2025 Enterprise AI Sourcebook

Features

Architecting a Modern Data Stack for AI Agents

Even if you've got a brand-new business model based specifically on the capabilities of a language-centered AI agent, you still have to deal with time-honored challenges such as disparate and scattered data, slow response, finding necessary information, and trusting that information.

Fast, Accurate, Relevant, Intuitive: The Future of Search

What attributes come to mind when you consider a great search experience? You would probably say it is fast, accurate, relevant, and intuitive. You would focus on what the act of searching allowed you to achieve.

Making Data More Accessible and Usable: Knowledge Graphs, Semantic Layers, and Vector Databases

The collective fields of knowledge, data, content, and information management have experienced massive changes over the last several years. AI, not long ago a concept mainly reserved for science fiction, has become an everyday tool for information workers.

The Power of RAG

Think back a few years to OpenAI's initial introduction of its generative AI (GenAI) chatbot—the excitement, the promise of marvelous opportunities, the sense of a change for the better. It wasn't long before the euphoria of GenAI wore off and people started noticing that large language models (LLMs) could produce absolutely fabulous results, but they could also deliver flawed information that looked plausible but was untrue.

The Rise of Agentic AI: Why Your AI Agent Is Clueless

Enterprises are rushing to adopt so-called "agentic AI," systems that promise to not just answer questions, but also take actions, automate tasks, and drive decisions. In theory, these agents can draft emails, update records, generate product specs, or flag anomalies without human involvement. In practice? Most deployments don't make it past the demo.