Computer Vision Development
What we do
Vision that has to keep up with the real world cannot afford a round trip to a server. We build computer vision that runs where the camera is — on the phone’s GPU or on dedicated embedded hardware — so tracking stays real-time and costs stay flat.
Problems we solve
Typical reasons clients come to us for this work:
- Objects need tracking in real time, faster than a cloud round trip allows.
- Per-image API costs make a cloud vision service uneconomic at volume.
- Imagery is sensitive and must not leave the device.
- Vision has to run on constrained embedded hardware, not a workstation.
- The camera pipeline needs custom processing that off-the-shelf SDKs will not do.
Who it’s for
- Sports and performance products tracking fast-moving objects.
- Hardware companies adding sensing or inspection capability.
- Privacy-sensitive applications that cannot send images off-device.
- Products needing AR or spatial understanding of a real environment.
Technologies & capabilities
On-device ML
CoreML on iOS, real-time inference without external services.
GPU acceleration
Metal shaders on iOS, GPU-accelerated pipelines on Android.
Embedded vision
Real-time object tracking running on dedicated embedded hardware.
AR & spatial
RealityKit and ARCore for 3D understanding and placement.
Supporting cloud
Serverless AWS for configuration, storage and results — deliberately off the critical path.
How we deliver
1. Understand
We start with the hard part — the constraint the project actually turns on. Latency, power budget, data volume, regulatory limits. That is what shapes the architecture.
2. Prove
Where there is genuine technical risk, we prove it early with a focused prototype rather than discovering it late in the build.
3. Build
Iterative delivery with working software you can see. Test-driven where it earns its place, continuous integration throughout.
4. Harden
Scale, failure modes and cost. We design for intermittent connections, awkward data and real concurrency rather than the happy path.
5. Support
Handover that leaves you able to run it — infrastructure as code, documentation and, where wanted, ongoing help.
Relevant work
Frequently asked questions
Does the processing have to happen in the cloud?
No, and usually it should not. We build vision that runs on the device — either the phone’s GPU or dedicated embedded hardware — which keeps latency low, cost flat and imagery private.
Can it run on both a phone and a standalone device?
Yes. We have delivered exactly that: one shared pipeline where vision runs either on the user’s phone or on a purpose-built embedded device needing no app at all.
What about real-time performance?
That is the design constraint we start from. We have used Metal GPU shaders and CoreML to build real-time image-processing and evaluation pipelines that keep pace with live camera input.
Can you combine vision with AR?
Yes. Spatial understanding and AR placement are closely related problems, and we have shipped production AR apps on both iOS and Android.
Related services
Projects like these often involve embedded software development, AI and machine learning development.
Start a project
Tell us what you’re trying to build and the constraint you’re up against. We’ll tell you honestly whether it’s something we can help with.