Intelligence Simplified

Pioneering the next generation of Computing and AI with the Universal State Machine

New Era of Computing

Privacy, Decentralization, & the Future of Personal Computing

In the 1980s, computing could have remained locked in centralized mainframes. Instead, personal computers put power in the hands of individuals, igniting a revolution.

Today, we face a similar choice; will intelligence be controlled by a few, or belong to everyone?

The Universal State Machine is the next leap forward. AI that’s lightweight, private, and untethered from corporate silos. The future isn’t just about intelligence. It’s about who owns it.

Revolutionary Not Evolutionary

Ren’s Universal State Machine redefines AI by breaking free from the limitations of deep learning. It outperforms traditional neural networks, ushering in a new era of affordable, decentralized, and scalable intelligence for personal computing.

Beyond Neural Networks

A fundamental shift away from traditional neural networks, the USM focuses on systemic interaction, emergent intelligence, and modular adaptability — unlocking new levels of efficiency and scalability.

Built on principles of energy distribution, the USM dynamically optimizes resource use — delivering intelligence that is powerful, efficient, and sustainable without brute-force computation.

The USM models knowledge as an interconnected system, mapping ideas, entities, and relationships with structured intelligence — allowing for deep contextual reasoning and adaptability.

Taking cues from biological intelligence, the USM mirrors the adaptability and efficiency of natural systems — enabling self-organization, continuous learning, and real-time evolution.

Beyond Neural Networks

A fundamental shift away from traditional neural networks, the USM focuses on systemic interaction, emergent intelligence, and modular adaptability — unlocking new levels of efficiency and scalability.

Built on principles of energy distribution, the USM dynamically optimizes resource use — delivering intelligence that is powerful, efficient, and sustainable without brute-force computation.

The USM models knowledge as an interconnected system, mapping ideas, entities, and relationships with structured intelligence — allowing for deep contextual reasoning and adaptability.

Taking cues from biological intelligence, the USM mirrors the adaptability and efficiency of natural systems — enabling self-organization, continuous learning, and real-time evolution.

Unlock the Future of AI with Ren’s Whitepaper

Explore the groundbreaking advancements and revolutionary vision that power our next-gen AI technology. Dive deep into our approach, methodology, and the impact of our innovative solutions.

Next-Generation Capabilities

USM-driven AI systems are breathtakingly fast, energy-efficient, and endlessly adaptable — delivering intelligence that learns, optimizes, and evolves in real time.

01

Energy-Efficient Operation

The USM delivers AI capabilities with dramatically lower energy requirements than traditional deep learning models. Its efficient computation reduces environmental impact, making it the ideal solution for sustainable, resource-conscious organizations.

02

Modular and Scalable

USMs are designed for modularity, allowing individual components to be added, replaced, or combined without affecting the overall system. This adaptability ensures scalability for diverse applications without requiring costly re-engineering.

03

Real-Time Learning

USMs learn continuously, adjusting their knowledge graph on-the-fly to new inputs without the need for retraining. This ability enables real-time adaptation to evolving environments and data, ensuring consistently accurate results.

04

Decentralized Intelligence

The USM operates directly on local devices, ensuring privacy-first AI solutions without reliance on cloud infrastructure. By decentralizing intelligence, the USM empowers users to control their own data and AI systems.

Background expertise

Maxwell Braun graduated with a BA in Political Economy from the Jackson School of International Studies at the University of Washington. He then began his career in Financial Services with UBS, acting as a technology liaison and helping drive AML efforts. While at UBS, he obtained his FINRA Series 7 and Series 66 licenses. Max then went on to become a Senior Financial Analyst at BNY Mellon in Seattle, where he refined KYC compliance protocols and become West Coast Associate of the Year in 2022. Max grew up in Piedmont, CA.

Background expertise

Rukmal Weerawarana graduated with a BBA in Finance and Business Economics from the Foster School of Business at the University of Washington.

In College, he contributed to various research projects, including targeted drug design for HIV patients, a CubeSat that is currently in orbit, and one of the world’s first functioning Hyperloop Systems.

As a Graduate Student at the Stevens Institute of Technology and a Research Fellow at Rensselaer Polytechnic Institute, he worked on ranking in knowledge graphs, and designing algorithms for processing sensorimotor data for BCI-driven robotic prosthetics.

After graduating with an MS in Financial Engineering from Stevens, Rukmal became a Software Engineer at ExtraHop Networks in Seattle. There, he worked with Big Data systems to develop cybersecurity algorithms and machine learning cloud infrastructure. He then contributed to kickstarting a (non-profit) technology-enabled school in Sri Lanka and was the Lead Data Scientist at Capitol AI in New York City in early 2023.

Rukmal is from Colombo, Sri Lanka.