Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.

nAIdem utilizes artificial intelligence to streamline document processing tasks, enabling businesses to automate workflows, manage files, and analyze data. With features such as optical character recognition, data extraction, and seamless integration with other software, nAIdem significantly reduces the manual effort required for managing documents, ultimately leading to enhanced productivity and accuracy for organizations.

ExcelFormulaGPT is a powerful tool that converts your textual descriptions into functional Excel formulas within seconds. This innovative solution is designed to simplify the process of creating Excel formulas, saving time and reducing errors. Whether you're a novice or an experienced Excel user, ExcelFormulaGPT streamlines your workflow, ensuring you get accurate formulas without the hassle of manual coding.