When Gary Singh Dhanda set out to build AI Twin, he confronted a problem that most artificial intelligence developers had sidestepped: how to create a personal operating system that learns individual personality without compromising the privacy that makes such intimacy possible. Dhanda, who was born and raised in Scotland, drew deeply on this background in shaping a system grounded in transparency, autonomy, and human‑centred design.
His solution, a framework that models behavioral patterns while keeping data under user control, earned him a 2025 Global Recognition Award for AI Twin’s personal operating system framework and concept, specifically its privacy-first architecture, personalization, accessibility, and sustainability focus, and the strength of its design and early demo. The recognition reflects not just technical achievement but a deliberate choice to build safeguards into the architecture rather than retrofit them later.
Dhanda’s approach departs from the prevailing model of artificial intelligence as a one-size-fits-all service. Where most systems offer broad capabilities with minimal customization, AI Twin adapts continuously to workflows, preferences, and decision patterns, functioning less as a tool and more as a cognitive partner. The distinction matters because it shifts the relationship between user and machine from a transactional to a collaborative one, allowing the technology to anticipate needs without eroding autonomy.
The platform scored highly across multiple metrics, market impact, technological advancement, and disruption of existing paradigms, because it addresses a widening gap as artificial intelligence has become increasingly capable. Dhanda recognized that personalization and privacy are not opposing forces but interdependent requirements. His work demonstrates that systems can learn deeply about individuals while respecting boundaries, a balance that has eluded many developers who treat security as an add-on rather than a foundation.
Designing Intelligence Around Individual Behavior
AI Twin functions as a privacy-first personal operating system and central intelligence layer, coordinating data and decisions across a user’s digital life. At its core is a proprietary personality engine that synthesizes behavioral and psychological frameworks into a single scoring matrix. This engine does more than catalog preferences; it models how users make decisions, structure their work, and express themselves. The system then applies that understanding to tasks like writing, planning, and execution, adjusting its output to match individual style and objectives.
Dhanda built privacy and security into the platform from the beginning, ensuring data sovereignty and regulatory compliance at every layer. This design choice directly addresses concerns about surveillance, misuse of personal information, and opaque data practices that have plagued other artificial intelligence applications. By making transparency and user control non-negotiable, Dhanda created a system that earns trust through architecture rather than assurances. The framework and concept are now complete, with a live demo available at www.ai-twin.app and the waitlist open at www.ai-twin.co.uk, with the full Version 1 app now in active development and launching soon.
The platform is designed to integrate with existing workflows, with plans to connect personal and professional data streams as users adopt the technology. Dhanda also requires that each AI-generated output undergo human review before publication, ensuring that users retain control of their digital identity and do not cede authorship to automated processes.
Measuring Value In Efficiency And Ethics
AI Twin is being developed to deliver meaningful improvements in how individuals manage repetitive, cognitively demanding tasks across life and work. Its long‑term roadmap includes a Pro version designed to support small teams, entrepreneurs, and independent professionals by preparing drafts, summaries, briefs, and structured information that frees people to focus on higher‑judgement responsibilities. Rather than replacing roles, the goal is to reduce administrative strain, improve clarity, and provide users with a calmer, better-prepared foundation for their daily decisions.
The system’s design anticipates integration with health, financial, and lifestyle data, but only with explicit, informed user consent. This broader scope positions AI Twin as a comprehensive life assistant capable of contextualizing advice, forecasting outcomes, and recommending actions that reflect personal preferences and longer-term goals. Dhanda’s insistence on consent and transparency distinguishes his platform from competitors who collect data first and address concerns later.
By incorporating safeguards, clear policies, and enforceable boundaries from the outset, Dhanda addresses global concerns about the ethical use of models that profile behavior. His approach treats governance not as an obstacle to innovation but as a precondition for responsible deployment. This perspective resonates with regulators, users, and organizations as they navigate the tension between capability and accountability in machine learning.
Redefining Collaboration Between Humans And Machines
Dhanda’s work signals a shift in how people and organizations conceive of digital representation. AI Twin presents a form of artificial intelligence that collaborates with users rather than replacing their judgment. This distinction becomes increasingly important as automation expands into domains that require nuance and context. The emphasis on personality modeling, privacy-first architecture, and explicit consent demonstrates that progress in this field need not come at the expense of accountability or individual autonomy.
This combination of technical depth and responsible design has positioned Dhanda as a significant figure in the movement toward more trustworthy and human-aligned artificial intelligence. His platform offers a practical example of how developers can build systems that respect user agency while delivering meaningful value. The framework he has established provides a reference point for others working to reconcile the competing demands of personalization, privacy, and performance.
Alex Sterling, spokesperson for Global Recognition Awards, observed that Dhanda’s achievement extends beyond incremental improvement in existing tools. “Gary Singh Dhanda has created more than an AI tool, since he has developed a framework for how digital twins can enhance human capability while respecting individual autonomy and privacy,” Sterling stated. “His exceptional work in combining personality science, security architecture, and ethical AI governance demonstrates the world-class innovation that Global Recognition Awards celebrates.” In building AI Twin, Dhanda has shown that the future of artificial intelligence depends not on what machines can do, but on how carefully developers choose what they should.
Together, these choices reflect Dhanda’s belief that the future of AI lies in calm, human‑aligned systems that strengthen judgment rather than replace it — a direction that positions AI Twin at the forefront of Scotland’s next wave of responsible innovation.
