Traditional RAG typically retrieves relevant text from a vector database and supplies it to an LLM as context. Automation ...
12d
Modern Data & Knowledge Platforms: The Foundation Every AI Strategy Actually Runs On: SD Times 100
Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...
Arango believes recognition highlights its native multimodel architecture, customer adoption, and contextual data foundation ...
SAN FRANCISCO--(BUSINESS WIRE)--Arango, the company pioneering the live Contextual Data Layer for enterprise AI, today announced it has been named a Strong Performer in The Forrester Wave™: Multimodel ...
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 ...
Artificial Intelligence (AI) agents based on Retrieval-Augmented Generation (RAG) technology are rapidly proliferating. RAG ...
Without a clear view of where sensitive data lives, who can access it and how it moves, blind spots can quickly turn into ...
A VB Pulse survey of 101 enterprises finds 57% traced a wrong AI agent answer to bad context, and only 25% have a governed context layer in production.
High-impact AI implementations are more likely to treat data architecture, governance, and operationalization as strategic requirements, according to TDWI's 2026 Blueprint report.
Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
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