SpandaVidya AI
AI-powered Ayurvedic Healthcare platform featuring chat-based Ayurvedic consultation and PyTorch computer vision cataract detection. Engineered with NestJS backend & React Native mobile app.
< 180ms
Inference Latency
94.2%
Cataract Accuracy
99.9%
Uptime
Project Overview
SpandaVidya AI bridges traditional Ayurvedic medicine with modern deep learning diagnostics. The platform provides real-time intelligent wellness consultations via custom NLP models and automated ocular analysis for early cataract detection via computer vision.
System Architecture & Design
Microservices architecture featuring a NestJS gateway, PyTorch inference engine hosted on AWS ECS, MongoDB cluster for patient EHR records, and WebSocket real-time communication protocol.
Key Engineering Features
- Real-time AI Ayurvedic Consultation Assistant
- PyTorch Ocular Scan Cataract Risk Classification
- Secure End-to-End Encrypted Patient Health Records
- Cross-Platform iOS & Android Mobile Client
Engineering Challenge
Optimizing multi-megabyte PyTorch vision models for edge device latency and ensuring HIPAA-aligned encryption for sensitive patient health records.
Technical Solution
Quantized PyTorch weights into ONNX format for rapid serverless container execution and implemented field-level AES encryption in MongoDB for medical data.
API Design
RESTful OpenAPI 3.0 specification with JWT bearer authorization and WebSocket fallback channels.
Cloud & Deployment
Containerized using Docker, hosted on AWS ECS with auto-scaling groups and CloudFront CDN.