App4Independence (A4i) is a co-designed digital health platform built to support recovery in people with schizophrenia-spectrum and related severe mental illnesses. A4i offers a mobile app for individuals and a clinical dashboard for peer support staff and clinicians, enabling real-time engagement, risk monitoring, and coordination of care. Developed through a collaboration between CAMH, and MEMOTEXT Corporation, A4i has evolved from research trials into a peer-led, real-world intervention operating across 9 clinical sites in California. This presentation will walk through the platform’s design, evidence, and clinical integration process, illustrating how people, process, and technology come together to support an underserved population. Key features of A4i include personalized check-ins, wellness goal tracking, medication reminders, and am AI-augmented moderated peer-driven content social feed. The platform also includes a patented ambient sound detector to help users differentiate environmental sounds from hallucinations, and cognitive remediation tool to build compensatory strategies. These features are supported by a clinician dashboard that flags risk signals (e.g., missed doses, mood deterioration, increased detector use), promoting earlier and more informed interventions. We will share insights from a CIHR funded 6 month RCT. Earlier feasibility studies demonstrated low churn, with over 90% retention after 20 days and strong self-reported improvements in mood, engagement, and self-management. Most recent results from real-world implementations in California show 75% of users feel their mental health improved with A4i, and nearly 95% would recommend it. A unique feature of A4i is its data science layer, where usage data, sleep patterns, and natural language input (e.g., posts, notes to clinicians) are analyzed using AI to predict disengagement or risk of relapse. This component is currently being validated and offers promise in integrating AI into routine mental healthcare in a clinically responsible, peer-augmented way. The presentation will also address practical challenges: EMR integration, peer staff training, and clinical buy-in. Feedback loops between frontline users and developers allowed A4i to evolve in real-time based on user experience. These lessons are especially relevant as more organizations look to scale AI-enabled tools for complex mental health needs. Overall, this talk will show how A4i offers an equitable, scalable, and evidence-based approach to mental health support for individuals often left behind by mainstream care. By weaving together clinical care, lived experience, and AI, A4i is reframing what effective support can look like in schizophrenia care. This session will be highly relevant to audiences focused on AI in mental health, digital implementation science, peer support roles, and strategies to engage high-need populations using technology.