Since we are close to launch, let’s start boasting about our creation :)

ASAP (Advanced Semantic AI Platform) automates the process of code and tech documentation generation. What once took hours can now be accomplished in minutes. The ASAP suite of AI copilots consolidates all essential functionalities into a single workspace. There’s no need to switch between tabs or applications to use different, disconnected AI copilots. At the same time, ASAP incorporates human-in-the-loop control for quality assurance over final outputs. ----- ASAP offers a suite of AI copilots transforming tech documentation and code creation using custom ontologies tailored to an organization's standards. Integrating advanced natural language search and automation within a unified workspace, ASAP combines the speed and efficiency of AI with the reliability of traditional systems. ASAP's Suite Includes: OntologyGen: Creates custom ontologies aligned with organizational standards, terminologies, and data structures, ensuring adherence during the document and code generation processes. TechDocGen: Uses OntologyGen's customized ontologies to generate professional, consistent documentation. CodeGen: Deploys software ontologies to generate accurate code across multiple platforms, ensuring compatibility and optimal performance. ReportGen: Employs predefined ontologies to create comprehensive, standardized reports in various formats (PDF, diagrams, images). Natural Language Queries: Enables users to perform precise data searches using natural language, with data stored in a vectorized format in Cosmos DB. Unified Workspace: Designed for accessibility and ease of use, each AI copilot has its workspace with chat sessions stored for later retrieval using semantic search. Retrieval Augmented Generation (RAG): Enhances performance in knowledge-intensive tasks by retrieving and incorporating external information into prompts. Each copilot is a domain expert: TechDocGen for documentation, CodeGen for coding languages and platforms. ASAP's customizable settings, such as target audience and response attributes, enable replication of the original chat context. Lifelong Learning Agents, supported by Cosmos DB and vectorization, ensure continuous improvement in performance.