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 Cross-Modal AI Frameworks Connect Text Images and Behavior using RCL®?
The upcoming AI wave isn’t about mastering any single data type and building on that. Rather, it will be about bringing them together. Enterprises these days are increasingly seeking a cross-modal AI framework that can unify structured data from different...
The AI in Cybersecurity: How RCL® Can Detect Changing Behavioral Patterns
Cyber threats are evolving - faster, smarter, and more difficult to detect.Traditional rule-based systems often fall behind, reacting only after an attack is underway. For this reason, cybersecurity teams are turning to AI, not just for detection, but for prevention....
How Does the Combination of RCL® and IoT Enhance AI in Demand Forecasting
Smart factories are emerging as the foundation of contemporary manufacturing and retail. Although factories have long embraced automation, real-time thinking, learning, and adapting systems are necessary for the next big step. That’s why AI in demand forecasting is...
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.