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ANTSA is in early-stage deployment, with staged clinical implementation through pilots, demonstrations, and practitioner trials, ensuring governance, safety, and readiness before broader rollout.
safer, consistent clinician-supervised support.
reduced admin, better client visibility, safe AI.
benefit from unified, safe, scalable digital care system.
better oversight, early intervention, ethical AI
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ANTSA addresses the lack of safe, clinician-governed mental health support between therapy sessions, where most deterioration, disengagement, and risk escalation occur. It also responds to the growing use of unregulated, general-purpose AI for mental health support without oversight, accountability, or escalation pathways, creating potential safety risks.
ANTSA is designed as a governance-first model for ethical AI in mental health, prioritising safety, accountability, and clinician oversight before scale. Rather than replacing clinicians, it strengthens care delivery and sets a globally relevant framework for integrating AI responsibly across diverse health systems and regulatory environments.
ANTSA is a clinician-built, governance-led digital mental health system that provides structured, clinician-supervised AI support between therapy sessions. It enables mood tracking, journalling, psychoeducation, and therapeutic tasks, with all activity visible to practitioners. By integrating between-session data into clinical workflows, ANTSA supports early risk identification, reduces administrative burden, and strengthens continuity of care while maintaining professional accountability.
AI conversations occur within a clinical framework, with practitioner oversight and review.
Built from the outset to prioritise accountability, transparency, and ethical use of AI in mental health.
Structured support where deterioration most commonly occurs, not just during appointments.
All client activity is visible to the treating practitioner and embedded within clinical workflows.
Real-time mood tracking and engagement data support earlier detection of deterioration or disengagement.
Between-session data feeds directly into the client record, supporting informed clinical decision-making.
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