Researchers at the Korea Advanced Institute of Science and Technology, in collaboration with startup GraphAI Co. Ltd., have unveiled AkasicDB, a next-generation database management system engineered to eliminate AI hallucinations and optimize enterprise generative AI workflows. Spearheaded by Professor Min-Soo Kim and first author Geonho Lee, the team introduced the architecture alongside its companion Omni RAG retrieval framework on June 2, 2026, during the ACM SIGMOD 2026 conference proceedings. Standard Retrieval-Augmented Generation pipelines depend predominantly on vector databases to align user prompts with unstructured documents. While proficient at semantic matching, this methodology overlooks structured tabular data and inter-entity connections, frequently causing AI agents to fabricate accurate-sounding but incorrect information. Enterprise environments typically circumvent this limitation by deploying separate relational, graph, and vector databases, forcing application layers to merge disparate outputs. This fragmented approach introduces severe latency, operational complexity, and excessive token consumption, ultimately compromising model reliability. AkasicDB addresses these constraints by unifying vector similarity search, graph traversal, and relational filtering within a single database engine. The system utilizes a centralized query planner that synthesizes complex multi-modal requests into one streamlined execution plan. Developers can construct advanced retrieval workflows using standard SQL or graph query syntax, enabling the database to concurrently evaluate document semantics, entity relationships, and structured filters. By removing intermediate data handoffs and redundant processing steps, the architecture drastically reduces the token load transmitted to large language models. Independent benchmarks indicate transformative performance improvements. The consolidated query execution accelerates complex retrieval operations by more than twenty times, compressing processing windows from over twenty-one seconds to under one second relative to legacy multi-database configurations. More importantly, the Omni RAG methodology elevates generative AI accuracy by up to seventy-eight percent by anchoring model outputs to cross-referenced semantic, relational, and tabular evidence. This substantial mitigation of factual drift resolves a primary barrier to deploying trustworthy AI agents in mission-critical applications. The architecture establishes a new baseline for data infrastructure supporting high-reliability enterprise AI. Professor Kim noted that unified processing of heterogeneous data formats is indispensable for scaling autonomous agents across complex organizational ecosystems. AkasicDB is positioned for immediate deployment in defense, manufacturing, finance, legal, and scientific research sectors, where data integrity and regulatory compliance dictate operational standards. The complete research findings, including technical specifications and performance validation, were published in the Companion of the International Conference on the Management of Data.