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Key Features
- LLM-based symptom analysis for natural doctor-patient conversations
- Real-time speech understanding with VAPI Voice Assistant + OpenRouter AI
- Automatic medical report generation (symptoms, prescriptions, advice, summary)
- Clerk authentication for secure access
- NEON PostgreSQL + Drizzle ORM for robust data operations
- SaaS-ready architecture: dashboards, doctor listings, pricing, health tips
- Deployed on Vercel for global accessibility
Challenges Faced
- Achieving natural, low-latency voice conversations that felt human rather than robotic
- Structuring unstructured doctor-patient dialogue into consistent, clinically useful reports
- Coordinating multiple services (voice, LLM, auth, database) into one cohesive real-time flow
Solutions Implemented
- Integrated VAPI Voice Assistant with OpenRouter AI for sub-second response times and natural turn-taking
- Designed prompt templates that reliably extract symptoms, prescriptions, and summaries from freeform conversation
- Used Clerk for auth and Drizzle ORM over NEON PostgreSQL to keep the data layer simple and type-safe end-to-end
Technologies Used
Next.jsTypeScriptAI/LLMPostgreSQLVoice AIVAPIClerk AuthDrizzle ORMSaaS
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Curious how it was built?
I'd be glad to walk through the architecture and decisions behind AI Doctor Assistant.