Anheuser-Busch InBev (AB InBev), a global corporation known for its popular beer brands like Budweiser, Corona, and Stella Artois, was facing challenges in optimizing its beer filtration process. The filtration process, which is crucial for achieving the best beer taste and meeting brand-required turbidity levels, involves many unpredictable variables. The existing technology could only handle basic logic, using meters to monitor and react to adverse conditions such as a change in pressure. AB InBev recognized the potential of machine learning (ML) and artificial intelligence (AI) in leveraging a larger dataset to better predict and prevent potential issues during filtration. However, the company needed a partnership and provider that could enable them to deploy ML quickly and effectively.

Benetton Group, a globally recognized fashion company with a network of 4,000 stores, was faced with the challenge of improving its online customer experience during the COVID-19 pandemic when physical stores were shut. The company had already digitized its shopping experience and built a marketing data lake to understand its customers better. However, it sought to enhance its recommendation tool for online customers and provide a more personalized, real-time shopping experience. The existing content management solution had a built-in recommendation tool, but it lacked sophistication. Benetton Group wanted to leverage advanced analytics and AI to gain detailed insights into shopping patterns, store performance in a multichannel environment, and how to localize recommendations for a global customer base.

BBVA, a global banking and financial services group, was faced with the challenge of adapting to the rapidly changing landscape of digital payments. The bank noticed a significant increase in mobile payments, particularly during the COVID-19 pandemic, with the percentage of customers using this method rising from 4.4% to 23%. As part of its digital transformation journey, BBVA aimed to offer its customers an exceptional range of services and a great banking experience. The bank was already using Google Maps Platform to help customers find their nearest branch or ATM locations, but it wanted to further leverage the potential of Google Maps Platform solutions. BBVA's mobile banking app was used by 71% of its customers in Spain, and was accessed more than 120 million times a month. The bank wanted to provide more information about each customer transaction to offer a better financial experience for digital customers.

BharatPe, a fintech company founded in 2018, aimed to make digital payments more accessible for over 10 million small offline merchants and kirana store owners in India. However, the company faced challenges in managing the massive amounts of data generated daily from payment processing to business analysis. Prior to using Google Cloud, BharatPe managed its legacy data warehouse with limited capacity to run a large number of queries. The company ran key performance indicator (KPI) reports, without the ability to understand real-time data patterns. Loading three months of data for quarterly reports took more than 30 minutes on the legacy system, and in some cases, queries failed because the system could not scale to support analytical needs. Additionally, BharatPe operates in a multi-cloud environment for disaster recovery and needed a data platform that could run queries against data, regardless of where it resides.

Assurance IQ, a direct-to-consumer platform offering personalized health and financial wellness solutions, faced a significant challenge in its early days. The company's data team was small and struggled with a scattered internal view of the business due to disconnected reports in siloed tools. They were using Excel, SQL, and other in-house tools, but lacked a true business intelligence (BI) platform. This resulted in inefficient use of time and a lack of a complete real-time view of the business for decision-making. Leadership lacked critical insight, and there were constant challenges around trust and consistency of the data. The data team wanted to ensure that each team member was looking at the same clean, accurate data and wanted to empower employees to explore the data on their own. However, the visualization tool they were using lacked version control and standardization.

Banco Davivienda, Colombia’s third largest bank, was facing a challenge in improving its customer ratings due to its continued reliance on manual processes. Despite having a reputation as a banking innovator and a trusted financial services provider, the bank recognized the need for a digital transformation to enhance customer interaction and drive efficiencies. The bank identified six transformation projects, including a sales performance model, sales digital support, self-learning, digital and effective meetings, digital workflows approval, and project management. However, the bank needed a technology partner to support this transformation and help change the way people worked, drive efficiencies, and improve performance.

Bank Rakyat Indonesia (Bank BRI), one of the largest banks in Indonesia, was faced with the challenge of increasing financial inclusion among unbanked Indonesians. The bank had an ambitious target of having 84 percent of Indonesians participating in the banking system by 2022. However, the bank's legacy technologies were proving to be a hindrance in achieving this goal. Each of the bank's products had their own public APIs, which were difficult to manage, secure, and monetize. Additionally, the process of onboarding new partners using host-to-host and VPN technology was time-consuming, taking up to six months. The bank also faced the challenge of reaching a largely rural population, with an estimated $8.3 billion in currency being held outside the banking system.

bolttech, an international insurtech business, was launched in 2020 with the aim of building a leading, technology-enabled ecosystem for protection and insurance. The insurance sector has traditionally been slow to embrace trends such as hyperconnectivity, and bolttech saw an opportunity to change this. The company's technology strategy involved enabling both supply side insurance carriers and demand side business partners to connect seamlessly to its exchange. However, bolttech faced challenges in finding an API management solution that could provide a superior integration experience with its partners across Asia, Europe, and the United States. The solution needed to support growth in traffic, promote a thriving developer ecosystem, and provide efficiency benefits from the deeper connection between systems and components.

