I'm a CS undergraduate at Vemana Institute of Technology obsessed with building production-grade AI systems at scale. Currently building GrowthNest — an AI agency automating enterprise workflows with autonomous agents, RAG systems, and intelligent pipelines.
Top 10 Finalist at RV Robotiesta Hackathon | 36-Hour AI Hackathon Winner | Self-taught in LLMs, AutoGen & RAG systems
I don't just study AI. I ship it. Every project solves a real problem.
GrowthNest is an AI agency specializing in enterprise automation through autonomous AI agents and intelligent systems.
- 🤖 Autonomous Agent Development — Multi-agent systems that decompose, execute, and self-correct
- 📚 RAG-Powered Intelligence — Production-grade retrieval pipelines with zero hallucinations
- 🔗 Workflow Orchestration — Seamless integration of AI with existing business processes
- 📊 Intelligent Data Pipelines — ETL automation with LLM-powered data transformation
- Enterprise RAG System — Document intelligence for large organizations
- Multi-Agent Research Platform — Autonomous research at scale
- Process Automation Engine — Custom AI solutions for client workflows
Transform how enterprises work by deploying AI agents that think, act, and improve independently.
Python ████████████████████ 100% | SQL ██████████████░░░░░░ 75%
Bash/Shell ██████████████░░░░░░ 75% | Git ███████████████░░░░░ 80%
LangChain ████████████████████ 100% | RAG Pipelines ████████████████████ 100%
AutoGen ████████████████████ 100% | Prompt Engineering ███████████████░░░░░ 90%
OpenAI/Gemini API ████████████████████ 100% | Fine-Tuning ██████████░░░░░░░░░░ 60%
Multi-Agent Systems ████████████████████ 100% | Vector DBs (FAISS) ███████████████░░░░░ 85%
Scikit-learn ███████████████░░░░░ 80% | NumPy/Pandas ███████████████░░░░░ 85%
Data Viz ████████████░░░░░░░░ 70% | Statistical A. ██████████░░░░░░░░░░ 65%
Streamlit ████████████████░░░░ 85% | Google Colab ████████████████░░░░ 90%
VS Code ████████████████████ 100%| Jupyter ████████████████░░░░ 90%
Linux ██████████████░░░░░░ 75% | Replit ███████████░░░░░░░░░ 70%
Production-grade RAG pipeline enabling users to query PDFs in natural language with citation-backed answers.
Key Achievements:
- ✅ 40% reduction in irrelevant retrievals through optimized chunking
- ✅ Zero hallucinations — every answer grounded in source documents
- ✅ One-click deployment for non-technical users via Streamlit
- ✅ Scales to 1000+ page documents in seconds
Tech Stack: LangChain, FAISS, OpenAI API, Streamlit, Python
Autonomous 3-agent system that researches, critiques, and synthesizes information without human intervention.
Performance Metrics:
- 🎯 Reduced 5-source research from 45 mins → 3 mins
- 🎯 100% pass rate across 30+ adversarial test cases
- 🎯 Handles finance, tech, and science queries flawlessly
- 🎯 Self-correcting architecture — learns from failures in real-time
Agents:
- Researcher — Decomposes queries, finds authoritative sources
- Critic — Cross-examines sources, identifies biases
- Synthesizer — Produces structured, debate-validated reports
Multi-step goal decomposition engine that breaks down user objectives into executable sub-tasks and self-corrects.
Reliability & Testing:
- 💪 85%+ success rate across 20+ edge cases
- 💪 Handles ambiguous instructions gracefully
- 💪 Stress-tested with conflicting constraints
- 💪 Iterative system prompt refinement for robustness
Architecture: Goal decomposition → Tool-use chaining → Failure detection → Self-correction
NLP-powered chatbot with multi-turn context management that advanced to finals against 100+ competing teams.
Judges' Feedback: Advanced technical implementation, real-time demonstration, superior intent classification.
| Metric | Value |
|---|---|
| Hackathons Competed | 2 (Top 10 Finalist × 1) |
| Projects Shipped | 4 Production-Grade Systems |
| Research Speed Improvement | 15× faster than manual |
| Agent Success Rate | 85%+ edge cases |
| RAG Retrieval Accuracy | 40% fewer irrelevant results |
| Lines of Code Written | 5000+ (LLM/AI systems) |
Vemana Institute of Technology, Bengaluru | Sep 2024 – Sep 2028
Coursework: Data Structures & Algorithms, OOP, DBMS, AI, Machine Learning, Computer Networks
Self-Study:
- 📚 Building autonomous multi-agent systems (no formal instruction)
- 📚 Advanced RAG architectures and vector search optimization
- 📚 LLM fine-tuning and prompt engineering at scale
- 📚 Production deployment of AI systems
Sri Sairam College of Engineering, Bengaluru | Apr 2022 – Apr 2024
| Achievement | Organization | Date |
|---|---|---|
| Top 10 Finalist — AI/ML Track | RV Robotiesta Hackathon (100+ teams) | 2024 |
| 36-Hour Hackathon Winner | Replit & Polaris School of Technology | 2024 |
| Data Analytics & Visualization | Accenture North America (Forage) | 2024 |
English ████████████████████ Professional
Hindi ████████████████░░░░ Professional
Bengali ████████████████░░░░ Native
Kannada ████████░░░░░░░░░░░░ Elementary
🤖 Autonomous AI Agents
📚 Large Language Model Systems
🔍 Retrieval-Augmented Generation
🔗 Multi-Agent Architectures
📖 Natural Language Processing
⚙️ Intelligent Process Automation
🚀 ML Model Deployment at Scale
I'm always excited to discuss:
- 🚀 AI agents and autonomous systems
- 📚 RAG architecture optimization
- 🤖 Enterprise AI automation
- 💼 GrowthNest partnership & collaboration opportunities
Email: [email protected]
Phone: +91 6297379254
LinkedIn: suman-rana-642806330
GitHub: SUMAN-IG
"Don't study AI. Build it. Every project should solve a real problem, not just demonstrate a technique."
I believe in:
- ✅ Shipping > Studying — Real projects over theoretical knowledge
- ✅ Rigorous Testing — Edge cases, stress tests, adversarial prompts
- ✅ Production-Grade Quality — Not just working, but scalable and reliable
- ✅ Continuous Learning — Reading papers, engaging with AI communities, breaking things to build them better
Last Updated: March 2025
Made with ❤️ by Suman Rana | Automating the future with AI