The world is moving
on-device.
NPUs / Mobile SoCs / Jetson / Drones / Embedded. Applied R&D for speech, vision, and ranking models that ship to the hardware (not the cloud).
Built on the tooling that ships.
NVIDIA Jetson / Hugging Face / ONNX / TensorRT. The open-source stack that lets models leave the cloud.
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Recent work
case study AuraFits: Multimodal AI Advisory Platform for Specialty Retail
Vision-driven product recommendations from 52 live Shopify inventories. One photo, a short conversation, ranked picks from real shelf stock.
Read Case Study
case study Semantic-Aware ASR Evaluation System for Edge Devices
On-device ASR evaluation at Fortune Global 50 scale. 90% faster evaluation, 4,000× throughput, deployed to smartphones.
Read Case StudyWhat we ship
Three capability areas. One constraint: it has to run on the hardware.
Speech
ASR, TTS, SLMs, voice agents. Whisper-class transcription on mobile SoCs. Sub-100ms voice pipelines. On-device assistants with no round-trip to the cloud.
Vision
Detection, tracking, segmentation, VLMs. Perception for drones, cameras, and embedded products. Tuned to Jetson, NPUs, and the compute budget you actually have.
Ranking
Recommendation, personalization, semantic search. On the device. Relevant results without shipping user data off the hardware.
Model optimization is how any of this ships.
Quantization / Distillation / Pruning / Compilation. TensorRT, ONNX, Core ML, TFLite. Every project ends with a model that fits the hardware and hits the latency budget.
Scope
Target device, latency budget, accuracy floor, data. We pick the model family and the optimization path.
Prototype on target
A model running on your actual hardware. Not a cloud demo, not a notebook.
Ship
Production weights, benchmarks, integration code, monitoring. You own everything.
Cloud inference has an edge problem.
- Latency: network round-trips kill real-time UX.
- Cost: per-inference API pricing doesn't scale at device volume.
- Privacy: audio, video, and user data can't leave the device.
- Availability: drones, wearables, and field hardware can't count on connectivity.
We've spent years making models smaller.
- Whisper variants on mobile.
- VLMs on Jetson.
- SLMs quantized to 4-bit with usable quality.
- Voice agent pipelines under 100ms.
- Recommendation ranking that fits in tens of MB.
If your AI has to run on the device, talk to us.
Drones / Consumer hardware / Mobile apps. If cloud inference isn't an option, that's our work.