturing kitting operations, line-side replenishment, and just-in-time material movement use the same voice-picking pattern. The Rhino YAML can be extended with kit IDs, station IDs, and exception codes specific to your line. The pipeline runs on the same Android tablets your line operators already carry.
A complete working recipe in Python. Open-source on GitHub. Runs 100% on-device.