# Frequently Asked Questions (FAQ)

## About Nikhil Sai Pagidimarri

### Who is Nikhil Sai Pagidimarri?
Nikhil Sai Pagidimarri is an AI/ML Engineer and Co-founder at Kraionyx AI, based in Hyderabad, India. He holds a B.Tech in Artificial Intelligence & Data Science from KLEF and specializes in high-assurance agentic AI systems, production RAG architectures, and deep learning models with 95%+ accuracy.

### What is Kraionyx AI and what products does it build?
Kraionyx AI is a healthcare artificial intelligence company co-founded in 2025 by Nikhil Sai Pagidimarri in Hyderabad, India. Recognized among top healthcare AI startups, Kraionyx AI builds:
- **KareOS:** Clinical decision support workspace featuring 20+ specialist medical AI agents and HIPAA-conscious imaging analysis.
- **Doclave:** Professional networking and knowledge-sharing platform for doctors.
- **Svaani:** Ambient voice intelligence turning doctor–patient conversations into real-time clinical notes.

### What are Nikhil's key technical skills?
- **ML & AI:** TensorFlow, PyTorch, Scikit-learn, Keras, OpenCV, Deep Learning, Computer Vision, NLP, Reinforcement Learning, RAG, LLM Applications, Attention Mechanisms, Multi-Agent Systems
- **MLOps & Deployment:** Docker, FastAPI, Git, CI/CD, Vector Databases, Model Monitoring, Mixed Precision Training, n8n
- **Languages & Databases:** Python, SQL, JavaScript, PostgreSQL, MongoDB, MySQL, Pinecone

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## Projects & Research

### What is the Intelligent RAG-Based Conversational AI Chatbot?
A production-grade RAG chatbot architected by Nikhil using Pinecone and OpenAI text-embedding-3-large, with a FastAPI backend and MySQL ETL pipeline. It achieves 95%+ response accuracy and sub-second query responses across 10,000+ documents.

### What is the Brain Tumor Segmentation project?
A medical deep learning model using ResUNet+ with Attention Gates built on TensorFlow/Keras for the BraTS20 dataset. It achieved 98.17% accuracy and a 0.7570 Dice coefficient with inference under 2 seconds per scan.

### What is the Hybrid MPC-PPO Rocket Landing research?
A 2025 research publication pioneering a hybrid MPC-PPO architecture with JAX-based differentiable physics for autonomous rocket landing with thrust vector control, achieving a 94.7% landing success rate.

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## Contact & Links

- **Email:** nikhilsaipagidimarri@zohomail.in / nikhilsai384@gmail.com
- **Phone:** +91 95532 27987
- **Website:** https://nikhilsaipagidimarri.dev/
- **GitHub:** https://github.com/Nikhiluuuuuuuuu
- **Twitter / X:** https://twitter.com/nikhiluuuuuuuuo
- **LinkedIn:** https://linkedin.com/in/nikhilsaipagidimarri