Featured Work & Research
Architecting scalable AI solutions across FinTech, enterprise NLP, and edge computing.
Credit Risk-LLM: Hybrid AI Credit Risk Assessment System
Active Research & Development
The Pitch:
A production-grade, dual-branch credit risk model that fuses structured financial data with unstructured applicant narratives. This architecture proves that generative AI can extract highly predictive risk signals from text without sacrificing the strict explainability required by global financial regulators.
Tech Stack:
Mistral-7B, LightGBM, QLoRA, SHAP, Scikit-learn, Python
Key Highlights:
Architecture: Dual-branch system combining a LightGBM classifier with a fine-tuned Mistral-7B-Instruct model (4-bit QLoRA) for minimal compute overhead.
Performance Metrics: Achieved an AUC-ROC of 0.8472 and PR-AUC of 0.6341. Intelligent Fusion: Implemented a Logistic Regression Meta-Learner that assigned 32.5% of the predictive weight directly to the unstructured text branch.
Enterprise Compliance: Fully aligned with the EU AI Act (Art. 13-15), ECOA, Basel III IRB, and GDPR Art. 22 standards through demographic fairness audits and a multi-modal explainability pipeline (SHAP + Counterfactuals).
Enterprise NLP Engine: Smart Email Classification System
Completed
The Pitch:
An intelligent text-processing pipeline designed to automate corporate communication sorting. Trained on a massive enterprise dataset, this system accurately routes high-volume communications into actionable categories like Business, HR, Legal, and Finance, drastically reducing manual administrative overhead.
Tech Stack:
Bidirectional LSTM, TensorFlow/Keras, Streamlit, Gmail API, Pandas
Key Highlights:
Deep Learning Pipeline: Built a Bidirectional LSTM neural network trained on the Enron Email Dataset containing over 500,000 records.
End-to-End NLP: Developed a comprehensive architecture encompassing automated text cleaning, tokenization, padding, and advanced word embeddings.
Interactive Deployment: Integrated the live Gmail API to dynamically fetch real-time data, visualizing label distributions and confidence scores via a custom Streamlit dashboard.
Intelligent Edge Automation System (SSIP Funded - Gesture Pilot)
SSIP-Funded Prototype (Market-Ready)
The Pitch:
A real-time computer vision and automation architecture deployed directly onto edge hardware. This project successfully bridged the gap between complex software algorithms and physical hardware constraints, securing government backing to transition from an academic prototype to a commercially viable product.
Tech Stack:
NVIDIA Jetson Nano / Orin NX, CUDA, OpenCV, RTSP, C++ / Python
Key Highlights:
Government Backing: Awarded ₹1,20,000 in competitive funding through the Student Startup and Innovation Policy (SSIP) and the Government of Gujarat.
Hardware Integration: Led the end-to-end architecture and deployment on NVIDIA Jetson hardware.
System Optimization: Utilized CUDA for real-time edge processing and integrated RTSP streaming, increasing overall cost efficiency by 25% and preparing the underlying architecture for a pending IPR patent.