• 🎓 Computer Science student at JIIT Noida (2024–28)
• 💻 I love building intelligent systems that interact, automate, and improve lives
• 🔬 Exploring AI agents, automation, FastAPI, Flutter, and AI APIs
• 🇮🇳 Mission-driven to use tech for India's advancement in defense and wellness
• 🧪 Constantly experimenting with AI, ML, automation, and hardware integration

Flutter, FastAPI, Dart, Python, Gemini, PostgreSQL

Spy AI, an AI-powered full-stack mobile app using Flutter (Dart) frontend and FastAPI (Python) backend that turns your phone into a searchable, lifelong memory by recording and indexing conversations in a PostgreSQL database.

• Implemented a background recorder with real-time audio capture, transcription pipeline, structured database storage and full-text search; integrated an LLM-driven chatbot to surface summaries, Q&A, and keyword-based query results across all past recordings.
• Delivered end-to-end production features including secure storage, privacy controls, meeting-level metadata, and cross-platform deployment—empowering users to recall details, verify claims, and revisit past conversations on demand.

Flask, Html, Twilio, Gemini

AI Calling, a full-stack voice-first application with an HTML frontend and Flask backend that enables natural, real-time phone conversations with an AI assistant.

• Integrated telephony providers and built real-time speech-to-text and text-to-speech pipelines, plus automated call initiation to allow users to talk directly to an LLM-driven agent over standard phone lines.
• Implemented an emergency auto-call feature to send urgent voice messages when users cannot place calls manually, and delivered secure call handling, scalable backend services, and a hands-free conversational UX for on-the-go access.

FastAPI, Gemini, Html, CSS, JavaScript

Flavor Match, a full-stack family food recommendation web app using HTML/CSS and JavaScript on the frontend with a FastAPI + PostgreSQL backend; deployed a live demo on Vercel that intelligently suggests meals everyone will enjoy.

• Designed and implemented a normalized relational schema (Family → Member → Food) applying primary/foreign keys, UNIQUE/NOT NULL constraints and cascade operations, with timestamped logs and structured preferences to ensure data integrity and efficient query performance.
• Implemented streamlined family/member registration and daily food-logging flows, plus an SQL-driven recommendation pipeline (rule-based / lightweight AI) to deliver personalized meal suggestions aligned with household tastes and dietary restrictions.

Languages

Frameworks & Tools

AI & APIs

🔥 Contribution Streak

📅 Contribution Activity