Kavara's approach is rooted in decades of research in energy-based models, now made tractable for real-time deployment. We didn't invent the physics. We made it work.
Artificial intelligence has conquered language. But there is an entire kingdom -- the kingdom of real-time sensory intelligence -- that remains unruled.
Transformers and large language models built a $6 trillion ecosystem. LLMs are brilliant at language, reasoning, code generation, and text analysis. This kingdom has its rulers -- OpenAI, Anthropic, Google -- and its infrastructure is well understood.
The Boltzmann machine is 40 years old. Energy-based models have deep theoretical roots -- but nobody made them tractable for real-time use. This kingdom has had no kings. Until now.
AI can read a 10-K filing and summarize the risk factors. But it cannot feel the tremor in the data that precedes a regime change. The cognitive layer exists. The sensory layer does not. That gap is the reason portfolio managers still get blindsided, the reason infrastructure fails without warning, and the reason "AI-powered" surveillance still misses what matters. Kavara exists to close that gap.
EBMs are a fundamentally different model family from transformers -- not a tweak, not a variant, but a different way of understanding the world.
Imagine a ball resting at the bottom of a bowl. The ball naturally settles into its lowest energy state -- the bottom. This is how EBMs model the world.
The system learns the shape of "normal" -- the energy landscape of your data. Familiar patterns sit at low-energy positions, like a ball settled at the bottom of a bowl. When something anomalous appears, it sits at a high-energy position -- like a ball perched on the rim, unstable and immediately detectable.
Unlike transformers that learn to predict the next token, EBMs learn the shape of the world itself. They don't need to be told what to look for. They know what normal feels like, and they sense when something deviates.
The human brain operates with cognitive, motor, and sensory systems working in concert. Current AI has replicated the cognitive layer. The sensory system remains absent. This is not a tuning problem -- it is an architectural one.
The sensory layer is not a nice-to-have. It is the missing third of intelligence.
Ulysses is the first system purpose-built to fill it.
Kavara's work sits within a growing body of research from leading institutions. The energy-based approach is being validated by some of the most respected minds in AI and quantitative science.
Theory matters. Results matter more. Here is what Ulysses delivers when energy-based models are made tractable for the real world.
When COVID-19 hit global markets in early 2020, standard statistical models detected the regime change with just 26% recall -- missing nearly three-quarters of the signal. Ulysses detected the regime shift with 100% recall, identifying the transition as it happened, not after the fact.
This wasn't post-hoc analysis. It was real-time detection, achieved with zero pretraining on pandemic scenarios and zero labeled crisis data. The energy landscape simply recognized that the world had changed.
Explore the research foundations, frameworks, and technical briefings behind Kavara's approach.
The complete framework document explaining the bifurcation of AI into cognitive and sensory intelligence, and why both kingdoms are necessary.
Request DocumentA focused technical walkthrough of the Ulysses architecture, EBM foundations, and deployment model. Tailored for technical and executive audiences.
Request BriefingTraces the intellectual lineage from Hinton's Boltzmann machines through four decades of energy-based model research to Kavara's real-time tractable architecture.
Request PaperThe research is published. The results are measurable. The next step is seeing how Ulysses applies to your domain.