AInome is building a scalable Mental Wellness Index that supports remote and Indigenous communities by redefining how digital mental health data is gathered, interpreted, and mobilized. Born out of doctoral research and community-based innovation, this project addresses longstanding limitations in how mental wellness is quantified and resourced—especially within First Nations in Canada’s North. At its core, the index is not simply a tech solution—it’s a shift in epistemology. It centers local values, OCAP® principles (Ownership, Control, Access, and Possession), and the Truth and Reconciliation Commission’s Calls to Action. The project blends community-led qualitative insights with digital systems science and machine learning to unearth non-obvious patterns in mental health needs and resilience indicators. The result: actionable, real-time data that can inform policy, funding allocations, and health service design without compromising cultural safety or sovereignty. This session will cover: The need: Why conventional data systems fail Indigenous and remote populations. The model: How the Mental Wellness Index is structured and the ethical frameworks it builds upon. The innovation: How the system integrates anonymized data, AI, and local indicators of wellness to present holistic, real-time community snapshots. The impact: Early pilots in the Yukon demonstrate promise for broader uptake across regions and nations. The potential: This model can be adapted for other marginalized or culturally distinct populations worldwide, offering governments and health systems a new standard for inclusive, responsive digital mental health tools. We will also explore how the project is positioned to attract cross-sector investment, including interest from tech and health equity stakeholders. AInome is not only a product—it is a catalyst for rethinking what counts as “data” in mental health, and who gets to decide how it’s used.