Customer Support Copilot
Enterprise assistant that answers support queries with RAG, LangGraph agent workflows and persistent conversation memory.
Generative AI first, with computer vision, ML and analytics work alongside.
Enterprise assistant that answers support queries with RAG, LangGraph agent workflows and persistent conversation memory.
Multi-agent research system: a LangGraph pipeline plans questions, searches the web, analyses findings against your own PDFs, and writes a report.
Turns PDF/DOCX/TXT notes into grounded, difficulty-controlled MCQ quizzes with a full RAG pipeline (parse, chunk, embed, retrieve, generate).
Resume analysis, AI code review, voice mock interviews and adaptive feedback in one interview-prep platform.
Real-time captioning with speaker diarization, plus RAG-based lecture summaries, key takeaways, definitions and action items.
Scores news claims by analysing text and images together with a PyTorch fusion model behind a FastAPI service.
Search and question answering over organisational policies with source-cited answers.
Chatbot for global statistics built on Worldometer data, Flask, LangChain and ChromaDB.
Real-time webcam/CCTV platform combining OpenCV preprocessing, YOLOv8 detection and multi-object tracking.
Lane detection, traffic sign and signal recognition, object tracking and risk assessment in one pipeline.
Real-time computer vision and compliance engine for industrial safety monitoring.
Equipment failure forecasting, anomaly detection, remaining-useful-life estimation and digital twin simulation.
Early-warning system for outbreaks using machine learning and deep learning.
Predicts diabetes, heart disease and lung risk with a React + Flask app and analytics dashboard.
Analyses profiles and resumes to recommend career paths, placement readiness and skill gaps.
Award-winning AYUSH platform: YOLOv8 plant recognition, therapy recommendations, multilingual voice interaction. Published research.
Offline-first multimodal assistant that helps farmers diagnose crop health.
SQL, Python, Power BI and Tableau analysis to find revenue gaps.
Retail sales forecasting with multiple ML and time-series models.
Clustering model that groups customers to target financial products.
Sales and returns analysis with Pandas, Excel and a business insights report.
More on GitHub.