We design AI system which are contextually more aware and react according to the environment. The design of such system takes inspiration from human psychology and Cognitive Abilities.

At the heart of our approach is an Appraisal Engine, inspired by how humans evaluate situations before acting. By integrating working memory and episodic memory, the system maintains relevant context, recalls prior experiences, and supports more coherent reasoning across extended interactions.

We also employ persona-driven inner reasoning, enabling AI agents to adapt their communication style, priorities, and decision-making to the role they are designed to perform.

Further using Mechanistic Interpretability we activate or ablate portions of model which are not conducive to our purpose.