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That’s why robust analytics have always been at the heart of Cleverbridge’s offering. The company supports more than $1 billion in software sales through its platform, helping clients understand performance, identify friction and improve returns over time.\u003C/p\u003E\r\n\u003Cp\u003E“Everything we do involves optimizing very small percentages,” says Radu Immenroth, Chief Technology Officer at Cleverbridge. “There is a lot of complexity and countless possible combinations when you think about different countries, payment methods and trade laws. We need to be able to drill really deeply into that data.”\u003C/p\u003E\r\n\u003Cp\u003EWhile the company provides performance insights to internal users and clients through business intelligence dashboards, sometimes users have more complex requests. Historically, those requests required engineers to develop SQL queries, which could be complicated and time-consuming. 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But we also knew there would be a huge advantage in seeing what people ask agents,” says Emanuela Premoli, Director of Product at Cleverbridge. “If we can understand what people want to know, we can create new products and dashboards to meet common needs.”\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_c":{"id":"title-v2-129500c194","type":"heading4","lines":["Saving months of engineering effort with Cortex AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy":{"id":"text-99e1c763d4","text":"\u003Cp\u003ETo provide access to data with minimal engineering effort, Cleverbridge set out to create a natural language analytics experience that could support monthly performance reviews, strategic business insights, forecasting and scenario analysis. The goal was to automate data exploration, segmentation and initial analysis, while allowing customer success managers (CSMs) to interpret the results and recommend the right next steps.\u003C/p\u003E\r\n\u003Cp\u003ECleverbridge originally planned to build these capabilities in-house. However, while proofs of concept worked, the project quickly became more complex as data volumes and business requirements grew.\u003C/p\u003E\r\n\u003Cp\u003E“When we scaled our pilots, we were only getting around 60% accuracy,” says Immenroth. “When you’re working in a high-stakes environment, that lack of accuracy can have seven-figure repercussions.”\u003C/p\u003E\r\n\u003Cp\u003EThat experience clarified what Cleverbridge needed from a long-term AI foundation: enterprise-grade reliability, trusted data access and a roadmap that could support future agentic workflows. 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In addition, the company estimates a potential long-term operational savings equivalent to three full-time employees annually.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"quote_item_2132084119":{"id":"quote-item-e2617f2856","alignment":"left","layout":"simple","quoteSourceName":"Radu Immenroth","quoteSourceTitle":"Chief Technology Officer, Cleverbridge","showQuoteIcon":true,"quote":"Creating our natural language analytics experience with Snowflake was extremely quick. In fact, I don’t think we would have been able to do it on our own, especially when you consider the time and expense of maintaining a platform like that.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_v2_copy_copy_8":{"id":"title-v2-7fdf5321bc","type":"heading4","lines":["Improving performance through speed, clarity and consistency"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_63041":{"id":"text-1a2ad559f2","text":"\u003Cp\u003EWith Snowflake CoWork, Cortex Search and Cortex Analyst, Cleverbridge’s data users can now explore structured and unstructured data using natural language prompts. In just a few months, the results have been significant.\u003C/p\u003E\r\n\u003Cp\u003EThe natural language analytics experience has enabled faster identification of performance drivers, helping teams connect KPI movements to root causes and reduce the time it takes to turn data signals into operational decisions and business strategy. As a result, Cleverbridge CSMs have saved over a thousand hours, representing a 33% reduction in the time it takes to pull insights together.\u003C/p\u003E\r\n\u003Cp\u003E“It used to take hours to produce performance reports for clients,” says Premoli. “Now, it takes five or ten minutes for an agent to produce a slide deck. The reaction from users is really incredible, and we’ve saved the equivalent time of around one full-time CSM a year.”\u003C/p\u003E\r\n\u003Cp\u003EWith faster access to insights, Cleverbridge can provide more timely and relevant performance updates to clients. 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There’s a huge value in more frequent data-driven analysis and decision-making, and that’s what Cortex enables.","showHangingPunctuation":true,":type":"snowflake-site/components/quote-item","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small snowflake-responsive-component-bottom-padding-extra-small"},"title_v2_copy_copy_8_191437395":{"id":"title-v2-60de3d88ff","type":"heading4","lines":["Preparing for the agentic era of commerce"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy_63041_306225306":{"id":"text-d39bd75dc8","text":"\u003Cp\u003EAgentic AI is already helping data teams access insights more quickly and share them more broadly. 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