Computer vision development services.
We design and develop computer vision systems for products, equipment and connected applications. Our work can cover the complete vision stack, including cameras, image processing, machine learning, embedded software, application software and cloud infrastructure. We build the system around the requirements of the product, whether processing needs to happen on the device, in the cloud or across both.
Computer vision is more than a trained model
A production-ready vision system depends on more than model accuracy. Camera selection, optics, image quality, data, compute, software and operating conditions all affect real-world performance.
Real-world conditions change.
Lighting, camera position, distance, motion, vibration and reflections can all affect image quality and system performance.
A reliable vision system must perform consistently under the conditions in which the product will actually operate.
Architecture depends on the application.
Latency, compute, power, memory, connectivity, privacy, cost and scalability all influence the system architecture.
Processing may run on-device, at the edge, in the cloud, or across a combination of these.
Vision must work within the product.
Cameras and models are only part of the system. Image acquisition, preprocessing, inference, embedded software, connectivity and cloud services must work together to deliver a practical, maintainable product.
A practical approach to computer vision engineering
We do not begin with a fixed technology stack or assume that every problem should be solved in the same way. We start with the product requirements, the visual problem and the operating environment, then select the architecture and technologies that fit.
Technology follows the application.
Computer vision can run on a device, in the cloud or across both. We select the architecture around factors such as latency, connectivity, privacy, available compute, memory, power consumption, operating cost and expected scale.
Computer vision and embedded engineering work together.
Many vision systems are part of a physical product. The solution may depend on cameras, processors, sensors, firmware, Embedded Linux, power constraints, thermal limits and the surrounding electronics.
Our vision development can be carried out alongside the embedded system rather than treated as a separate software layer.
Build for the conditions the system will actually face.
We consider the environment in which the product will operate, including lighting variation, movement, vibration, changing backgrounds, camera positioning, contamination and other sources of visual variation.
These factors become part of the engineering and validation process rather than problems discovered after deployment.
Measure the complete system.
Model accuracy is important, but it does not define a successful product. Depending on the application, we also evaluate latency, throughput, memory usage, power consumption, reliability and overall system behaviour.
The objective is a vision system that performs its intended function within the actual constraints of the product.
Computer vision engineering for custom applications
Every vision project starts with a different problem. We work from the capability your product needs rather than limiting the engagement to a predefined set of vision functions.
We can develop complete computer vision systems, improve an existing implementation or take responsibility for a specific part of the stack.
Computer vision development
Custom computer vision capabilities designed around the application, available data, target 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, the available data and the intended deployment environment.
Image and video processing
Design and implementation of the processing pipeline required to acquire, prepare, analyse and transform image and video data reliably.
Embedded computer vision
Computer vision developed for embedded devices and resource constrained systems, including integration with processors, cameras, sensors, firmware and Embedded Linux environments.
Edge and cloud computer vision
Architectures for local processing, remote processing or hybrid systems where edge devices and cloud infrastructure work together.
Vision system integration
Integration of computer vision into new or existing products, applications and platforms, including software interfaces, APIs, databases, dashboards, connectivity and supporting systems.
Computer vision optimisation
Review and optimisation of existing models, pipelines and deployments to improve accuracy, latency, memory usage, power consumption, reliability or compatibility with the target hardware.
Custom computer vision systems
When the required capability does not fit a conventional solution, we design the system around the actual problem. The technology is selected after understanding what the product needs to do and the constraints it must operate within.
The engineering around computer vision
Camera sensors, lenses, fields of view, working distances and camera placement selected around the visual task and the physical environment.
Reliable image and video capture, buffering, format conversion, exposure control and preprocessing using the interfaces and software frameworks appropriate to the target system.
Models developed and evaluated using project data, representative datasets or additional data collected during development.
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 and DSP resources where acceleration can provide meaningful performance improvements within the target system.
The surrounding software required to turn computer vision into a working product, including device software, applications, interfaces and system integrations.
Remote processing, APIs, databases, storage, monitoring and other cloud components where the application benefits from a connected architecture.
Computer vision built around your product
Tell us what the system needs to see, identify, measure, track or automate. We can assess the problem, define the appropriate architecture and develop the vision system around your product requirements.
Technologies and platforms
We work across the software, hardware and infrastructure layers required to develop and deploy computer vision systems.
Computer vision and AI
OpenCV, machine learning frameworks, model development and computer vision processing pipelines selected around the requirements of the application.
Model deployment
ONNX, TensorFlow Lite, LiteRT, TensorRT and other deployment runtimes selected according to the target hardware and operating 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. Where the target is a Jetson platform, development can include JetPack, CUDA, TensorRT and DeepStream.
Hardware acceleration
CPU, GPU, NPU and DSP based acceleration selected according to the capabilities and constraints 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, testing and validating production systems.
How a computer vision project works
The development path depends on the maturity of the product, the available data and the technical requirements. We adapt the process to the project while keeping the engineering direction clear.
Understand
We begin by understanding the product, the visual problem, the operating environment, the available hardware, existing software, available data and the outcome the vision system needs to provide.
Explore
We evaluate possible vision approaches, system architectures, hardware and processing strategies against the requirements of the application.
Develop
Models, image processing pipelines, embedded software, application software, cloud components and other required system elements are developed according to the selected architecture.
Integrate
The vision system is integrated with 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 latency, throughput, memory, power and reliability where those factors matter to the application.
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 requirements of the application rather than limiting projects to a fixed set of functions. Depending on the product, this can include image analysis, video processing, machine learning, embedded vision, inspection, monitoring, automation, measurement or other vision based capabilities.
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 capacity, memory, scalability and cost.
Can you work with an existing product? ▼
Yes. We can assess an existing product to determine what hardware, camera, compute, software and architectural changes may be required to introduce or improve computer vision capabilities.
Can you develop the hardware as well as the vision software? ▼
Yes. Computer vision often depends heavily on the hardware around it. Where the project requires an integrated engineering approach, we can work across cameras, embedded processors, Embedded Linux, firmware and the surrounding software.
Do you need training data before starting? ▼
Not necessarily. Existing data can be used where it is suitable. Additional data can also be collected, structured and prepared during development depending on the visual problem and the requirements of the application.
Can you optimise an existing computer vision system? ▼
Yes. Existing models, image processing pipelines and deployments can be reviewed and optimised for accuracy, latency, memory usage, power consumption, hardware compatibility or other practical 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. The platform is selected around the requirements of the product.
What do clients receive at the end of a project? ▼
The exact deliverables depend on the engagement. They can include source code, trained models, deployment files, image processing pipelines, integration software, build configurations, documentation and other engineering assets required for continued development or deployment.