DocSpy AI is an advanced tool designed for analyzing documents using artificial intelligence, transforming how users handle contracts, agreements, and other important documents. Its AI-powered engine ensures no crucial detail is overlooked, allowing users to make informed decisions swiftly and efficiently. From business owners and real estate professionals to freelancers, students, and individuals, DocSpy AI caters to various needs by quickly unveiling hidden clauses and important points in documents. The app guarantees efficiency, saving both time and money while reducing dependency on legal experts.

Screpy is an AI-powered SEO tool designed to help you optimize and monitor your website. With features like keyword rank tracking, article writing, competitor tracking, and uptime monitoring, Screpy allows you to stay ahead of the competition and ensure your website performs at its best. The platform also supports team collaboration and customizable SEO reports, making it easy to manage projects and deliver professional insights to your clients.

AI Job Analyzer is designed to help you navigate through job descriptions on LinkedIn. Using AI, it reads job postings and finds specific information that you are interested in, such as relocation packages or knowledge of specific technologies like React. This tool makes your job search easier by providing answers to your questions while you browse through listings, saving you time and effort.

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.