Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document repositories. The culprit isn't model quality but architecture: Traditional retrieval augmented generation (RAG) systems were designed to retrieve and summarize, not analyze and aggregate across large document sets.Snowflake is tackling this limitation head-on with a comprehensive platform strategy announced at its BUILD 2025 conference. The company unveiled Snowflake Intelligence, an enterprise intelligence agent platform designed to unify structured and unstructured data analysis, along with infrastructure improvements spanning data integration with Openflow, database consolidation with S [...]
Our LLM API bill was growing 30% month-over-month. Traffic was increasing, but not that fast. When I analyzed our query logs, I found the real problem: Users ask the same questions in different ways.& [...]
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 a [...]
The modern customer has just one need that matters: Getting the thing they want when they want it. The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses fre [...]
Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]
Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation co [...]
By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an [...]
It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI.RAG emerged in the last couple of years to bec [...]