{"id":31035,"date":"2025-12-09T11:00:58","date_gmt":"2025-12-09T09:00:58","guid":{"rendered":"https:\/\/scroll.media\/?p=31035"},"modified":"2025-12-09T12:28:06","modified_gmt":"2025-12-09T10:28:06","slug":"data-ai-and-efficiency-how-reenbit-builds-data-solutions-for-global-clients","status":"publish","type":"post","link":"https:\/\/scroll.media\/en\/2025\/12\/09\/data-ai-and-efficiency-how-reenbit-builds-data-solutions-for-global-clients\/","title":{"rendered":"Data, AI, and Efficiency: How Reenbit Builds Data Solutions for Global Clients"},"content":{"rendered":"\n

Today, data is the fuel for AI. Companies spend vast amounts of money to organize, label, prepare it for AI training, or even simply access it when it belongs to someone else.<\/p>\n\n

Ukrainian company Reenbit<\/a> started working with data sets long before the \u00abAI boom.\u00bb<\/p>\n\n

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\u00abWe clearly understand that every year, the amount of data created by businesses only increases. If you combine and organize it correctly, there are tremendous opportunities for analysis, which leads to better decision-making and improved business efficiency,\u00bb explains Volodymyr Yarymovych, co-founder and Chief Data Officer of Reenbit.<\/em><\/p>\n<\/div><\/div>\n\n

Reenbit\u2019s core areas include custom software development, SaaS platforms, process automation, design services, and artificial intelligence. Over the last five years, especially with the rapid spread of AI, the company has doubled down on its data focus.<\/p>\n\n

But the work goes far beyond AI. Reenbit\u2019s expertise covers retail, e-commerce, fintech, energy, and even fashion. Its client geography spans Europe, Scandinavia, and the Benelux region, the UK, the US, Canada, and South Africa. With over 70 completed projects and dozens more in progress, many new clients come through referrals or by expanding existing collaborations. And an increasing share of these orders is specifically for data-related work: Reenbit has been turning raw data into real business value for years.<\/p>\n\n

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\u00abWhen we work with data, we\u2019re interested in much more than reports or dashboards,\u00bb says Volodymyr. \u00abWe look for answers to simple questions: Where can a business eliminate unnecessary movement and get more results? How do we reduce costs, optimize supply chains, boost team productivity, or react faster to shifting demand? Our job is to make these answers obvious, practical, and easy to use in daily decisions.\u00bb<\/p>\n<\/blockquote>\n\n

What makes this process challenging? Scroll.media\u2019s editorial team asked Reenbit about the nuances of working with data and uncovered a lot of insider details.\r\n<\/p>\n\n

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After a short intro about the company, Volodymyr immediately highlights the key challenge in data projects: clients want to see ROI as soon as possible.<\/span><\/p>\n\n

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\u00abDuring our work, we try to close the entire project vertical as quickly as possible for at least one agreed metric. In practice, this approach works well \u2014 clients get interim results faster and understand the process better,\u00bb he explains.<\/p>\n<\/blockquote>\n\n

Data work is complex. A full project typically takes 6\u201312 months. For clients without a technical background, it\u2019s not always clear what requires so much time and effort. Yet they know that data can reveal where money is wasted, which processes slow operations down, where margins leak, which customer segments lack attention, and where exactly new growth potential lies.\r\n\r\n<\/p>\n\n

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Photo by Reenbit.<\/em><\/p>\n\n

Volodymyr arrived at this approach after working with a large Italian fashion brand. Although the entire plan had been approved, the client wanted immediate results. However, data collection \u2014 the first and most crucial stage \u2014 cannot be skipped.\r\n<\/p>\n\n

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\u00abThat\u2019s when we discovered that a significant portion of the client\u2019s data was corrupted due to a failed historical migration on their side. But it was difficult for them to understand why they still didn\u2019t see dashboards,\u00bb Volodymyr recalls. The situation became an important lesson for our business.<\/p>\n<\/blockquote>\n\n

The dashboard every Reenbit customer wants to see is essentially the final product. However, assembling it and defining what it should display is a substantial challenge in its own right.<\/p>\n\n

Data as a driver of Reenbit\u2019s business<\/h2>\n\n

To build a dashboard, you need high-quality data. That\u2019s why Reenbit\u2019s primary goal is to ensure quality and consistency. When analytics are built on insufficient data, businesses make decisions based on contradictions.<\/p>\n\n

To avoid this, a significant effort is invested in cleaning the final analytical repository \u2014 filling gaps, removing duplicates, and resolving issues caused by legacy software. And Reenbit shows value even at this stage.<\/p>\n\n

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\u00abPeople don\u2019t always think about it, but building data analytics involves using specific tools, and some of them are extremely expensive. We train our engineers and analysts in business thinking so they understand client investments and the real ROI of the tools they choose. It\u2019s easy to pick something trendy and expensive (like Fivetran), but those costs aren\u2019t always justified for the customer. We try to choose the right toolset that helps clients save money,\u00bb Volodymyr explains.<\/span><\/em><\/p>\n<\/div><\/div>\n\n

Today, many believe data is needed mostly for AI, and that the market revolves around that. But as Reenbit notes, AI is only part of the current demand.\r\n\r\nVolodymyr shares an unexpected example: the fashion industry. And not in the context of boosting sales and quality analytics.<\/p>\n\n

In recent years, major American and European fashion brands have been required to implement Digital Product Passports (DPPs) \u2014 regulators demand greater transparency for consumers, showing the full lifecycle of each item, including who made it, where, when, from what materials, and how. \u00abThis creates additional demand for data-driven projects in fashion. We now have several such clients,\u00bb shares Volodymyr, offering an inside look into an industry where, at first glance, data seemed to be used only for boosting sales.<\/p>\n\n

Another interesting <\/span>success story<\/span><\/a>\u00a0involves a consulting agency developing an analytical product for cost reduction. Companies uploaded their cost data and received dashboards showing where they overspent or underinvested.<\/span><\/p>\n\n

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\u00abThe foundation of the solution was comparative analysis using industry benchmarks from Gartner and Computer Economics. We considered industry, company size, and expense categories, calculated current spend, and visualized the comparison.<\/p>\n\n\n\n

The challenge was technical as well: benchmarks arrived in PDF files with different structures. In the first MVP, we manually transferred numbers from graphs into our data model. Later, we automated the process \u2014 something AI now handles easily. Another challenge involved analyzing companies operating across multiple industries: we developed our own algorithm that merged benchmarks from different sectors and calculated a custom benchmark,\u00bb Volodymyr notes.<\/p>\n<\/blockquote>\n\n

Regardless of whether the project is a DPP, AI product, or something entirely different, Reenbit follows the same core workflow:<\/p>\n\n