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 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...
How CPU-Based AI for Risk Analytics Can Help the Retail Industry?
Retail is a fast-paced industry. Your top-selling winter coats are selling out in a flash, and then you have too much inventory, which is reducing your earnings due to unforeseen warm weather. Accurate forecasting is not only useful but also necessary for businesses...
How RCL® Helps AI in Fraud Detection Identify Weak Patterns?
AI-driven fraud is evolving fast, and it isn’t just a financial threat. From suspicious insurance claims to abnormal banking transactions, fraud may cost billions, disrupt corporate operations, and erode consumer confidence. The methods used to detect fraudulent...
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.