AI_RAG delivers a modular retrieval-augmented generation solution that combines document indexing, vector search, embedding generation, and LLM-driven response composition. Users prepare corpora of text documents, connect a vector store like FAISS or Pinecone, configure embedding and LLM endpoints, and run the indexing process. When a query arrives, AI_RAG retrieves the most relevant passages, feeds them alongside the prompt into the chosen language model, and returns a contextually grounded answer. Its extensible design allows custom connectors, multi-model support, and fine-grained control over retrieval and generation parameters, ideal for knowledge bases and advanced conversational agents.

Faraday Web Researcher Agent leverages AI and web scraping techniques to perform end-to-end online research workflows. This agent integrates with various search engines and content sources, automatically querying topics, crawling result pages, and extracting relevant content. It processes HTML and PDF documents, filters extraneous details, and applies natural language processing to generate concise summaries or structured reports. Users can customize search parameters, set depth of research, and define output formats, enabling tailored intelligence gathering for market analysis, academic studies, or competitive intelligence. By automating repetitive tasks, Faraday accelerates research cycles, reduces human error, and provides a unified interface for accessing and digesting large volumes of web-based information.