Projects using TensorFlow in ChennaiProjects using TensorFlow in Chennai
Cover image for VitaCare🚀 

1. Immutable Health Records
VitaCare🚀 1. Immutable Health Records (Blockchain & AES-256 Encryption) I moved beyond standard database storage to build a Tamper-Proof Medical Ledger. I learned how to implement a hybrid storage strategy where sensitive patient data is encrypted via AES-256 at the application layer before being anchored to a blockchain. This taught me how to ensure absolute data integrity, making medical histories immutable while providing a verifiable audit trail for every access request. 2. Privacy-First Consent Logic (Granular Data Sharing) Architecting the "Time-Limited Access" protocol taught me how to handle high-stakes privacy. I engineered a system where patients can issue temporary, scoped decryption keys to doctors via smart contracts. This taught me how to implement a Zero-Trust architecture, ensuring that healthcare providers only see what they need, exactly when they need it, with access automatically revoking after a set TTL (Time-To-Live). 3. Edge-Optimized Backend & Secure Validation By leveraging Supabase Edge Functions, I learned how to move critical business logic closer to the user while maintaining a "Thick-Client, Secure-Server" model. I architected isolated server-side environments for data validation and healthcare-specific compliance checks, which taught me how to drastically reduce latency in high-volume environments without compromising on server-side security. 4. Proactive Health Intelligence (Predictive Monitoring) I leveled up my AI integration skills by building an Advanced Command Center for Disease Surveillance. I learned how to aggregate anonymized, real-time data from disparate sources—including IoT wearable integrations—to generate heatmaps for disease outbreaks. This taught me the complexity of Geospatial Data Engineering and how to turn passive monitoring into proactive healthcare interventions. 5. Multi-Platform Synchronization (Unified Digital Ecosystem) Building a system that bridges Citizens, Doctors, and Government officials taught me the challenges of Cross-Stakeholder State Management. I learned how to maintain a "Single Source of Truth" across a multilingual Next.js web ecosystem and mobile interfaces, ensuring that a life-saving update on a doctor's portal is reflected on a patient's mobile dashboard in near real-time. 6. Inclusive Design & Localized Accessibility To tackle the diversity of the Indian healthcare landscape, I implemented a Multilingual UI Framework. I learned how to architect a scalable localization layer that supports regional languages, ensuring that the platform is accessible to rural citizens. This taught me the importance of Inclusive UX Engineering—where the technical complexity is hidden behind a simple, high-impact interface for non-technical users.
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Cover image for RAG is only as good
RAG is only as good as the data you feed it. 📄➡️🤖 I am excited to share that I’ve completed the Build an AI-Powered Document Retrieval System with IBM Granite and Docling lab from IBM SkillsBuild! While my previous work focused on the RAG pipeline, this lab went deeper into the most critical step: Document Parsing. We often forget that real-world data isn't clean text—it's locked in complex PDFs and formatted documents. What I built in this hands-on lab: 🔹 Advanced Parsing with Docling: I used Docling to not just "read" text, but to understand the structure of documents, preserving the context for the AI. 🔹 Granite Power: Leveraged IBM Granite models (granite-embedding-30m-english) to create high-quality vector embeddings. 🔹 Seamless Integration: Orchestrated the entire workflow using LangChain to connect the parsed data with the retrieval engine. This skill allows me to build AI agents that don't just "guess" answers but can accurately retrieve information from complex business documents. Technical breakdown of what I built: 🔹 Orchestration: Used LangChain to manage the flow between the user, the database, and the model. 🔹 Embeddings: Leveraged IBM Granite models (granite-embedding-30m-english) to convert text into vector representations. 🔹 Data Processing: Implemented document loading and chunking strategies to optimize context windows. 🔹 Synthesis: Created a system that retrieves relevant data and generates accurate, fact-based summaries. This experience has given me the practical skills to build AI applications that are not just "smart," but also accurate and domain-specific.
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