OUR VISION

‍At RabbitsHat, we are developing a Big Psychological Model and Expirience management system 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 and establish genuine trust. 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:

‍Relational Memory Engine turns every user interaction into a structured, time-stamped episode inside a knowledge graph, giving the agent a living autobiography it can query in milliseconds. Because each fact is linked to its origin, the system can trace provenance for safety checks, reconcile new information with prior beliefs, and maintain coherent preferences across sessions, devices, and domains. In effect, memory becomes the control layer that manages an agent’s entire experience—what it has promised, what it has learned, and how it should build on that history in the very next utterance.Complementing this, the

‍Big Psychological Model supplies the inner motives, appraisals, and emotion dynamics that guide how the agent behaves in a given role. Instead of relying on surface chat patterns, the model encodes drives like competence and affiliation, evaluates events through context-specific lenses, and tunes the agent’s tone, urgency, and action policies accordingly. Coupled with relational memory, it enables deep, stable role-play that evolves with every conversation yet never drifts outside clear psychological boundaries—delivering assistants that feel consistent, trustworthy, and genuinely attuned to human goals.

We’re releasing an open API that lets game studios and AI-app developers plug directly into our Relational Memory Engine and Big Psychological Model. A handful of endpoints allow you to write new events, pull context, assign roles, and instantly receive behavior in which goals, emotions, and decisions arise as naturally as they would for a living character. Lightweight SDKs for Unity, Unreal, and popular back-end frameworks make it a two-line integration, whether you’re shipping a mobile RPG, a collaborative SaaS co-pilot, or an educational chatbot.

On top of the API, we’re building a creative platform for authoring AI personalities where writers, artists, and designers — no ML skills required—can craft full characters: define personalit, communication patterns, personal goals, and even growth arcs. Each persona is automatically backed by cloud-hosted memory and a library of motivational patterns, so it evolves in real time as players or users interact. Collaborative editing, version control, and engagement analytics turn what used to be deep R-and-D into a straightforward, highly creative workflow for any content team.

‍OUR TEAM
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Alexander EliseenkoCEO, 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 KulikovCTO, 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.