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Hear the latest global insights from our consultants on the ground.
INSIGHTS
Enterprise AI creates value only when organisations industrialise the work around it: the operating model, the decision process, and the data products that provide reliable context
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Managing multiple AI providers means fragmented billing, scattered API keys, and blind spots in your data governance. The Vivanti AI Gateway puts it all behind a single interface — route requests to any model, set per-team budgets, and enforce data protection policies before a single token leaves your network.
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Everyone wants AI. Few know how to make it valuable. Several years ago, I wrote about the importance of cross-functional data science teams and the critical role of a "data science leader" - the individual who bridges the divide between scientists, engineers, domain experts and the business. That article argued for a single linchpin - a *golden thread* - who could see an ML model from design through to production, ensuring value creation was validated at every step. That golden thread was what mended engineering and product teams together, giving coherence to what would otherwise be a fragmented effort.
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Legacy chatbots trap customers in rigid scripts and take months to maintain. CX Agent Studio, powered by Google Cloud and Gemini, lets you design AI agents that hold real conversations, connect to live business data, and hand off to humans seamlessly — deployed in weeks, not quarters.
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Snowflake Intelligence transforms how organisations interact with data, enabling natural language queries across structured and unstructured sources whilst maintaining enterprise security and governance. We're helping organisations implement conversational analytics that empower business users with direct access to insights - no SQL required!
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Model Context Protocol is an open protocol that lets AI agents (LLMs) interact with other applications in a standardised way. Think of MCP as the API for AI
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How a unified, secure, and visual approach to data integration can simplify pipelines for teams of any size.
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In the rapidly evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) stands out as a groundbreaking approach to enhancing the capabilities of language models. By integrating information retrieval with advanced generation techniques, RAG systems can provide more accurate, contextually relevant, and informative responses. This article provides a high-level overview of RAG, delving into its architectural principles, the role of word embeddings and vector stores, and the process of preparing and managing data.
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Databricks clean room allows you to virtually put your data in a “share”, more like a folder, and share the “share” with recipients. A recipient can be an external user with or without a Databricks workspace.
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Unit testing is essential to catch errors early, ensure code quality, and maintain functionality as software evolves. In this article, we will go through the process of setting up unit tests in DBT
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By understanding CLV, businesses can make data-driven decisions that increase customer loyalty, retention, and revenue.
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BigQuery Remote Functions allows integration of non-SQL logic with SQL queries. Using remote functions, data can be decrypted within a query without storing the decrypted data.
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