- 🎓 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
🕵️ Spy AI
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 meetings in real time.
- 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, exact quotes, timestamps and context on demand across past conversations.
- 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 avoid being misled.
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 a call.
- 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 UX for time-critical alerts.
🍽️ Flavor Match (Live Demo)
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 for interactive testing.
- Designed and implemented a normalized relational schema (Family → Member → Food) applying primary/foreign keys, UNIQUE/NOT NULL constraints and cascade operations, with timestamped logs to reliably capture individual preferences for analytics.
- 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 from historical activity and health status.
Languages
Frameworks & Tools
AI & APIs
Crafted with 💻 logic, ☕ passion, and 🇮🇳 heart
🧠 "Code for the Nation. Code for the Future." 🇮🇳