Real-world embedded AI solutions.
Exploitable consumer focused AI solutions deployed on edge devices, reducing latency, improving energy efficiency and providing strong privacy guarantees.Contact
We support companies to exploit the true potential of Edge AI
We have proven experience in delivering real-time visual and analytical solutions at the edge for diverse industries, such as healthcare, automotive, manufacturing, retail and energy. Our clients benefit from our team’s expertise in AI, data and hardware engineering to move AI prototypes to products, ensuring that their applications are open, scalable, cost-effective, and deployed in a responsible fashion.
In production, real-world AI solutions in healthcare, automotive, retail and manufacturing.
Darwin Edge has dedicated team of 30+ engineers, scientists and developers passionate about embedded AI.
Software & Hardware
Optimising AI to run on a range of embedded platforms suited for specific use case requirements.
Use Case: Visual Quality Inspection
Embedded AI visual quality inspection improves the efficiency of the manufacturing production line by performing automated quality checks on the spot. Darwin Edge designed a Deep Learning based Computer Vision model to detect defects and perform defect segmentation (categorize the defects) in real-time.Read More
Combining new machine-learning techniques with…
Computer Vision (CV) platforms have considerably matured over the last few years. This has been enabled by the advent of Deep Learning, e.g., Convolutional Neural Network (CNN) based architectures that allow extracting meaningful and contextual insights from given images.
Predictive Analytics models and tools allow optimizing production (e.g., demand forecasting) and maintenance (e.g., predictive maintenance) decisions through more efficient understanding of patterns in current and historical data.
Edge AI, or TinyML, leverages the fact that training and deployment processes for a Machine Learning (ML) model are completely decoupled. It allows a trained ML model to be embedded in devices with limited memory and computational resources…
Most of today’s Machine Learning (ML) models are supervised and applied on a prediction/classification task. Given a dataset, the Data Scientist has to go through a laborious process called feature extraction and the model’s accuracy depends entirely upon the Data Scientist’s ability to pick the right feature set.
Reinforcement Learning (RL) refers to a branch of Artificial Intelligence (AI), which is able to achieve complex goals by maximizing a reward function in real-time. The reward function works similar to incentivizing a child with candy and spankings…
Federated learning, also known as Collaborative Learning, or Privacy preserving Machine Learning, enables multiple entities who do not trust each other (fully), to collaborate in training a Machine Learning (ML) model on their combined dataset; without actually sharing data…