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.
Edge AI · MLOps
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.
On-deviceREPS12
Weight20 lb
FormGood
InferenceReal time
NetworkNot required
- Object detection
- Tracking
- Pose estimation
- Speech recognition
- Text-to-speech
- Audio classification
- On-device LLMs
- Audio LLMs
About us
An engineering team for AI on the edge.
EdgeOps.AI is an applied AI company based in Islamabad, Pakistan. Our engineers build vision, speech and language models and make them run on the hardware our clients ship, from microcontrollers to phones and edge servers.
We handle every stage: collecting and labeling data, training, optimizing for the target device and deploying. After a model ships, we keep measuring it in the field and improving it.
- Edge first
- Whether it is a phone, an embedded board or a microcontroller, we design for the target device from day one instead of adapting a cloud model as an afterthought.
- End to end
- Data, training, optimization and deployment under one team, so nothing gets lost between handoffs.
- Across modalities
- Vision, speech, audio and language models, so we pick the right approach rather than the one we know.
- Scoped to the project
- We plan around the target hardware, the data and the constraints each project brings.
What we build
Models for vision, speech, audio and language.
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 it runs
From microcontrollers to edge servers.
We fit the model to the hardware you ship, not the other way around. Each class of device sets its own budget for memory, power and speed.
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
We also work with many other boards and chips.
How it works
One pipeline, from raw data to a model in your users’ hands.
Most AI projects stall between the notebook and the product. We run the whole path, so the model we test is the model that runs on the device.
- 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.
Case study
AI FitLift Coach: rep counting and form feedback, running on the phone.
A fitness app needed to know which dumbbell a user picked up and count every rep, without manual input. We built a vision system that does it live on the phone.
Read the case study- 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
From the blog
All posts Notes on applied AI.
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.
Contact
Get in touch.
For a project, support with something we built, or anything else, email us and we will get back to you.
Email us- info@edgeops.ai
- Office
- Blue Area, Islamabad, Pakistan