Computer vision development services.
Custom computer vision systems engineered around your product, application and operating environment. We work across computer vision, AI, embedded systems and software to turn cameras and visual data into reliable product capabilities, whether processing happens on the device, in the cloud or across both.
Computer vision is more than training a model.
A successful vision system has to work as part of a real product. The camera, optics, data, hardware, software, processing architecture and operating environment all influence the final result.
The real world is not a dataset.
Lighting changes. Scenes change. Cameras move. Subjects appear from different angles and distances. Environmental conditions introduce noise and variation that may never appear in development data.
A production computer vision system has to account for those conditions from the beginning.
Every application has different constraints.
Some systems need low latency at the edge. Others benefit from cloud computing, centralised processing or scalable infrastructure. Some operate with limited power and connectivity, while others have access to substantial compute.
The right architecture depends on the product, not on a predefined deployment model.
Vision has to integrate with everything around it.
The camera and model are only part of the system. Image capture, processing, embedded software, applications, connectivity, databases, cloud infrastructure and user interfaces can all be required to turn computer vision into a usable product capability.
A flexible approach to computer vision engineering.
We do not start with a fixed technology stack or force every project into the same architecture. We start with what the product needs to achieve and engineer the system around it.
Technology follows the application.
On device inference, cloud processing and hybrid architectures all have their place. We select the approach around factors such as latency, connectivity, privacy, compute requirements, operating cost, scalability and the environment in which the system will operate.
Computer vision meets embedded engineering.
Many vision products have to exist within physical hardware. That means cameras, sensors, processors, firmware, operating systems, power, thermal constraints and mechanical design can all affect the solution.
Our computer vision work can therefore be developed alongside the embedded system rather than treated as a separate software layer.
Build around real conditions.
The system is considered against the environment in which it will actually operate. Lighting variation, movement, vibration, contamination, changing backgrounds, camera positioning and other sources of variability become part of the engineering process.
Measure what matters.
Model metrics alone do not define a successful product. Depending on the application, we evaluate the complete system across accuracy, latency, throughput, memory, power, reliability and other practical requirements.
The objective is not simply a model that performs well in development. It is a computer vision system that performs its intended function within the constraints of the product.
Computer vision engineering for custom applications.
Every computer vision project is different. Rather than limiting development to a predefined list of vision functions, we work from the visual problem your product needs to solve. We can develop systems for analysing images, video, live camera feeds and other visual data, then connect the resulting intelligence to the wider product.
Computer vision development
Development of custom vision capabilities around your application's requirements, data, hardware and operating environment.
AI and machine learning for vision
Model development, training, evaluation and optimisation using project specific data and requirements. The approach is selected according to the visual problem, available data and intended deployment.
Image and video processing
Design and implementation of the processing pipeline required to acquire, prepare, analyse and transform visual data reliably.
Embedded computer vision
Computer vision engineered for embedded devices and resource constrained systems, including integration with processors, cameras, sensors, firmware and embedded Linux environments.
Edge and cloud computer vision
Architectures designed for local processing, remote processing or hybrid systems where edge devices and cloud infrastructure work together.
Vision system integration
Integration of computer vision into existing or new products, applications and platforms, including software interfaces, APIs, dashboards, databases, connectivity and other surrounding systems.
Computer vision optimisation
Improving an existing vision pipeline, model or deployment to meet practical requirements for performance, latency, memory, power or reliability.
Custom computer vision systems
When the required capability does not fit a conventional description, we develop the system around the actual problem. The technology is chosen after understanding what the product needs to achieve.
The engineering around computer vision.
Camera sensors, lenses, fields of view, working distances and placement selected around the visual task and physical environment.
Reliable image and video capture, buffering, format conversion, exposure handling and preprocessing using appropriate interfaces and software frameworks.
Models developed and evaluated using project data, representative datasets or data collected during the engagement.
Models and processing pipelines deployed to the environment that best fits the application, from embedded hardware to cloud infrastructure.
Use of available CPU, GPU, NPU or DSP resources where acceleration can provide meaningful performance benefits.
The surrounding software required to turn computer vision into a working product, including device software, applications, interfaces and integrations.
