FalcoVita
Healthcare data visualization platform with AI-powered insights. Built with Vue.js frontend and Flask async backend using Celery pipelines and Redis caching for real-time responsiveness.
Project Overview
Type
Healthcare SaaS Platform
Timeline
4 months (2024)
Key Feature
Async Task Pipelines
Status
Live in Production
Technology Stack
Frontend
- • Vue.js 3 with Composition API
- • Chart.js for 20+ visualizations
- • Axios for HTTP requests
- • TailwindCSS for styling
Backend & Async
- • Flask (Python) REST API
- • Celery task queue
- • RabbitMQ message broker
- • Redis for caching
AI & Data
- • OpenAI API for NLP
- • Pinecone vector database
- • Pandas for data processing
- • NumPy for numerical analysis
Database & Security
- • PostgreSQL (persistent data)
- • Bcrypt + Argon2 (password hashing)
- • HMAC-SHA256 for signatures
- • JWT tokens for auth
Technical Achievements
⚡ 70% Latency Reduction
Implemented Celery async queues to offload heavy computations. Request latency dropped from 8s to 2.4s by moving CSV parsing and data aggregation to background workers.
📊 20+ Interactive Visualizations
Built dynamic Chart.js visualizations (line, bar, pie, scatter) with real-time data updates via WebSockets. Supports filtering by date range, patient cohort, and metrics.
🤖 AI-Powered Insights
Integrated OpenAI + Pinecone for semantic search over healthcare documents. Users can ask natural language questions like "What treatments work for condition X?" and get relevant results.
🔐 Enterprise Security
HIPAA-compliant authentication with Argon2 hashing (resistant to GPU attacks). JWT tokens with 1-hour expiry. All sensitive data encrypted at rest and in transit (TLS 1.3).
Impact & Metrics
70%
Latency Reduction
20+
Active Visualizations
0.8s
Avg Response Time (Cached)
Key Learnings
- 1.
Task Queues Transform UX
Users expect instant responses. Celery queues let you process 10MB CSVs in the background while returning "Processing Started" immediately. Game-changer for healthcare workflows.
- 2.
Redis Caching is Non-Optional
Without Redis, repeated queries hit the database. With it, repeated visualizations load in <100ms. Difference between professional and amateur products.
- 3.
AI APIs Scale Fast with Monitoring
OpenAI API calls can get expensive quickly. Implemented caching + monitoring to prevent runaway costs. Saved ₹20K+ in the first month after optimization.