OUR VISION

‍At RabbitsHat, we are developing a Big Psychological Model that represents a fundamentally different approach to artificial intelligence. While much of the AI community focuses on scaling computational power and expanding knowledge bases, we believe that truly transformative AI assistants require something more—they need to understand and operate within the psychological frameworks that give meaning to human experience.The development of powerful AI assistants isn't simply a matter of processing more data or increasing computational efficiency. It requires systems that can comprehend emotional nuance, establish genuine trust, and maintain intrinsic motivation. These capabilities aren't merely "human-like features" but essential computational frameworks that enable AI to interact meaningfully in complex social environments.

Our work is informed by developments in cognitive science, psychological research, and human-AI interaction studies from institutions such as MIT's Affective Computing Lab, Oxford Internet Institute, and Stanford's Human-Centered AI Institute. We build upon the pioneering psychological contributions to AI, including Dietrich Dörner's PSI theory and model of cognitive-emotional information processing, which demonstrated how motivations, emotions, and cognitive processes can be integrated into comprehensive computational architectures.

ENHANCING HUMAN-AI ENGAGEMENT AND UTILITY

‍From a practical perspective, the Big Psychological Model significantly enhances both the usefulness of AI assistants and human engagement with these systems. Current AI assistants often face challenges in maintaining consistent user engagement and delivering truly valuable assistance across diverse contexts. Our psychological approach addresses these limitations through several key mechanisms:

Trust-Based Engagement
‍By incorporating psychological principles of trust-building, our AI assistants establish credibility with users through consistency, appropriate transparency, and acknowledgment of limitations. Research demonstrates that when humans trust AI systems, they engage more deeply, share more relevant information, and are more receptive to AI-generated suggestions and insights. This trust foundation transforms the human-AI relationship from transactional interactions to collaborative partnerships.

Controlled and Safe Interaction Flows
‍The psychological architecture of our systems enables more predictable and safe interaction patterns. By modeling human conversational expectations, emotional responses, and information processing limits, our assistants maintain interactions within appropriate boundaries while adapting to individual user needs. This controlled flow prevents the common problems of current AI systems, such as:
> Harmful or misleading information delivery
> Context collapse during extended interactions
> Inappropriate emotional escalation
> Misalignment between user needs and AI responses

Our psychologically-informed workflow management creates a safety framework that doesn't rely solely on content filtering but emerges naturally from the assistant's understanding of appropriate human interaction patterns.

Sustained Motivational Alignment
‍Perhaps most importantly, our Big Psychological Model enables assistants to maintain alignment with human motivational structures over time. By understanding not just what users request but why these requests matter within their broader goals and values, our assistants can provide contextually relevant support that evolves alongside changing user needs. This motivational alignment ensures that interactions remain valuable even as tasks and circumstances change.Studies from human-computer interaction research consistently show that perceived usefulness is the primary determinant of technology adoption and continued use. By enhancing trust, safety, and motivational alignment, our approach doesn't just make AI assistants marginally better—it fundamentally transforms their utility across domains.

THE PATH FORWARD

‍We believe that the path to powerful AI assistants lies not in endless scaling of existing models, but in the development of psychologically complete systems that mirror the fundamental structures of human cognition. Our ongoing research focuses on:

Integration of Psychological SubsystemsFollowing modular theories of mind proposed by cognitive science, we're developing interconnected modules that work in concert to create a cohesive intelligence capable of adapting across domains and situations.

Core Psychological CapabilitiesOur approach emphasizes implementing key psychological capacities including theory of mind (understanding others' mental states), metacognition (thinking about one's own thinking), intrinsic motivation (self-directed goal pursuit), emotional regulation (managing response to stimuli), and identity continuity (maintaining a coherent self-concept).

Learning Through Social ExperienceRather than training solely on static datasets, our systems learn through simulated social experiences that mirror human developmental trajectories. This approach, supported by developmental psychology research, creates AI assistants that understand social dynamics at a fundamental level.

‍OUR TEAM
‍
Alexander Eliseenko
CEO, Business Logic & Psychological Architecture
Alexander combines psychology expertise with over 12 years in organizational consulting to architect the psychological frameworks powering our AI systems. His background in change management and digital transformation, coupled with 5+ years developing ML system logic, enables the creation of AI Assistants with psychological authenticity. His academic connection as a lecturer at the Higher School of Economics (HSE) brings theoretical rigor to our practical applications.

Andrey Kulikov
CTO, Technical Implementation & ML
With over 18 years of experience in IT, IT security, and Big Data, Andrey translates psychological models into robust, scalable technical architectures. His background includes 8 years as CEO of SocialLinks, where he led international expansion in web investigation and crime prevention solutions. Andrey ensures our psychologically-informed AI maintains high standards of security and performance.

Anna Smith
AI Client Simulator & Therapeutic Practice Coach
Anna represents our breakthrough in psychological modeling—a synthetic role-playing system designed for mental health professionals. She simulates realistic client scenarios while providing comprehensive analysis of therapeutic interactions, demonstrating how psychologically-informed AI can transform professional practice through meaningful feedback.

At Rabbitshat, we are developing the Big Psychological Model because we believe that truly powerful AI assistants must understand the psychological dimensions of human experience. This approach represents not just an enhancement to existing AI systems, but a fundamentally different vision of what artificial intelligence can be and how it can serve humanity.