Enterprise RAG ecosystem managing 15,000+ semantic chunks. Features hybrid parsing (LlamaParse/PyMuPDF) and 256-dim MRL embeddings for 512MB RAM environments

AURA (Multi-Agent Retrieval-Augmented Generation System) is a next-gen solution for financial data processing. It features: 🧠 FinGPT for financial analysis and TableVision for data parsing. 🔍 Adaptive RAG Framework for real-time, accurate insights. 🌐 Human-in-the-Loop Mechanism for AI-human collaboration.

Financial research RAG agent over SEC EDGAR filings — multi-stage ingestion pipeline (download → parse → chunk → embed), pgvector semantic search, and a ReAct agent (Claude) that answers questions with citations.

Agentic RAG for financial intelligence: hybrid retrieval (FAISS + BM25), ReAct reasoning loop, 10-K document parsing, and live market data integration.

A multi-agent financial analysis system based on LangGraph, integrating Text2SQL, code interpreter, PDF parsing, web search, and RAG retrieval capabilities.

A full-stack platform for AI-driven financial analysis. Agent-ready MCP endpoints for structured bank statement parsing and RAG-based querying.

An Agentic Multimodal RAG system for financial risk assessment. Utilizes Claude 3.5 Vision and 3-tier dynamic TOC routing to parse 300+ page unstructured financial reports into structured KPIs via a Streamlit dashboard.

A modular, agent-based financial analysis platform with document parsing, retrieval-augmented generation (RAG), and interactive UI. Easily analyze, summarize, and query financial documents using advanced AI agents. Includes a React frontend and extensible backend for custom workflows.