Cutting-edge financial analysis tool using Retrieval-Augmented Generation (RAG) to process complex financial data. This project combines AI-powered document retrieval with contextual response generation for scalable, insightful financial analytics.

Clinical Trial Inspector is an advanced AI agent designed to revolutionize how researchers, clinicians, and analysts explore clinical trial data. By combining Semantic Search, Retrieval-Augmented Generation (RAG), and Visual Analytics, it transforms raw data from ClinicalTrials.gov into actionable insights.

Data-Driven, AI-Augmented MeshEraser: A Scalable, Intelligent Distributed Platform for Spatial Analytics Suite.

This project is building an AI-powered system for detecting, analyzing, and logging road accidents and hazards. Using YOLOv11 models. Detected images are analyzed through a Vision-Language Model (VLM), vectorized, and integrated into a Retrieval-Augmented Generation (RAG) agent for intelligent analytics.

Cognitive-Driven, AI-Augmented SaaS Platform Empowering Real-Time Analytics and Machine Learning-Based Performance Optimization Across Federated Cloud Architecture.

Autonomous AI usage analytics agent (Tempo) — tracks Augment/Copilot/Claude metrics and usage patterns

LangSphere is an interactive AI playground specially developed to demonstrate the capabilities of large language models (LLMs) within the realm of Augmented Analytics.

CureWise-AI is a modern, production-grade healthcare data platform that leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and advanced AI/ML techniques to deliver intelligent analytics, medical report parsing, and conversational AI for hospitals, doctors, and patients.

CollaBoard is a collaborative, real-time virtual teaching tool built with JavaFX and integrated with PeerJS. It enables teachers and students to interact through a shared whiteboard, live video, and chat - augmented by AI tools for transcription, summarization, translation, and session analytics.

The Synapti Marketplace is a curated collection of Claude Code plugins designed for AI-augmented development + advanced analytical and research tasks. Each plugin provides specialized agents, skills, and commands that extend Claude Code's capabilities in specific domains.

It’s not science fiction. We are living connected lives filled with internet-enabled devices that learn our preferences and provide the experiences we want to make our lives more convenient. And the technology that makes it possible to connect our lives is expanding. The term IoT refers to the several related technological fields involved such as- AI , Blockchain , Cloud,Computing,Advanced analytics, Big data, Augmented & Virtual reality, Cyber-security & many more. The Internet of Things refers to electronic devices that are able to connect to the Internet and share data with other Internet enabled devices. Also known as connected devices, this includes laptops, smartphones, and computers, however they are far from limited from just these three things. The term ‘thing’ in the Internet of Things could be anything from a person’s blood pressure monitor, a bluetooth connected door lock, to a garage door opener that has an IP address and can communicate over a network.

Customer Relationship Management: Will it sustain in the Future? “The next generation will always surpass the previous one. It's one of the never-ending cycles in life.” Masashi Kishimoto. Well, although that quote was made in the context of humans and our inherent capabilities, this thought can actually also be applied to anything that’s created by us. The more we learn, the better we apply. To build on that idea, let’s talk about one such small, albeit important, piece of creation – Customer Relationship Management, or simply CRM. The concept of CRM evolved from the 80s’ Rolodex devices, which were hailed for being the next-generation contact management tools at the time. After experiencing its highs and lows in the 90s and the beginning of the new millennia, CRM has grown and firmly rooted itself as an indispensable resource for businesses in the Information Age. But can it continue its run in the times to come? 1. Making sense of more and more data Most CRM software today have an efficient data collection tool embedded within themselves, which aims to recognize social patterns and make context-based decisions for the business. Markets are no longer influenced by businesses and corporate institutions; instead, it’s their entire customer base that has full control over each and every transaction. In the future, as big data becomes even more prominent, it’ll become extremely challenging for a business to analyze and make sense of the huge amount of customer data. CRM will have to build on analytical tools and make smarter decisions in order to serve a large customer base and keep them loyal. 2. Delivering a complete value package All businesses need to understand that their customers appreciate it when their queries and complaints are heard and resolved, and then some. The future of CRM has to be built on smarter data and trend analysis. Digging deeper and obtaining insight into their customers’ behavior will allow businesses to proactively create better product and service offerings, and address their needs more accurately. 3. A smarter CRM with a smarter AI As digital capabilities continue to grow, reliance on actual manpower becomes less prominent. This is essentially due to the emergence of Artificial Intelligence (AI), which aims to impart rationality to a machine and mirror the concept of ‘thought’ in them. In the same vein, as CRM becomes augmented with smarter AI, businesses are avoiding wasting precious time on the more monotonous and repetitive tasks. Along with this, the AI can make substantial studies on existing customer data and predict and deliver exact suggestions on sales and marketing activities. And it will constantly learn and re-calibrate itself to keep imparting more and more accuracy to these results, sans any human intervention. 4. Reaching business objectives efficiently At the end of the day, every business transaction is a value exchange between the company and its customer. And each business aims to maximize this value potential. Delivering an exceptional customer experience with CRM can greatly improve customer acquisition and loyalty rates, which can in turn exponentially increase the profit margins for the business. A more adept CRM can interpret market data and deliver better suggestions in a swift and productive manner. As a CRM software keeps iterating and improving itself with the use of Artificial Intelligence, businesses can start to rely less on making uneducated decisions and come up with more meaningful strategies to engage with their customers organically. This not only fulfills the customers’ value requirements, but also makes the business more successful – a win-win situation for both the parties.

ARAI is a modular, AI-augmented healthcare analytics platform that automates risk adjustment, HCC mapping, and gap detection.

BrandSentimentRAG is an AI-powered analytics tool that leverages Retrieval-Augmented Generation (RAG) for sentiment analysis and topic modeling. It helps brands monitor consumer opinions on product launches and identify areas for improvement.

A full-stack RAG (Retrieval-Augmented Generation) system that combines AI-powered text generation with efficient vector search to provide intelligent cricket analytics. Built using local RAG implementation with Phi-3-mini model and FAISS vector store for real-time IPL data analysis.

Kuber is an experimental AI agent inspired by enterprise financial analysis platforms like BlackRock’s Aladdin, reimagined with modern open-source tooling. Built in Python, it integrates retrieval-augmented generation (RAG) with real-time analytics, enabling natural-language queries on structured and unstructured data.

AI-Augmented Data Analyst portfolio — full-stack insight systems using GPT, R, Shiny, and predictive modeling.

AI-augmented analytics systems: anomaly detection, LLM SQL agent, and deepfake detection pipelines

AI Analytics combines powerful data analysis capabilities with an intuitive, user-friendly interface to help businesses make data-driven decisions without requiring a team of data scientists.

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Analytics Engineer | Python Fullstack Developer | AI-Augmented Builder

Comprehensive data analysis of the Global Superstore dataset using Python, Pandas, and Plotly, with assistance from generative AI for streamlined workflow, code debugging, and insights generation. Explores sales trends, profitability, and operational costs through interactive visualizations.

AI-Augmented Business Intelligence System for E-Commerce Analytics

AI-augmented analytics app with SQL guardrails and repairs

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AI-Augmented QA Engine with Computer Vision and Predictive Analytics

dbt + Python Analytics + AI-augmented Reporting of OLIST Ecommerce Data