Description
AI-Investigator is a Python-based framework designed to automate the analysis of website content, specifically targeting enterprise AI case studies. It leverages the Claude 3.5 Sonnet API for content analysis and Firecrawl for web scraping. The primary goal is to extract relevant information from case studies and generate structured reports, providing valuable insights into AI implementations.
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The system operates in two main modes: CSV mode and Website mode. In CSV mode, users provide a CSV file containing URLs of case studies to be analyzed. The system then processes these URLs, extracts content, and generates reports. Website mode allows the system to automatically discover and analyze case studies from company websites using Firecrawl's map endpoint to find links and scrape endpoint to extract content. This mode automates the process of identifying and analyzing relevant case studies.
Key capabilities include case study discovery and analysis, content processing, and report generation. The system identifies potential case study links using Claude 3.5 Sonnet, filters content, and performs a detailed analysis. The content processing pipeline involves content extraction via the Firecrawl API, case study identification, content analysis, and report generation. The system generates individual case study reports, cross-case analysis reports, and executive dashboards.
The target audience includes data scientists, AI researchers, business analysts, and anyone interested in understanding enterprise AI implementations. The value proposition lies in automating the analysis of case studies, providing structured insights, and saving time and effort compared to manual analysis. The system helps users understand AI strategies, technical implementations, business impacts, and key success factors.
The technical architecture involves Firecrawl integration for web scraping, Claude 3.5 Sonnet integration for content analysis, and a data processing workflow that includes input, Firecrawl mapping, link analysis, content extraction, Claude analysis, and report generation. The project structure is organized with separate modules for scraping, processing, and report generation, making it modular and easy to maintain.
- CategoryMarket Research Tools
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
AI Enterprise Case Study Analyzer's Core Features
Automated case study discovery
Content extraction using Firecrawl API
AI-powered content analysis with Claude 3.5 Sonnet
CSV and Website analysis modes
Individual case study reports
Cross-case analysis reports
Executive dashboard generation
Enterprise AI qualification check
Detailed, multi-section analysis
Business impact assessment
Technical implementation details
Key success factors identification
Report generation in markdown format
Getting Started with AI Enterprise Case Study Analyzer
Developer: Clone the repository from GitHub.
Developer: Create a virtual environment using `python -m venv .venv`.
Developer: Activate the virtual environment using `source .venv/bin/activate` (Linux/macOS) or `.venv\Scripts\activate` (Windows).
Developer: Install dependencies using `pip install -r requirements.txt`.
Developer: Set up environment variables for the Anthropic and Firecrawl APIs in a `.env` file.
Developer: Place your CSV file (if using CSV mode) in the `input/` directory.
Developer: Run the analyzer using `python -m src.main`.
AI Enterprise Case Study Analyzer's Use Cases
- Enterprise AI Analysis
- Competitive Analysis
- Market Research
- Report Generation
- Due Diligence
- Technology Evaluation
