Supercharge your ML workflows with RCL.
Random Contrast Learning (RCL™) streamlines AI model creation – delivering state-of-the-art accuracy and rapid training on CPUs. RCL runs natively on Windows and leading Linux distributions, so you can build, iterate, and deploy directly within the environments your data science workflows already use.
PrismRCL’s Release 2.7.1 — Now on macOS + ARM
Our CPU-optimized ML engine now runs across Windows, Linux, and macOS with native x86_64 and ARM builds. Linux builds are validated on Ubuntu 22/24, RHEL 9/10, and Fedora 42. Train high-accuracy models from the command line and explore LLM training mode and Auto-Optimize for fast parameter search.
Free for 30 days.
RCL x GPT-4o ChatBot
By leveraging OpenAI’s GPT-4o API, we’ve created a prototype chatbot to allow an accessible window into the capabilities of RCL, inviting you to explore its potential in an intuitive, conversational format.
Latest Updates
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How Federated Learning and RCL® Are Changing AI Validations?
Healthcare innovation continues to streamline diagnostic workflows. In medicine, if AI is being used, nothing reaches patients without rigorous AI validation, whether it’s a new drug or a diagnostic tool. The questions is no longer if AI will shape clinical care, but...
How Collaboration with Federated Learning is Securing Medical AI
Healthcare has often been at the forefront of concerns regarding privacy and security. Patients depend on the integrity of people and systems for everything from sharing medical records with a physician to receiving therapy based on diagnostic instruments. One of the...
RCL®: Pioneering Shift Towards AI in Medical Imaging
Healthcare is at a turning point. While cloud-based AI has dominated discussions around medical imaging, hospitals and clinics around the world are exploring faster, more responsive care pathways and the infrastructure to support them. As patient volumes rise and...
Work with Lumina AI
RCL promises to advance AI and to expand its related markets as we improve existing AI workflows by replacing neural networks employed for classification, thereby reducing capital expenditure and increasing accuracy.
As CPU resources remain accessible, we believe that Random Contrast Learning will allow for a new market for AI and machine learning – enterprises without access to the capital or resources to build an AI practice.
For enterprises interested in deploying Random Contrast Learning at scale, please contact us for additional information.