Remote processing, APIs, databases, storage, monitoring and other cloud components when the system benefits from a network connected architecture.
Computer vision built around your product.
Tell us what you need the system to see, understand or automate. We will determine the appropriate technical approach, hardware and software architecture for the application.
Technologies and platforms.
We work across the software, hardware and infrastructure layers required to build computer vision systems.
Computer vision and AI
OpenCV, machine learning frameworks, model development and computer vision processing pipelines.
Model deployment
ONNX, TensorFlow Lite / LiteRT, TensorRT and other deployment runtimes selected according to the target environment.
Image and video pipelines
V4L2, GStreamer, MIPI CSI, USB cameras and application specific image processing pipelines.
Embedded platforms
Embedded Linux systems, NVIDIA Jetson platforms, application processors and embedded SoCs, with development on JetPack, CUDA, TensorRT and DeepStream where the target is a Jetson module.
Hardware acceleration
CPU, GPU, NPU and DSP based acceleration depending on the capabilities of the target hardware.
Programming
C, C++ and Python, alongside the software technologies required by the wider product.
Cloud and connected systems
APIs, databases, remote processing, data services and cloud infrastructure where the application requires connected computer vision.
Engineering and validation
Git, debugging and profiling tools, performance measurement, hardware analysis and development workflows for building and validating production systems.
How a computer vision project works.
There is no single development path for every project. The process is adapted to the maturity of the product, the available data and the technical requirements.
Understand
We first understand the product, visual problem, operating environment, available hardware, existing software and the outcome the vision system needs to provide.
Explore
The appropriate computer vision approach, architecture, hardware and processing strategy are evaluated against the application's requirements.
Develop
Models, image processing pipelines, embedded software, cloud components or other required system elements are developed according to the selected architecture.
Integrate
The vision system is connected to the product around it, including cameras, hardware, applications, interfaces, connectivity and other required components.
Validate
Performance is evaluated using representative data, target hardware and realistic operating conditions. Accuracy is considered alongside practical factors such as latency, throughput, memory, power and reliability.
Deploy and hand over
The completed system, source code, models, configuration, documentation and other project assets are prepared for deployment and continued development.
Frequently asked questions.
What does a computer vision development company do? ▼
A computer vision development company designs and builds systems that allow products to interpret images, video or other visual information. This can include machine learning models, image processing pipelines, camera systems, embedded software, cloud processing and the integrations required to turn visual intelligence into a working product.
What type of computer vision systems can you develop? ▼
We develop custom computer vision systems around the application's requirements rather than limiting projects to a fixed set of functions. The system might involve image analysis, video processing, machine learning, embedded vision, automation, inspection, monitoring or another vision based capability.
Does computer vision have to run on the device? ▼
No. The appropriate architecture depends on the application. Computer vision can run entirely on the device, entirely in the cloud or across both. We select the approach based on factors such as latency, connectivity, privacy, compute, scalability and cost.
Can you work with an existing product? ▼
Yes. Existing products can be assessed to determine what hardware, software, camera, compute and architectural changes may be required to introduce or improve computer vision capabilities.
Can you develop the hardware as well as the vision software? ▼
Computer vision often depends heavily on the hardware around it. We can work across cameras, embedded processors, embedded Linux, firmware and surrounding software where the project requires an integrated engineering approach.
Do you need training data before starting? ▼
Not necessarily. Existing data can be used where available, while additional data can be collected or structured during development depending on the application and visual problem.
Can you optimise an existing computer vision system? ▼
Yes. Existing models and pipelines can be reviewed and optimised for accuracy, speed, memory usage, power consumption, hardware compatibility or deployment requirements.
What platforms can computer vision run on? ▼
That depends on the application. We work with embedded Linux systems, NVIDIA Jetson platforms, application processors, embedded SoCs, accelerated hardware and cloud environments, selecting the platform around the requirements of the project.
What do clients receive at the end of a project? ▼
The exact deliverables depend on the engagement, but can include source code, trained models, deployment files, processing pipelines, integration code, build configurations, documentation and other engineering assets required for continued development or deployment.