Quantum-HPC

In 2006, we developed a Distributed -Shared-Memory Architecture for small Supercomputing Grids. A similar architecture was later used by IBM in their first "Watson" computers.  

As well, we applied our compute architecture to the Rosetta protein folding project, often having all ten spots on their "top ten" list.

Since then, we've designed a distributed GPU architecture, combined with a DSM Server grid design. Finally, combining this with virtual quantum emulation - which gives us the capabilities of both quantum and classical computing.

This allows machine learning and modern AI.models to be used on proprietary (S)ML models - for highly confidential information and clear insights.

Multi-Sys  AI

Approaching 2025, it is clear that no one LLM software is or will remain superior to others.  

Instead, our unique hardware design allows us to cost-effectively use ALL of the best current LLM models: (OpenAI/GPT; Google/Gemini; Anthropic/Claude; Meta/Llama; Mistral Large; Baidu/Ernie;).

Then we have each Grid, with it's own model, compare & share the results with the others.

Thereby allowing each model to learn from all instances. 

Then, iterating as needed until very high quality convergence.

CyberSecurity

Given the multitude of Cyber attack vectors (OS, Server, Admin, BIOS, Clent, IoT, etc.) it is impossible for any one company to offer the "best of breed" in every part of the stack.  

We use ours when it's the best.  We use other good providers when they have the best part of the stack.

While we have a "Who's Who" of very, very good people we work with - Our clients are necessarily confidential.