Initializing Arc Reactor
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From AI-powered applications to full-stack products. Each project taught me something new about building software that matters.
A five-sector daily news dashboard that runs on zero infrastructure: a scheduled GitHub Action pulls ~71 RSS feeds, dedupes and ranks the survivors, and Netlify redeploys a static site on every commit.
A professional-grade Pomodoro timer built in Flutter with an AI coach: natural-language session input, Gemini-powered productivity insights, and ambient soundscapes for staying in flow.
Compliance analysts auditing dense policy documents get grounded, evaluable answers instead of a black-box chatbot — a self-correcting CRAG + Self-RAG pipeline measurably improved retrieval precision and answer faithfulness against a Ragas golden dataset.
PMs who spend hours each week triaging feedback scattered across Slack, support exports, and documents get a weekly, citation-verified brief instead — a 5-agent LangGraph pipeline where every quote is copied verbatim from source and independently re-verified before it ships.
Knowledge workers who need trustworthy answers from long internal PDFs get every response with the source page number and a relevance score attached — a multi-agent RAG system built on LangGraph orchestration and an event-driven cloud ingestion pipeline.
A dynamic utility designed for Product Managers to manage probabilistic AI systems. Leverages background agents to automate the synthesis, prototyping, and testing of new product features.
An AI-powered job search pipeline that automates lead generation and evaluation. Solves the fragmentation of job boards by aggregating sources, scoring leads via LLMs, and refining accuracy through feedback loops.
A multi-agent system that simulates an Agile squad to autonomously refine feature requests into comprehensive, estimated tickets — streamlining the product planning phase.
Developers stuck turning "it looks broken in this recording" into a code fix get an assistant that watches the screen recording and reads the code at the same time — built end-to-end during Google DeepMind's one-week Vibe Code with Gemini 3 Pro sprint.
SMB recruiters who screen hundreds of resumes by hand — and lose good candidates to formatting, not fit — get autonomous parsing, scoring, and ranking with a reasoning trace behind every score. Shipped as the Phase-1 "Screener" agent in a roadmap that scales the same core into scheduling, sourcing, and bias-audit agents.
Engineering teams that need to stress-test AI agents before real production data exists get an autonomous multi-agent pipeline that fabricates structurally valid, adversarially diverse synthetic datasets — cutting dev-cycle time 10–20x and infrastructure cost up to 80% versus hand-built test data.
An adaptive, AI-powered study mentor for GATE aspirants that personalizes the learning journey through real-time adjustments and proactive nudges.
A production-grade, modular RAG application designed to handle complex information retrieval across diverse data formats using intelligent query routing.
A full-stack travel booking platform that streamlines trip planning with dynamic content management, secure payments, and AI-driven advisory.
An end-to-end machine learning application that predicts critical machine failures in real-time, enabling proactive intervention in industrial settings.
An AI-powered health assistant that democratizes access to complex medical information by analyzing reports and summarizing dense web articles.
A comprehensive career optimization tool that maximizes candidate success by using AI to align resume bullet points directly with target job descriptions.
An intelligent learning companion that accelerates professional development by creating personalized, actionable roadmaps based on user goals.
A secure, full-stack journaling application that helps users cultivate self-awareness by tracking mental health trends and analyzing emotional states.
An AI-powered productivity tool that transforms unstructured daily logs and handwritten notes into formal, structured Weekly Progress Reports for university compliance.
Compliance analysts auditing dense policy documents get grounded, evaluable answers instead of a black-box chatbot — a self-correcting CRAG + Self-RAG pipeline measurably improved retrieval precision and answer faithfulness against a Ragas golden dataset.
Compliance analysts need precise, defensible answers from dense policy documents — a standard RAG chatbot can retrieve weak context or generate an ungrounded conclusion, with no way to tell which one happened.
Built a self-correcting CRAG + Self-RAG workflow: a four-node LangGraph pipeline grades every retrieved document for relevance, rewrites the query when context is weak, falls back to web search once retries are exhausted, and grades its own generated answer for grounding before returning it. Added a Ragas evaluation harness with custom golden datasets and dynamic column mapping so retrieval and answer quality are measured, not assumed.
Against a golden evaluation set, the self-correction loop lifted Context Precision from 68.9% to 81.8% (+12.9%), Answer Relevancy from 68.9% to 81.4% (+12.5%), Context Recall from 79.5% to 83.6% (+4.1%), and Faithfulness from 70.3% to 75.9% (+5.6%) over an ungraded baseline.