Dynamic Multi-Agent RAG Pathway provides a modular architecture where each agent handles specific tasks—such as document retrieval, vector search, context summarization, or generation—while a central orchestrator dynamically routes inputs and outputs between them. Developers can define custom agents, assemble pipelines via simple configuration files, and leverage built-in logging, monitoring, and plugin support. This framework accelerates development of complex RAG-based solutions, enabling adaptive task decomposition and parallel processing to improve throughput and accuracy.

Unifyr AI is a comprehensive platform designed for AI engineers and developers to automate optimization problems, enhance cost efficiency, and improve accuracy. Leveraging advanced technologies like natural language processing (NLP), machine learning (ML), and process automation, Unifyr AI simplifies complex tasks, providing greater visibility and control over AI projects.

AlgoDocs is an intelligent document processing platform designed to automate the extraction of critical data from various document types such as PDFs, images, and text files. Utilizing advanced AI and machine learning, AlgoDocs offers a no-code solution perfect for businesses looking to save time, reduce errors, and improve data accuracy. The platform supports seamless integration, making it simple to validate and export data into your preferred formats or systems.

Financial Agentic RAG combines document ingestion, embedding-based retrieval, and GPT-powered generation to deliver an interactive financial analysis assistant. The agent pipelines balance search and generative AI: PDFs, spreadsheets, and reports are vectorized, enabling contextual retrieval of relevant content. When a user submits a question, the system fetches top-matching segments and conditions the language model to produce concise, accurate financial insights. Deployable locally or in the cloud, it supports custom data connectors, prompt templating, and vector stores like Pinecone or FAISS.