Bringing the future to AI development and design

Our patented 'AI building AI' technology reduces the complexity and guesswork in designing efficient and high-performance deep learning solutions for real world applications.

Working with Deep Neural Networks

Because of their tremendous complexity, designing and implementing deep neural networks is difficult for even the most skilled experts. The process typically involves: A.) choosing a public reference design that ‘seems’ appropriate for the task at hand; B.) modifying and training it for that particular task; C.) manually fine tuning and iterating laboriously until the result is ‘good enough’.

Our Approach: Generative Synthesis

Our Generative Synthesis technology uses AI itself to understand a network at a foundational level and then employ this understanding to generate a number of new and highly optimized versions of said network. It can do this as it tackles the complexities of such networks with powerful AI - AI that identifies inefficiencies in the network impossible for a human to uncover.

The Result

The result are new and entirely unique networks that are not only significantly smaller, but infers faster without sacrificing functional accuracy. Designers collaborate with the Generative Synthesis platform to create the right network for the task at hand. Moreover, the understanding garnered by the technology enables explainable deep learning, wherein the platform can elucidate how the network is reaching its conclusions across an entire dataset or a singular inference.

DarwinAI™ opens up possibilities for your deep learning neural networks.

How We Can Help


The Generative Synthesis platform

The Generative Synthesis platform allows your team to quickly address performance concerns by facilitating AI-collaborative design and automated optimization.

Pre-optimized models

We also offer numerous models that have been highly optimized for common tasks (classification, detection, segmentation), and which can be further optimized based on your training data.

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