ARD, the world’s largest public-service broadcaster network, was facing challenges in keeping up with changes in how users consume content. The organization's previous hosted data center infrastructure was 10 years old and had a monolithic architecture, which resulted in a development turnaround cycle of three months. Video content was distributed without being tailored to particular devices. In 2018, ARD decided to change the monolithic infrastructure for its video delivery platform to improve the user experience, regardless of how users were watching or listening. The challenge was to build a new platform that could cover all touchpoints, support a truly digital-first strategy, and improve the user experience on a global scale.

In Indonesia, the fishing industry has traditionally been fraught with challenges. Fishermen relied on traditional knowledge to predict weather patterns and locate prime fishing grounds, leaving much of their catch and sales to chance. The increasing impact of climate change further complicated these predictions. Additionally, fishermen in remote villages were dependent on middlemen to sell their catch, store, and transport the fish to larger markets. This process often resulted in lower income for the fishermen. Furthermore, the fishing industry was largely paper-based, slowing down the flow of information and making it difficult to trace seafood from the fishermen to the consumer. Exporting fish abroad was a complex process due to the paperwork and logistics involved, which many fishermen did not understand. Lastly, only 25% of fishermen in rural villages used smartphones due to limited power supplies and low digital literacy, posing a significant challenge to digitizing the industry.

Asahi Group Holdings, a global corporation with a diverse portfolio in food and beverages, was facing challenges with its legacy systems. Back in 2014, the company lacked the necessary environment and tools to carry out its vision, which was affecting daily operations. The IT department was becoming a cost center, with the team focused on updating tools daily. As part of their growth strategy and mission of creating “a winning Asahi”, the company needed to modernize its business systems. Extending the life of existing assets and moving to a new environment became a priority. However, the company had limited resources to achieve this.

Brainly, a Kraków-based technology company, provides an online learning platform where students can ask questions and get answers instantly. The platform is used by over 350 million students and parents across 35 countries. However, Brainly faced a challenge in making its platform more accessible and user-friendly for mobile users. Traditional typed queries on smartphones were cumbersome and less efficient. Furthermore, Brainly needed to ensure that its solutions were multilingual, given its global user base. The company also faced the challenge of maintaining stable service during peak usage times, especially during the COVID-19 pandemic when the platform gained tens of millions of new users.

Auto Avaliar, a leading B2B automotive eCommerce platform for used vehicles in Brazil, was facing challenges with its legacy data center infrastructure. The company was poised to expand into new markets, but the existing infrastructure could not cost-effectively or efficiently scale to support additional users, new applications, and evolving security requirements. The company needed a reliable, secure, and fast cloud-based platform to support its growth and meet its security needs. Furthermore, Auto Avaliar required a knowledgeable partner to assist with the migration to the new platform and provide ongoing consulting services.

Auchan France, a multi-format and phygital retailer with over 600 locations and 60,000 employees, was facing challenges with its massive data management system. The system was responsible for handling data from 500 applications used across the business. The company was relying on on-premise infrastructure to manage data, but it was struggling with speed, monitoring, and scalability issues due to the rapid growth of its operations. The company was processing approximately five million data flows daily through its database and various applications covering business intelligence, supply chain, financials, B2B, human resources, and more. The platform team at Auchan was seeking ways to maximize the value of the data exchange system and improve access to business intelligence.

bonprix, an online international fashion and homeware retailer, was facing challenges with its traditional on-premises data warehouse setup based on Teradata and a data lake based on Cloudera Hadoop. As the amount of raw data the company collected grew, storage costs rose. The company also had to deal with the complexity of managing two different systems for structured and unstructured data. This not only made data comparison difficult but also hindered the enhancement of their machine learning and artificial intelligence capabilities. bonprix wanted a solution that would allow all their data to be in the same place, be more cost-effective, and enhance their machine learning and artificial intelligence capabilities.

