We are engineering the next frontier of AI — Self-Experience.

Current models are excellent at predicting the next word, but they are structurally incapable of real-time learning. They struggle with high-level goals rather than just low-level instructions, they also fail when reality defies their training data in messy, open-world situations.

In those moments, we need an architecture that can perceive what’s happening, update its state, reflect on outcomes, and adapt its plan through continuous feedback without expensive retraining cycles.

As Prof. Yann LeCun highlights, true intelligence requires architectures that can learn from observation and self-supervision almost as efficiently as humans do.

We tackle this challenge through Cognitive Engineering. This enables a continuous Learning loop of Perception, Reflection, Action, and Feedback, allowing the system to achieve Zero-Shot Reasoning to act intelligently on the first try, and One-Shot Adaptation to learn instantly from mistakes without any retraining. The result is AI that can handle unforeseen situations in real time whether it’s an enterprise system responding to a novel operational anomaly or a physical robot safely executing a task it has never encountered before.

Just as important, AI stops being a black box. It becomes a transparent, controllable asset you can trust with complex tasks, reducing the risk of unpredictable outcomes.