AARI is a clinical decision‑support platform that captures short voice check‑ins from patients, extracts acoustic features to infer emotions in over 30 languages, and uses AI to identify patterns and generate personalized wellness recommendations. The system provides clinicians with a secure dashboard that aggregates voice‑derived metrics, automated notes, and AI‑assisted treatment suggestions, all encrypted to meet HIPAA requirements.
Funding
Funding not disclosed
Founders
Product
Problem
Many individuals lack accessible tools to continuously monitor emotional states and share actionable mental health data with clinicians, leading to fragmented care and delayed interventions. Providers often rely on manual note‑taking and periodic assessments, which limits real‑time insight into patient progress and hampers efficient treatment planning.
Solution
AARI delivers an end‑to‑end clinical decision support platform that leverages interactive AI to capture, analyze, and act on mental health signals. Users record short voice check‑ins; the system extracts tone, rhythm, and prosody features to infer emotions across more than 30 languages. AI algorithms identify patterns, triggers, and correlations (e.g., stress spikes during meetings) and generate personalized recommendations, wellness tools, and progress summaries. For clinicians, a secure dashboard aggregates voice‑derived metrics, assessment results, and treatment goals, offering AI‑assisted insights for treatment planning, goal tracking, and automated documentation. All data are encrypted and processed in compliance with HIPAA, enabling seamless, privacy‑preserving sharing between patients, families, and care teams.
Target Audience
The primary users are individuals seeking continuous mental‑health self‑monitoring and personalized support, as well as licensed therapists, psychologists, and behavioral health clinics that need AI‑augmented assessment and treatment workflow tools.
Features
- Voice‑enabled emotion tracking with multilingual support (30+ languages) using acoustic feature extraction and sentiment modeling
- AI‑driven pattern analysis that correlates emotional states with contextual factors and suggests actionable insights
- Personalized care recommendations, including mindfulness exercises, CBT prompts, and habit tracking based on detected trends
- Provider dashboard with smart patient analysis, progress analytics, and AI‑generated treatment plan suggestions
- Automated note‑taking and transcription tools that convert session recordings and handwritten notes into structured summaries
- End‑to‑end encryption (AES‑256‑GCM) and tokenized PHI handling to meet HIPAA Security and Privacy Rule requirements
- Secure, role‑based sharing of progress reports and alerts with trusted family members or care circles
- Integrated AI chat interface for user reflection and practice conversations, distinct from professional therapy