Booksy, a world-leading appointment management platform, was facing a significant challenge in scaling its infrastructure to match its rapidly growing user base. With its client base doubling every year, Booksy needed a scalable architecture to handle the increasing number of bookings. Initially, Booksy was built using on-premises servers, which led to a series of scaling issues. When extra capacity was required, the team had to wait several weeks before new servers were assembled and online. Maintaining the data centers was a lot of work and took their focus away from coding. Furthermore, they needed to do more coding to meet the demands of scaling up, such as refactoring code for larger architecture. The Booksy application ran on a monolithic architecture, making migration to a more scalable solution a complex task.

autoRetouch, a Germany-based company, provides an automated image processing platform for fashion image producers worldwide. The shift to e-commerce has increased the demand for high-quality product images, which often require edits such as background removal and skin retouching. For retailers with hundreds of product lines, manually editing each image to perfection is a time-consuming and costly process. autoRetouch aimed to revolutionize this process by making it more time- and cost-effective. However, to train and run the models for its custom-developed machine learning algorithms, autoRetouch required powerful CPU and GPU processors that need to be available on demand. The platform had to be available around the clock and scale from processing a few images to hundreds of thousands, in seconds.

AVEVE Group, a Belgian agricultural supplier, was facing challenges in coordinating its diverse businesses spread across more than 50 companies and 400 locations. Each of these companies operated independently, running its own administrative processes, leading to a siloed work environment with little communication between different parties. This lack of a unified platform for information and resource sharing was hindering productivity and efficiency. AVEVE Group was looking to centralize and standardize its processes, requiring a robust platform for collaboration. The company also aimed to shift from individual bookkeeping to a shared service center for managing finances for the entire group, necessitating increased inter-company communication.

Blockchain.com, a leading cryptocurrency software company, was facing significant challenges in managing its large databases. The company's primary products, Blockchain Wallet and Blockchain Explorer, required complex calculations on hard-to-access data across vast databases. As the popularity of cryptocurrencies grew and the company expanded to cover other cryptocurrencies such as Ethereum, Bitcoin Cash, and Stellar Lumens, securely maintaining these databases became a major undertaking with significant resource overheads. The company was spending a lot of time and resources on maintaining and managing its infrastructure, which was affecting its ability to focus on domain-specific challenges and deliver a peerless user experience across all products.

Alpred SL, a Spanish information services company, faced the challenge of transforming a traditional print newspaper, Estadio Deportivo, into a sustainable, digital sports media brand. The company aimed to create a free, continuously updated, 100% digital sports news platform for Spanish-speaking sports fans worldwide. However, the existing infrastructure of Estadio Deportivo was entirely on-premise, and its content management system (CMS) was designed for both print and online versions, which was obsolete and no longer fit for Alpred's purposes. The company was dealing with constant setbacks, errors, and outages, which made it difficult to manage. Moreover, the sports world moves fast, and Estadio Deportivo’s services needed to be constantly available and handle considerable traffic peaks, especially on match days.

Breuninger, a luxury department store in Germany, was facing a challenge with its complex IT landscape. The company was fragmented into many departments, each with their own technology stack focusing on their own use cases. They had on-premises databases and other systems such as SAP, all gathering different types of data for different business units. This dispersed IT landscape made it difficult for the company to make the most of its data. Furthermore, the company's online storefront, which brought in a significant 30% of sales in 2018, presented exciting opportunities with data. However, to optimize the website and make the online customer experience smoother and more tailored to individual shoppers, Breuninger needed to get its data on track.

Auto Trader (UK), the largest digital automotive marketplace in the United Kingdom, was facing challenges with its existing infrastructure. The company's infrastructure was previously deployed from its own data centers via a private cloud. The company wanted to improve its security and encryption, and create a more agile architecture to enable faster innovation. Auto Trader (UK) wanted to upgrade its security layers and implement mutual Transport Layer Security (mTLS) authentication between its apps. However, retrofitting these changes to its previous infrastructure would have been difficult due to the complexity of modifying hundreds of applications and building additional elements. The company also wanted to optimize its CPU allocation, which was previously uniform across all instances due to the inability to accurately measure the impact of changes.

Ayoconnect, a SaaS company based in Southeast Asia, aimed to provide an open finance platform for developers to deliver a range of financial products to their customers. However, the company faced challenges in launching the platform on a cloud environment due to regulations requiring Indonesians' financial and transactional data to stay within the country. The domestic cloud service provider they initially used could not fully meet Ayoconnect's requirements, leading to performance and stability issues. Furthermore, the company needed to transition from a business-to-consumer model to a business-to-business model, which required standardization and visibility to solve issues created by multiple infrastructure types and API formats across financial services.

