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Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after you’ve ingested your data, migrating to a different model means re-embedding your entire corpus, rebuilding vector indexes, and validating search quality from scratch. The right embedding model should deliver […]

Learn how Google’s NAI framework uses AI to make technology more adaptive, inclusive and helpful for everyone. ​ 

Google Cloud built an industry-first AI tool to help U.S. Ski and Snowboard athletes. ​ 

Learn more about Google’s new ad that will run during football’s Big Game on February 8. ​ 

Google AI announcements from January ​ 

Marketing teams face major challenges creating campaigns in today’s digital environment. They must navigate through complex data analytics and rapidly changing consumer preferences to produce engaging, personalized content across multiple channels while maintaining brand consistency and working within tight deadlines. Using generative AI can streamline and accelerate the creative process while maintaining alignment with business

This post is cowritten with James Luo from BGL. Data analysis is emerging as a high-impact use case for AI agents. According to Anthropic’s 2026 State of AI Agents Report, 60% of organizations rank data analysis and report generation as their most impactful agentic AI applications. 65% of enterprises cite it as a top priority.

Many organizations need to manage file uploads across different cloud storage systems while maintaining security and compliance. Although Google Drive provides APIs for integration, organizations often don’t have the technical experts to interact with these APIs directly. Organizations need an intuitive way to handle file uploads using natural language, without requiring specialized knowledge of the

Building production-ready AI agents requires careful planning and execution across the entire development lifecycle. The difference between a prototype that impresses in a demo and an agent that delivers value in production is achieved through disciplined engineering practices, robust architecture, and continuous improvement. This post explores nine essential best practices for building enterprise AI agents

Performing research and clinical analytics on vast amounts of clinical data can be difficult. Healthcare data scientists and epidemiologists possess deep domain expertise in patient care, disease patterns, and clinical outcomes, yet they often spend weeks navigating complex data infrastructures, writing boilerplate code, and wrestling with technical barriers before they can answer a single clinical