We are building a new model architecture for artificial consciousness.

Today’s frontier models have pushed AI capabilities forward at extraordinary speed. But they all share a structural limitation: they are largely stateless systems. They do not compound intelligence through persistent internal experience in the way biological systems do.

Human intelligence improves by integrating memory, feedback, and lived context over time. Current LLMs approximate this through prompts, retrieval systems, databases, and external context windows. This works, but it is inefficient. The same information must often be repeatedly reintroduced into the model, increasing token usage, latency, and cost with every interaction.

We believe this is one of the core bottlenecks on the path to superintelligence.

Our work focuses on a new class of neuron designed to retain context across otherwise stateless layers. The goal is to create models that learn from ongoing interaction, preserve relevant state, and reduce dependence on external context injection.

We are developing alternative architectures for more efficient, memory-native intelligence. Since 2024, our team of engineers, researchers, and former founders has been working on a focused set of missing components required for autonomous, economically useful AI systems.

The early results are promising. In live demonstrations, our architecture reaches state-of-the-art-level performance while using 25 to 160 times fewer tokens in selected workloads.

Extraordinary claims require extraordinary evidence. We agree. That is why we prefer to show the system directly. Get in touch.