Category: RAG Architecture

Enterprise organizations generate knowledge across dozens of disconnected systems—Confluence wikis, Slack conversations, email threads, project documentation, customer support tickets, and legacy databases. When your AI assistant can only access one or two of these sources, you’re essentially building a digital librarian who’s only allowed to read picture books while ignoring the encyclopedias. This fragmentation creates…

The average enterprise loses 2.5 hours per day searching for information across scattered documents, databases, and knowledge repositories. Despite investing millions in enterprise search solutions, knowledge workers still struggle to find relevant, contextual answers when they need them most. Traditional search returns documents; what teams really need are synthesized insights that connect information across multiple…

The race to deploy real-time AI applications has exposed a critical bottleneck in traditional RAG architectures: latency. While organizations rush to implement retrieval-augmented generation systems, many discover their carefully crafted solutions crumble under production workloads, delivering responses that arrive seconds too late for real-time applications. Consider this scenario: A financial trading platform needs to provide…