We are a machine learning company that has created a new, ultra-secure standard for privacy as an alternative to PII. Our EdTech application engine transforms any content into an interactive experience, generating powerful, privacy-safe data assets that allow for long-term user support without ever storing personal identity. This is the future of digital security.

The challenge we solve is universal. The $7 trillion education and training market is filled with static, passive content. This leads to fragile knowledge and a poor return on investment. Organizations have valuable content, but they lack the tools to turn it into an effective, interactive experience that actually builds skill.

Our Ideal Client is an institution like Princeton, Oxford, or the University of Delhi. They possess a unique, world-class body of knowledge and a distinct way of thinking. Our platform allows them to create a proprietary AI that learns from their content and 'thinks' like their graduates, turning their curriculum into an intelligent, scalable data asset that serves as an alternative to PII.

Our solution is an 'Application Learning Engine.' We are a B2B SaaS company providing a white-label AI platform that transforms our partners' static content into a fully interactive, custom-branded learning application. This platform is a live case study: by using it, our partners aren't just teaching; they are building a valuable, proprietary data asset. Every interaction generates the high-quality, structured data that is essential for training next-generation AI models.

Our core technology is proprietary by its process. We've built an AI with a fundamental constraint: it cannot 'think' without being given resources. It only generates questions and exercises based on the curated source material a partner provides. This makes the engine incredibly safe—it cannot 'hallucinate' facts—and creates a clean legal framework for IP ownership. Our partners own their content; we provide the engine that brings it to life. Each partner is creating their own data asset.

This leads to our core data innovation: The Empirical Learning Engine. The system tracks anonymized learning patterns—like hesitation time or translation use—to understand how a cohort is struggling, not who is struggling. This creates a privacy-compliant alternative to PII-based metrics, providing invaluable insights. While our first application is education, this engine has broad horizontal applicability:

• Corporate Compliance: Verifying employee understanding of critical policies.
• Healthcare: Ensuring patient comprehension of treatment plans.
• Market Research: Gauging true consumer understanding of product features.

Here is our engine in action. A teacher can upload a single, standardized exam. After 50 students complete it, our system doesn't just return 50 grades. It analyzes each student's performance and automatically generates 50 new, unique practice exams for the next learning cycle. Each exam is personalized, focusing on the specific concepts that individual student struggled with. This allows educators to move beyond one-size-fits-all teaching and provide targeted, automated intervention at scale.

This data generation creates a powerful flywheel effect. Our partners aren't just licensing software; they're building a long-term, strategic asset. Every quiz taken and every question answered enriches their own proprietary dataset. This high-quality, structured learning data is the ideal fuel for a new generation of AI models, turning a simple content library into an appreciating data asset that compounds in value over time.

This brings us to our primary competitive advantage. Our moat is not a single feature; it's the machine learning pipeline itself. By focusing on anonymized learning patterns, we can track a student's conceptual understanding throughout their entire educational journey—from primary school to professional certification—without ever needing to know who they are. In a world of increasing data privacy regulation, this is a defensible long-term strategy that is incredibly difficult for competitors who rely on PII to replicate.

Our business model is a classic, high-margin B2B SaaS play. We license our white-label platform to institutional partners—corporations, certification bodies, and universities—for an annual fee. They get a custom-branded, AI-powered application without the multi-million dollar cost of building an in-house ML team. We provide the scalable solution to a universal business need: making their proprietary content effective.

Our platform also unlocks a new investment model for our partners. By transforming their static curriculum into a dynamic data asset, we enable them to scale their educational impact exponentially. Institutions are no longer just teaching—they're building a proprietary 'data lake' of learning patterns, a strategic asset that fuels continuous improvement and future innovation.

Our go-to-market is de-risked and two-pronged. Our direct-to-consumer CMFAS exam prep tool serves as live validation of the core engine. The organic user engagement in this hyper-specific niche, amplified by our powerful multilingual and adaptive AI, proves there is a real, unmet need for better learning tools. This success is the foundation for our primary GTM: a zero-risk B2B research pilot that gives innovative institutions a full-featured tool and gives us a direct path to securing large-scale enterprise contracts.

Before we move to our final slide, I want to address some common questions. We've created a comprehensive FAQ page on our website that covers details on our technology, security, and business model. I encourage you to review it, and I'm happy to answer any further questions you may have.