Convo Care — Privacy-First AI Risk Triage Platform
Designed and built a privacy-first AI system that analyzes text and voice conversations to produce sentiment and mental-health risk scores without storing transcripts or raw audio.
Highlights
- Built a full-stack platform (FastAPI + React) for text and voice-based risk scoring
- Implemented in-memory speech-to-text and NLP scoring with zero transcript persistence
- Designed a scores-only data model for privacy-preserving analytics and trend tracking
- Delivered per-student risk trends, history, and high-risk flagging
Impact
- Demonstrated how AI systems can be designed with privacy as a first-class constraint
- Produced a reusable pattern for compliant AI decision-support pipelines
- Showcased end-to-end system design: ingestion, scoring, storage, and visualization
Context
In sensitive domains like mental health, data privacy is a core system constraint. Storing transcripts or audio creates unnecessary risk, even when the goal is only trend analysis and triage.
This project explores a scores-only AI pipeline:
Analyze → score → discard raw data → retain only safe metrics.
What This Is
A web-based AI decision-support platform that:
- Accepts text or voice input
- Transcribes audio in memory
- Runs sentiment and risk scoring models
- Stores only numerical scores and safe metadata
- Presents trends, history, and high-risk flags in a React dashboard
The backend is built with FastAPI and MySQL, and the frontend uses React with WebRTC for voice capture.
Outcomes
- A working demonstration of privacy-first AI system design
- A full-stack, containerized platform with real-time scoring flows
- A reusable architectural pattern for compliant AI analytics systems
Why This Matters
This project reflects how I design AI in production environments:
Treat data handling, privacy, and compliance as system-level architecture decisions, not afterthoughts.