Rishabh Goel
Software Developer | AI Researcher
goelr668@gmail.com
Hi, I'm Rishabh, a 17-year-old passionate about building AI-powered tools and systems. I focus on developing practical applications that solve real-world problems, which currently involves a lot of AI. Right now, you can find me working on Interchat or playing around with new deep learning architectures I find interesting. Feel free to reach out to me on LinkedIn or email, or if you can, check out my thoughts...
Industry work
Founder at Interchat
Jan 2025 - Present
- Founded an enterprise messaging platform integrating AI agents into communication workflows
- Built product serving 20+ users across 5 organizations using Next.js and Vercel AI SDK
- Implemented AI communication interface and RAG components
Research Intern at Invista Health
May 2024 - Jan 2025
- Developed Flutter-based survey analysis tool for remote patient monitoring
- Implemented solution used by 600+ geriatric patients with multiple comorbidities
- Designed and optimized patient data collection workflows
Software Lead at Pacific Links Foundation
May 2024 - Dec 2024
- Developed AI-driven English learning platform for 2,000+ human trafficking survivors
- Built Next.js application using Llama 3.1 for intelligent language processing
- Led $500K Llama 3.1 Impact Grant application
Software Intern at Rezolve AI
Jun-Sep 2024
- Developed enterprise HR solutions integrating voice AI with Retrieval-Augmented Generation (RAG)
- Used Hume.ai for sentiment-aware speech processing and designed RAG system for context-aware responses
- Deployed scalable demo on Google Cloud Platform serving 150+ businesses
Some cool projects
Zero Tolerance Robotics
Founded and led multi-high school FRC team, developing robot control systems in C++ and mentoring 30+ student programmers.
Quantum RNG Validator
Developed transformer-based validator for Quantum Random Number Generators, presented at IEEE ISNCC 2024. SOTA efficiency for RNG validation.
ML Library From Scratch
Built GPU-accelerated machine learning library implementing dense layers, CNNs, optimizers, and more in Python.
Medical Text Classification
Developed RAG-based system achieving 96.4% accuracy across 8 conditions. Published in JEI.
Cryptographic Functions Library
Implemented core cryptographic functions from scratch including Trapdoor RSA, AES-128/256 in multiple modes, and completed Cryptopals cryptanalysis challenges.