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 RCL® Identifies Financial Threats Early By Using AI in Risk Management?
Financial institutions face an unprecedented convergence of threats. Market volatility, sophisticated cyber attacks, evolving fraudulent schemes, and regulatory complexity pose risks that shift faster than traditional management systems can track. The challenge now...
How CPU-Optimized AI Is Powering Near Real-Time Insurance Fraud Detection?
Every insurance claim is a pledge. Policyholders anticipate prompt, equitable results. Insurers battle behind the scenes with manual reviews, sluggish insurance fraud detection, and growing overhead. That’s why insurance fraud detection has become a key priority. It...
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...
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.