One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination" — the generation of plausible-sounding but factually incorrect information. KAIST researchers have developed a next-generation database technology capable of understanding documents, data, and relationships among entities all at once. The technology improves AI response accuracy by up to 78% and processing speed by up to 20 times, addressing a key challenge in the commercialization of enterprise AI.
KAIST, led by President Kwang Hyung Lee, announced on the 19th that a research team led by Professor Min-Soo Kim of the School of Computing, in collaboration with faculty startup GraphAI Co., Ltd., has developed "AkasicDB," a next-generation database technology that integrates the functions of vector databases, graph databases, and relational databases into a single database management system (DBMS). Based on this technology, the team also developed a new Retrieval-Augmented Generation (RAG) method called "Omni RAG."
AkasicDB is designed to integrate and execute the functions of vector databases, which convert the meaning of documents or images into numerical vectors to search for similar information; graph databases, which store and analyze relationships among entities such as people, companies, and products; and relational databases, which systematically manage data in table form. Omni RAG, developed on this foundation, improves the accuracy of generative AI responses by simultaneously utilizing semantic information from documents, relationships among entities, and structured data.
AI agents have recently been spreading rapidly based on RAG technology, which searches vast collections of corporate documents and expert knowledge and generates responses based on the retrieved information. However, real-world enterprise data is distributed across various forms, including documents, tables, and relationships among entities, making it difficult for AI to comprehensively understand and use the data. As a result, AI may generate factually incorrect responses without sufficient grounding, creating hallucination issues that have been regarded as a major obstacle to the broader adoption of enterprise AI.
Conventional RAG typically works by converting user queries and documents into vectors, retrieving semantically similar documents, and providing them to a large language model (LLM), an AI model trained on massive datasets to generate human-like language. While this approach is effective for searching unstructured documents, it has limitations when handling complex queries that must also consider relationships among entities in documents or structured conditions such as specific periods, types, or ranges.
For example, a query such as, "Find clauses related to Company A among contracts signed last year, and explain how those clauses are connected to product supply issues," requires vector search to identify document meaning, graph search to explore relationships among entities, and relational queries to filter by date and type. In existing systems, this required building multiple types of databases separately and combining the results at the application layer, leading to management complexity and response delays.
To solve this problem, the research team proposed Omni RAG, which integrates vector similarity search, graph traversal, and relational filtering within a single query and execution plan. Omni RAG identifies more accurate evidence by simultaneously utilizing semantic information from documents, relationship information from knowledge graphs, and structural conditions from tabular data, significantly reducing AI hallucinations.
AkasicDB, developed to support this method, adopts a new architecture that integrates graph databases, vector databases, and relational databases into a single engine. Users can express complex RAG queries that combine vector search, graph traversal, and relational filtering as a single SQL/GQL* query, and AkasicDB optimizes and processes the query as one unified execution plan.
SQL/GQL, or Structured Query Language/Graph Query Language, refers to command languages used to search or modify information stored in databases. SQL is the traditional language used to handle tabular data, while GQL is a language dedicated to graph data and is used to analyze connections among entities such as people, companies, and products.
Through this integrated architecture, AkasicDB minimizes unnecessary intermediate result generation and data movement, greatly reducing the number of tokens used by LLMs and shortening response latency. In experiments, complex search queries that took up to 21.3 seconds in existing systems were processed in under one second, achieving a performance improvement of more than 20 times. Omni RAG also improved response accuracy by up to 78% compared with conventional RAG. These results demonstrate its potential to substantially mitigate hallucination, one of the core challenges for enterprise AI agents.
Professor Min-Soo Kim said, "For AI agents to accurately understand and utilize the vast amounts of data held by enterprises, data infrastructure capable of processing vector, graph, and relational data in an integrated manner within a single system is essential. AkasicDB is a next-generation database technology for the era of AI agents, and we expect it to be used as core data infrastructure in fields requiring high reliability, including defense, manufacturing, finance, law, science, and technology."
KAIST School of Computing Ph.D. student Geonho Lee participated in this research as the first author. The research results were presented as a demo paper on June 2 at ACM SIGMOD 2026, one of the world's most prestigious international conferences in the field of databases, where they drew strong interest from global companies and researchers.
※ Paper title: AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
DOI: https://doi.org/10.1145/3788853.3801609
※ Author information: Geonho Lee, KAIST, first author; Jeongho Park and Donghyoung Han, GraphAI Co., Ltd., co-authors; Professor Min-Soo Kim, KAIST, corresponding author
※ Demonstration video: https://www.youtube.com/watch?v=KD6MznZ61P4
