AI-Driven Financial and Market Analysis with Azure AI Agent Services

Agentic AI for Real-Time Cyber Fraud Defense. Core Idea SentinelMesh is an AI agent system that: Detects fraud patterns in real-time Identifies scam messages (romance scams, phishing, social engineering) Monitors suspicious financial activity Issues alerts Provides human-readable explanations Auto-generates fraud evidence packages (PDF or structure

Enterprise AI systems engineering project focused on scalable LLM architectures, agent workflows, and financial services integration.

Experiments with multi-agent systems using CrewAI, featuring coordinated agent teams for coding assistance, debates, financial analysis, stock selection, and software engineering collaboration — exploring autonomous teamwork, reasoning, and task specialization in AI-driven workflows.

Enterprise Financial AI Platform (EFAP) This repository contains a complete, production-style implementation of a cloud-native, multi-agent AI platform designed to simulate how AI systems are built and operated inside a financial institution. It is not a simple chatbot demo. It is a full-stack Enterprise AI Engineering blueprint.

Production-grade AI systems for Financial Intelligence — GraphRAG pipelines, Multi-Agent systems + MCP, Data Engineering, LLM Fine-tuning, Real-time streaming, Evaluation (RAGAS • LangSmith), Monitoring (Prometheus • Grafana), LLMOps, Cloud deployment — end to end, production ready.

⚙️ Engineering advanced AI systems for cryptocurrency and financial intelligence. Combining LangChain, multimodal agents, and semantic search to create scalable architectures for market automation, data analytics, and real-time decision support.

End-to-end Financial Document Intelligence system — RAG pipeline, LangGraph agent, traditional ML, deployed on AWS with CI/CD and monitoring. Built as a portfolio project demonstrating Applied AI / MLOps engineering skills.

This repo provides an AI agent-based system for generating automated equity research reports by combining financial data pipelines, intelligent prompt engineering, and large language models (LLMs). It enables scalable, data-driven report writing for public companies—automating the traditionally manual process of financial analysis and commentary.

Enterprise-grade AI system that analyzes ERP AP (Accounts Payable) data to detect invoice anomalies, assess vendor risks, and generate CFO-level financial reports. Built with Isolation Forest, feature engineering, and LLM-based multi-agent collaboration, complete with an interactive Streamlit dashboard.

AlphaOps AI is an end-to-end MLOps financial system combining data engineering, LLM-powered SQL agents, and LSTM/Prophet forecasting. It features automated ingestion, model serving with FastAPI, monitoring with Evidently, autonomous workflows via LangChain, and Docker-based orchestration for continuous retraining and scalable deployment.

Design and implementation of an AI-driven financial market analysis system using Clean Architecture and DDD, featuring a LangGraph-based multi-agent workflow, RAG with FAISS for semantic pattern search, and LangChain integration with OpenAI/Gemini for embeddings and advanced prompt engineering—achieving 85%+ accuracy in trading signal prediction.