AI-native tiered mental healthcare.
Making providers better, therapies more potent, and care more accessible — AI embedded across every tier of the stepped-care system.
Two structural problems
The treatment gap persists because of fundamental constraints — not lack of effort.
Not enough providers
Fewer than 10 mental health professionals per 100,000 people globally — and fewer than 1 per 100,000 in most low-resource settings. The result is long wait times and care systems that reach only a fraction of those in need.
Treatments are ineffective
Meta-analyses spanning six decades show that psychotherapies for youth depression outperform no treatment by roughly 12 percentage points — a modest effect that hasn’t meaningfully improved since the 1960s.
Three capabilities
AI can overcome structural barriers and be a force for more effective, more accessible care.
Making providers better
AI-augmented supervision gives every lay provider access to structured, timely feedback — closing the gap between training and practice without requiring more licensed professionals.
Making therapies more potent
Network analysis and session-level data reveal which intervention components drive outcomes — enabling iterative improvement of treatment protocols based on evidence, not intuition.
Expanding reach
AI-guided self-care tools extend structured mental health support beyond the clinic — making evidence-based interventions available to anyone with a phone, with clear escalation pathways when human care is needed.
AI-native stepped care
A four-tier system with AI embedded across each level — not replacing humans, but making every tier more effective.
Tier 1: Licensed Professionals
Psychologists and psychiatrists manage complex and high-risk cases. AI provides decision support — surfacing relevant history, flagging risk patterns, and suggesting evidence-based protocols.
Tier 2: Semi-Professionals
Supervisors recruit, train, and oversee lay providers. AI augments their work — automating session review, generating fidelity scores, and focusing supervision time where it matters most.
Tier 3: Non-Professionals
Lay providers deliver group-based interventions in schools and communities. AI provides real-time feedback on session quality, identifies skill gaps, and personalizes coaching recommendations.
Tier 4: AI-Guided Self-Care
Structured, validated self-guided interventions for young people — embedded within the stepped-care system with clear escalation pathways to human providers when needed.
Licensed professionals
AI-informed decision support
Semi-professionals
AI-augmented supervision & training
Non-professionals
AI-driven feedback on group interventions
AI-guided self-care
Structured interventions with escalation pathways
Where we are today
We've built and piloted the first component — AI-augmented supervision for lay providers.
What we've built
An AI system that processes raw therapy audio in English, Swahili, and Sheng — transcribing sessions, extracting prosodic features, and generating structured fidelity feedback for supervisors. Weeks of manual review compressed into hours.
What we've shown
A non-inferiority trial demonstrating that AI-generated supervision feedback is comparable to expert human supervision across key fidelity dimensions — with dramatically lower cost and latency.
Built on evidence
5
RCTs
11,500+
youth
100,000+
in delivery
50+
publications