GMO AI RAG is an enterprise Retrieval-Augmented Generation (RAG) platform released by GMO Prime Strategy on June 30, 2026. It connects internal documents accumulated in SharePoint, Google Drive, Windows File Server, and other repositories to a dedicated knowledge base, leveraging retrieved evidence for response generation by large language models (LLMs). While GPT constructs answers based on the context of input text, GMO AI RAG functions by linking scattered document archives within an organization into a unified reference library, first locating materials relevant to a query. It supports both on-premises and cloud deployments, allowing organizations to configure search environments tailored to their structure by separating departmental information collections, access permissions, and LLM settings. Support for the Model Context Protocol (MCP) server serves as a connection point that extends this knowledge base to serve as a context source for external AI agents and business tools. Typical RAG implementation requires selecting and combining components such as document collectors, text extraction and chunking, vector search, permission management, and model invocation layers. During this process, discrepancies may arise between the permissions of the original storage and the visibility scope of search results, or relevant documents may be missed due to notation variations in Japanese texts and specialized terminology. The differentiator of GMO AI RAG lies in bundling existing repository connections, departmental collections and access controls, deployment options, LLM settings, and MCP integration into a single enterprise foundation. Particularly for organizations that find it difficult to export documents to external services, the ability to deploy within closed networks or internal environments offers an alternative to generic SaaS-based document chatbots. However, since actual search methods, embedding models, rerankers, document parsers, supported file formats, and permission synchronization scopes cannot be confirmed solely through the provided Discovery information, verification of official technical documentation is required prior to adoption. From the perspective of biotech researchers, GMO AI RAG can be utilized as an internal question-answering system connecting research documents and regulatory materials. For example, Standard Operating Procedures (SOPs) from SharePoint and experimental reports from Google Drive can be separated into departmental collections, and an evaluation set can be created by reviewing the top five search results to measure answer accuracy and source relevance. Clinical development organizations can connect protocols, statistical analysis plans, and data management plans to quickly locate evidence documents for changes and pass them to human reviewers for verification. Bioinformatics teams can structure pipeline operation documents and incident logs from Windows File Server as a knowledge base, receiving search results via MCP-supported agents to utilize in analytical procedure guidance. These scenarios can reduce document exploration time while enabling review of answer evidence at the source document level, thereby enhancing research reproducibility and regulatory responsiveness; however, supported citation formats and audit log features require separate verification.

GMO AI RAG is an enterprise Retrieval-Augmented Generation (RAG) platform released by GMO Prime Strategy on June 30, 2026. It connects internal documents accumulated in SharePoint, Google Drive, Windows file servers, and other repositories into a dedicated knowledge base, leveraging retrieved evidence to generate responses from large language models. While GPT constructs answers based on the context of input text, GMO AI RAG links scattered document archives within an organization into a unified reference library, enabling questions and related