I’m Ashmit Gautam, an AI/ML student (B.Tech CSE – AI & ML, 2027) from Lucknow, focused on building AI systems that are accurate, safe, and practical.
I’m Ashmit Gautam, an AI/ML student (B.Tech CSE – AI & ML, 2027) from Lucknow, focused on building AI systems that are accurate, safe, and practical. I enjoy taking ideas from concept to working projects—especially with agentic AI, AI agents, RAG, and LLM evaluation—and I’m strengthening my foundation in CS through DSA, OOP, OS, and networks.
I’ve also gained experience through internships in data analytics and AI/data applications, where I built and improved ML/NLP models and deployed them as APIs using FastAPI. I like collaborating with teams, solving real-world problems with data, and continuously learning topics like guardrails and LLM evals so my work delivers reliable results.
Scoutly AI – B2B Research & Data Scrapper An intelligent B2B research and data scraping assistant designed to automate lead generation and market research pipelines. It extracts, processes, and structures data from target web sources to streamline B2B outreach and data collection workflows.
A secure, offline-first Progressive Web Application (PWA) built with Kotlin, Jetpack Compose, and Room SQLite to store critical medical, financial, and legal documents. Deployed on Vercel, it features biometric authentication, granular role-based access for up to 5 user roles, and a 48-hour access request safety delay to ensure absolute privacy and high reliability during high-stress scenarios.
Collected, cleaned, and validated large multi-city business datasets to ensure correctness of client reports. Automated data checks using Python scripts to reduce manual effort and improve reliability of the reporting workflow.
Collected, cleaned, and validated large business datasets across many cities to ensure client reports were correct. Wrote Python scripts to automate data checks and reduce manual work.
Built and tested machine learning and NLP models and exposed them via APIs using FastAPI. Evaluated AI chat model responses, iterated on prompts to improve result quality, and supported deployment-ready workflows.
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