Systems | Development | Analytics | API | Testing

How Semantic Layers and Ontologies Create Trusted AI

Learn why an organization’s ontology, a structured framework for how a business defines, connects, and makes sense of its data and knowledge, is the most valuable and most overlooked asset in any AI strategy. Jessica Talisman, CEO and Founder of The Ontology Pipeline, and Tony Seale, Founder of The Knowledge Graph Guys, break down what it actually takes to build trusted AI, covering everything from semantic layers and knowledge graphs to why provenance is non-negotiable.

RAG Pipeline Testing: How to Validate Retrieval, Context Use & Answer Accuracy

Large Language Models (LLMs) are impressive, but they are not without significant flaws. Their biggest hurdles are "knowledge cut-offs" where they cannot access information created after their training, and a tendency to "hallucinate" or confidently state false information. These models often struggle with the specific or real-time data that modern businesses rely on daily.

From Insights to Action with Your Personal Work Agent

Stop switching tools. Start getting work done. Snowflake Intelligence is a personal work agent that helps you analyze data, generate insights, and take action—all in one place. Ask questions, automate workflows, and connect to the tools you already use, all within Snowflake’s governed platform. Learn how teams are using Snowflake Intelligence to move faster, collaborate better, and work at the speed of AI.

Get work done in one place with Snowflake Intelligence

See how Snowflake Intelligence transforms everyday work with a personal work agent built on your enterprise data. In this demo, a sales leader goes from insights to action in minutes—analyzing accounts, preparing meeting briefs, collaborating via Slack, and uncovering root causes with Deep Research—all in one seamless, governed experience.

AI-Ready APIs for Legacy Systems

80% of enterprise apps still use decades-old systems, but accessing their data for AI is tough. The challenge? Security risks, outdated interfaces, and slow performance. Here's the solution: API abstraction. This method creates a secure, no-code layer between AI and legacy systems. It keeps your old code intact while enabling AI to access data safely and efficiently.