Talbot West delivers enterprise intelligence orchestration. Lucidity Sciences has developed a superior machine learning ML Solutions model. Each firm complements the other.
SALT LAKE CITY, UT, UNITED STATES, December 9, 2025 /EINPresswire.com/ — Talbot West, a leading AI enablement and digital transformation firm, today announced a strategic partnership with Lucidity Sciences, creator of the Lumawarp machine learning engine. The partnership unites Talbot West’s enterprise transformation capabilities with Lucidity Sciences’ novel approach to pattern recognition and predictive analytics, offering organizations a powerful new pathway to AI-driven competitive advantage.
Talbot West and Lucidity Sciences have teamed up for a strategic partnership. Brian Pasi, Stephen Karafiath, Richard Wellman, Alan Mullenix, and Steve Larsen are all standing. Alexandra Pasi, PhD, and Jacob Andra are sitting down.
From left to right, Brian Pasi, Stephen Karafiath, Richard Wellman, Alan Mullenix, and Steve Larsen are standing. Seated: Alexandra Pasi, PhD, and Jacob Andra
Lumawarp is a big step forward in machine learning for structured data. Lumawarp uses a mathematical framework based on partial differential equations and geometric manifold regularization, which is different from traditional methods that use deep learning or tree-based algorithms. This makes it more accurate and useful in general, and it runs nearly 300 times faster than the best old models.
Jacob Andra, CEO of Talbot West, said, “The enterprise AI landscape has become fixated on large language models, but many of the most important business problems need accuracy and speed that LLMs just can’t provide.” “Lucidity Sciences’ Lumawarp technology is best when accuracy is a must and milliseconds count. This relationship lets us give our clients tools that will really change their lives.
Dr. Alexandra Pasi, CEO of Lucidity Sciences, said, “The meaning of the word ‘AI’ has always changed because of changing public opinion and marketing trends.” Talbot West knows that various challenges need different tools, and that the best AI solutions generally use more than one method at the same time. They are the best partner for providing Lumawarp to businesses that need enterprise-grade AI infrastructure since they are dedicated to aligning technology to business needs.
Important Things About the Partnership
As part of the relationship, Talbot West will add Lumawarp to client solutions where its unique features (such as microsecond inference durations, better accuracy and generalization, and deterministic behavior) meet specific business needs. Clients of Lucidity Sciences will be able to use Talbot West’s full range of AI skills and enterprise design methods to get the most out of their Lumawarp deployments.
About Lumawarp
Lumawarp is a revolutionary machine learning system that uses a completely new mathematical framework based on partial differential equations to build the best models directly from training data. This method is more accurate than the best old models and can make inferences in microseconds. You can use Lumawarp on-premise, at the edge, or in the cloud. It works best in GPU-parallel processing scenarios. Business uses include healthcare, banking, manufacturing, logistics, energy, and scientific research.
About Talbot West
Talbot West is an expert in digital transformation for businesses. The company uses Fortune 500 systems architectural technique along with full-spectrum AI knowledge, such as computer vision, predictive ML, optimization algorithms, reinforcement learning, neurosymbolic approaches, and huge language models. Talbot West’s FRAME (Future-Readiness Assessment & Modernization Engineering) technique sees organizations as systems of systems and designs them to have full organizational intelligence.
About Lucidity Sciences
Lucidity Sciences makes cutting-edge machine learning tools that can find and predict patterns in structured data. Dr. Alexandra Pasi helped start the company, and its research-based methodology solves major problems with current ML methods, making models more accurate, more generalizable, and more efficient at using computers.
