
This project is an autonomous market research agent designed to generate comprehensive, up-to-date company dossiers directly from live web sources. Powered by Gemini 3.5 Flash via the official google-genai SDK, the agent orchestrates a dynamic ReAct (Reason + Act) loop to answer multi-layered queries. The data acquisition layer integrates Bright Data’s APIs to bypass common scraping hurdles like anti-bot tracking and CAPTCHAs. Specifically, it employs the SERP API to fetch clean Google search results and the Web Unlocker API to extract webpage text. To ensure high execution speed and data quality, the engine leverages a ThreadPoolExecutor to run search and scraping tasks concurrently, while utilizing BeautifulSoup to structurally parse HTML before feeding it to the model. Users interact with the agent through a streamlined Streamlit web interface. They can type in a target startup name, monitor the parallel tool execution logs, view the generated report containing key leadership, funding history, and business models, and download the output directly as a clean Markdown file. This architecture provides a functional template for real-time web-grounded AI workflows.
31 May 2026

This project is designed for enterprise-grade financial intelligence and analytics. The platform uses multiple AI agents to: Analyze financial datasets Detect suspicious anomalies Forecast financial trends Generate executive insights Provide AI-powered decision support Multi-Agent Architecture The system contains specialized AI agents: 1. Extraction Agent Processes CSV, Excel, and PDF financial documents Cleans and structures financial data 2. Financial Analysis Agent Calculates KPIs Generates financial insights Evaluates business health 3. Anomaly Detection Agent Detects suspicious financial activity Identifies abnormal transactions Performs risk classification 4. Forecasting Agent Predicts future financial trends Generates forecasting intelligence Estimates business risks 5. Executive Intelligence Agent Produces executive summaries Generates strategic recommendations Answers financial questions System Architecture: User Uploads Financial Data ↓ Document Extraction Agent ↓ Semantic Financial Classifier ↓ Financial Analysis Agent ↓ Anomaly Detection Agent ↓ Forecasting Agent ↓ Executive Intelligence Agent ↓ Dashboard + Reports + AI Chat Intelligent Features Semantic Financial Understanding The system intelligently distinguishes between: Financial Columns revenue expenses payments invoices Non-Financial Columns transaction_id age quantity zip_code This prevents invalid analysis and improves enterprise accuracy. Anomaly Detection The platform detects: Fraud indicators Suspicious transactions Expense spikes Abnormal vendor payments Revenue inconsistencies Forecast Intelligence The forecasting system provides: Revenue predictions Cashflow forecasting Financial trend analysis Risk forecasting Executive AI Chat Users can ask: Why did revenue decrease? What is the biggest financial risk? Predict next quarter performance Identify suspicious activities Dataset Intelligence
19 May 2026