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Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed for reliable business operations. […]

Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A guardrail that’s too strict

Google is partnering with the Massachusetts AI Hub to provide every Baystater with no-cost access to Google’s AI training. ​ 

There’s a lot of excitement right now about AI enabling mainframe application modernization. Boards are paying attention. CIOs are getting asked for a plan. AI is a genuine accelerator for

New alternatives, “understand” and “ask” buttons in Google Translate help you navigate the complexities of natural language. ​ 

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and

Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and

Nano Banana 2 (Gemini 3.1 Flash Image) delivers Pro-level intelligence and fidelity for all image applications. ​ 

Our latest image generation model offers advanced world knowledge, production-ready specs, subject consistency and more, all at Flash speed. ​ 

Organizations and individuals running multiple custom AI models, especially recent Mixture of Experts (MoE) model families, can face the challenge of paying for idle GPU capacity when the individual models