What’s your problem statement?

Science and Innovation :
In the current healthcare landscape, individuals struggle to access personalized wellness solutions due to fragmented medical records, lack of holistic health planning, and limited integration of traditional healing practices. There's a critical need for a unified platform that can securely manage medical data, provide personalized health recommendations, and make healthcare information more accessible while bridging the gap between modern technology and traditional medicine.

The problem AyuWave solves

For Individuals:

Struggle with scattered medical records → Secure IPFS storage for all health documents
Difficulty finding personalized health plans → AI-driven custom fitness & diet recommendations
Limited access to traditional medicine → Digital access to verified ayurvedic remedies
Complex healthcare policies → Simplified benefit discovery in local languages

For Healthcare Providers:

Paper-based patient records → Digital OCR-based data collection
Generic treatment plans → Data-driven personalized health recommendations
Limited patient engagement → 24/7 AI health companion
Disconnected traditional & modern medicine → Integrated wellness approach

For Rural Communities:

Healthcare accessibility gap → Digital consultations
Lack of awareness about benefits → Easy government scheme discovery
Limited access to authentic medicines → Verified ayurvedic marketplace
Language barriers → Local language support

Core Value Proposition:
Makes healthcare management simpler, more accessible, and personalized by combining modern AI technology with traditional wellness practices in a single, secure platform.

Challenges we ran into

Challenges We Ran Into:
While building this project, we encountered challenges with training the Llama AI model 3.2, specifically regarding computational resource demands and integration with other components in our pipeline. The high memory and processing requirements initially caused delays in model training, and integrating the model into our existing architecture presented compatibility issues.

How We Got Over It:
To address these issues, we optimized resource usage by fine-tuning model parameters and leveraging model distillation techniques, which allowed for a more manageable training process. For integration challenges, we employed virtual environments to manage dependencies and conducted targeted tests with each part of the pipeline, which helped us isolate and resolve compatibility bugs.