Computer vision
Detection, tracking and pose estimation that run in real time on cameras, boards and phones, including the vision behind AR and virtual try-on.
AI models that run on the device
EdgeOps.AI takes vision, speech and language models from data to production on the hardware you ship: phones, NVIDIA Jetson, NXP i.MX boards and Alif Semiconductor microcontrollers. Real time, private, and working without a connection.
Case study
AI FitLift Coach
A fitness app needed to know which dumbbell a user picked up and count reps without manual input. We built the detection, tracking and pose models and got them running on the phone.
- Real-time dumbbell detection trained on workout footage we collected and annotated
- Frame-to-frame tracking that stays steady through motion and blur
- Pose estimation for feedback on form and technique
- Runs on phones and wearables with no cloud round trip
On-deviceReps12
Weight20 lb
Schematic animation of detection and pose tracking
What we build
Speech
Speech recognition, text-to-speech and wake word detection that work offline with low latency.
On-device language models
LLMs and audio LLMs compressed to run locally, for assistants and voice interfaces that keep user data on the device.
Audio classification
Always-on models that recognize sounds and acoustic events at low power, on the device and without a connection.
Where the models run
Memory, power and latency limits on the target device guide model selection and optimization.
Microcontrollers
Always-on models that fit in on-chip memory and run on microcontroller NPUs: wake words and short voice commands, sound events, and sensor or vision triggers.
- Alif Semiconductor
Embedded boards
Vision, speech and small language models on application processors with NPUs or GPUs, running next to your cameras and microphones.
- NXP i.MX
- NVIDIA Jetson
Wearables, phones and edge servers
Real-time vision and pose on wearables and phones. Larger models such as LLMs and audio LLMs, plus multi-stream video, on phones and edge servers, close to the data rather than in the cloud.
- Wearables
- Mobile phones
- Edge servers
An engineering team for AI on the edge
EdgeOps.AI is an applied AI company based in Islamabad, Pakistan, working with clients in the United States, the United Kingdom and Canada. The same engineers take each model from data to the device, so the version we test is the version that ships.
- 01
Annotate
Collect the right data and label it precisely, with expert annotators and model-assisted labeling.
- 02
Train
Build and train vision, speech, audio and language models around your data and target hardware, then validate them on unseen data.
- 03
Optimize
Quantize, prune and compile the model for the target, from GPUs down to microcontroller NPUs, so it runs in real time within its memory and power budget.
- 04
Deploy
Integrate the model into your product, from mobile apps and Linux boards to microcontroller firmware, with no dependence on the cloud.
- 05
Improve
Measure performance in the field and retrain as conditions change.
Blog
The power and ethical dilemma of AI image generation models
How modern AI image generators work, where the ethical risks lie, what provenance and new rules do about them, and what changes when generation runs on-device.
Virtual try-on (VTO)
How virtual try-on works today, from live AR overlays to diffusion-based garment try-on, what is still hard, and why it belongs on the device.
Sounds right: an introduction to audio classification
What audio classification is, how it is built today with pretrained encoders and zero-shot models, and how to run it on microcontrollers and edge devices.
Get in touch
For a project, support with something we built, or anything else, email us and we will get back to you.