GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...

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

Data lakehouses offer a solid footing, but when agents access the data autonomously, enterprises need to consider security, ...

I am here at the Databricks event in San Francisco. I attended the first day's keynote. At an event that drew 30,000 people, the Databricks CEO stated at the very beginning: "AGI is already here. And ...

Data lakehouses have become central to enterprise data strategy due to the need for data consolidation, the emergence of open ...

AI applications do not run on models alone. They run on timing. A support copilot, fraud system, recommendation engine, or AI assistant can all break in the same way: the underlying data arrives too ...

Traditional RAG systems struggle bridging structured SQL databases and unstructured document collections (a challenge we call the modality gap), leading to incomplete reasoning and hallucinations.

Many enterprise RAG pipelines handle one type of search well and fail silently on the rest. Databricks on March 4 released a new agent called KARL, or Knowledge Agents via Reinforcement Learning, that ...

Most enterprise RAG pipelines are optimized for one search behavior. They fail silently on the others. A model trained to synthesize cross-document reports handles constraint-driven entity search ...

Building a RAG system can be challenging. In addition to deployment and infrastructure challenges (eg, scaling up your vector db), there are many tradeoffs and decisions to make for each component of ...

A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used ...