Banco de Crédito e Inversiones (Bci), one of Chile’s largest banks, faced the challenge of meeting the changing expectations of its employees and customers who were increasingly comfortable using internet-based tools and services. The traditional ways of banking did not resonate with the younger workforce, particularly the millennials, and the bank needed to evolve to meet their changing expectations. The bank also aimed to get closer to its increasingly digitally engaged customer base. To do this, Bci decided it needed a digital transformation that would bring interactions between employees and with customers closer to their more intuitive digital experiences with consumer apps. The bank also needed greater support for working off-site, including finding ways to replace in-person and telephone meetings with simple, reliable videoconferencing. Bci also hoped to move beyond email as the means for sharing and document collaboration.

Alquería, a Colombian conglomerate of over 24 companies with more than 4,000 employees, faced significant challenges in integrating teams across different cities and locations into a collaborative environment. The company aimed to increase its digital employee base, further digitize its processes, enhance collaboration and productivity in internal processes, and improve security levels to reduce information leaks. The main obstacle to growth was identified as technological barriers, which needed to be broken with cloud solutions for the company to scale. The challenge was to incorporate tools that did not exceed the limit of what was possible, while efficiently allocating licenses and resources for each employee role.

ATB Financial, a crown corporation of the province of Alberta, operates in a highly competitive financial services space. To retain its competitive advantage, ATB launched a bold transformation initiative to reimagine the way banking products and services could be delivered. However, the company faced several challenges. Remote employees often experienced connectivity and performance issues with the corporate VPN, which limited their productivity. Legacy office tools did not foster innovation and often slowed business processes, delaying the company’s ability to deliver new services and respond to customer needs. The ATB banking operations team also struggled with workflows and lost files in public folders, a common issue with legacy office applications.

Bloomreach, a leading Commerce Experience Cloud, faced significant challenges as it grew rapidly since its launch in 2015. The company's growth began to strain its bare-metal IT infrastructure, affecting both development and stability. The company experienced server failures and poor connections with their data center provider, which threatened the reliability of their service. Furthermore, with the advent of GDPR regulations and a global client base, Bloomreach had to prepare for a more complex security environment. Their existing infrastructure was not capable of handling the upcoming challenges, and they needed a solution that could provide scalability, stability, and security.

The global COVID-19 lockdowns during 2020 and 2021 accelerated the growth of online sales, leading to a 34% increase in e-commerce purchases in the U.S. alone. This surge in digital activity led brands across all industries to reassess their digital presence. As businesses look to maximize revenue from e-commerce, the global search engine optimization service market is expected to be worth more than $100 billion in 2025. Botify, an enterprise software company, faced the challenge of helping its customers drive sustainable traffic and increase online revenue in this rapidly evolving digital landscape. The company needed to analyze vast amounts of data, optimize websites for search engines, and provide actionable insights for website improvements.

Bounce, a smart mobility solution in India, was faced with the challenge of making daily commutes stress-free, time-saving, reliable, and convenient for its users. The company, which operates a scooter sharing mobility app, needed a location solution that could scale rapidly and provide accurate location information to its growing customer base. The motorized scooter, being the best selling vehicle in South Asia, was the chosen mode of transport for Bounce. However, the company needed to guide users to the nearest available scooters, provide unmatched location accuracy and information, and quickly scale to serve a growing customer base of more than 2.1 million people. Bounce officially launched its mobility app in September 2018 in Bangalore, one of the largest cities in India with a metropolitan population of more than 10 million. The app was so popular and easy to use that within two years, it had spread to bustling Hyderabad and a host of other cities.

Autofleet, a leading solution provider for fleet and mobility operators, was faced with the challenge of offering its customers a platform that was secure, reliable, and scalable. The company provides solutions for asset-heavy fleets and mobility operators to optimize their operations and launch new on-demand ride services. The comprehensive and modular nature of the company’s solutions meant that reliability, security, and performance were crucial to its operations. Failures and downtime could have a negative impact on their customers' core businesses. Furthermore, the company needed to be able to scale at will and with little notice. Whenever Autofleet signed a new client, the platform was immediately subjected to high volumes of extra traffic, which posed a significant challenge.