The engineer
behind the code.
I'm Akash G Patil, a 3rd-year B.Tech AI/ML student at JSSATE Bengaluru with a CGPA of 9.15. I build AI systems that go beyond prototypes — focused on reliability, scalability, and real-world deployment.
From detecting hallucinations in LLMs to building smart traffic systems for Bengaluru — I work on problems where accuracy and speed genuinely matter. Currently seeking AI/ML internships and research opportunities.
Things I've built.
Each project solves a real problem. No toy demos — built fast, deployed, and production-ready.
The journey.
From first model to production systems — the milestones that shaped the engineer.
10th Grade — 95.8%
Kendriya Vidyalaya · CBSE
Strong foundation in Mathematics and Science. Early interest in computers and problem-solving.
12th Grade — 92.5%
Alva's Education Foundation · PCMB
Physics, Chemistry, Mathematics with Biology. Solidified decision to pursue AI/ML engineering.
Started B.Tech — AI/ML
JSSATE Bengaluru
Joined Department of Computer Science & Engineering with specialization in Artificial Intelligence and Machine Learning.
First ML Projects
Academic Projects
Built Fraud Detection System and NLP Question Answering System. Began exploring PyTorch and HuggingFace transformers.
F1 Race Analytics + ML Pipeline
Personal Projects
Built Formula 1 race strategy prediction system and an end-to-end ML model deployment pipeline with Docker and FastAPI.
Hackathon Streak Begins
Multiple Competitions
Competed in 8+ hackathons. Developed personal winning formula: idea clarity over code volume, deploy early, invest in presentation.
HalluciSense — Research Project
Major Project · JSSATE
Designed confidence-aware hybrid framework for LLM hallucination detection. Three-pillar architecture with H-Score metric. Target: Elsevier / Frontiers in AI.
GreenWave AI — Build for Bengaluru 2.0
Hackathon · AI/ML Track
Built real-time emergency vehicle green corridor system with Dijkstra routing + WebSocket architecture. Deployed live to Vercel.
CGPA 9.15 · 3rd Year
JSSATE Bengaluru
Maintaining strong academic record while shipping real-world AI projects and competing in hackathons.
In progress.
HalluciSense: A Confidence-Aware Hybrid Framework for Detecting and Quantifying Hallucinations in Large Language Models
Akash G Patil, Chirag O, Darshan A, Keerthan B M · JSSATE Bengaluru
We present HalluciSense, a three-pillar hybrid architecture for hallucination detection in LLMs combining retrieval-based verification, model-intrinsic confidence scoring, and cross-consistency checking. Our proposed H-Score metric provides a unified quantitative measure of hallucination severity across generative model outputs, enabling more reliable deployment of LLMs in high-stakes environments.