Arango believes recognition highlights its native multimodel architecture, customer adoption, and contextual data foundation ...

AkasicDB integrates vector, graph, and relational stores within a single DBMS, and processes queries across the three data models as a single execution plan through an unified query planner and ...

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Sales, a function that obviously runs on language, has been among the least changed by the technology built on language.

The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based ...

Artificial Intelligence (AI) agents based on Retrieval-Augmented Generation (RAG) technology are rapidly proliferating. RAG ...

While AI holds the promise of radically transforming KM, human oversight takes on intensified responsibilities for ensuring the knowledge provided is accurate, timely, and relevant as well as guarding ...

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 ...

Erik Steiger discusses the operational pain of legacy PDF generation in regulated banking and manufacturing. He explains how ...

A good software architecture ensures that an AI system does not depend on the performance of a specific model.