Almundo, a leading travel technology company and omnichannel agency, was facing a challenge in transforming its ecosystem into a more collaborative model. With operations expanding throughout Latin America, the company had over 800 professionals and more than 330 travel experts working in various areas of the business. The goal was to create a more coordinated work environment that would have a positive impact on productivity. However, the company was relying on legacy tools that were not conducive to collaborative work, distributed access, and productivity of work teams. The challenge was to find a solution that would meet these specific requirements and help the company maintain its leadership position in Latin America.

ARIGATOBANK Inc., a Tokyo-based financial services business, operates a platform called kifutown that connects donors with those in need of financial aid. The platform has grown significantly, handling over 2,000 donation projects as of March 2022. However, the company faced challenges in managing sudden traffic surges, particularly due to the influence of high-profile shareholder Yusaku Maezawa. A single social media post from Maezawa could lead to a significant increase in traffic, posing a challenge to the platform's infrastructure. The company needed a solution that could handle these traffic surges without impacting availability and latency. Additionally, ARIGATOBANK wanted to maintain development speeds and quality while undertaking progressive development, which required clear delineation of responsibilities and efficient operation of the application server.

Asahi Group, a leading food and beverage company, faced significant challenges in its global operations due to its legacy on-premises infrastructure. The company's global units, often born of acquisitions, needed to collaborate seamlessly and in real time. However, the existing infrastructure was causing bottlenecks even in routine tasks such as onboarding new employees, as data needed to be funneled through a centralized hub server. The company's ambitious multi-year cloud modernization program aimed to overhaul its legacy infrastructure to a modernized cloud architecture by 2027. However, the sprawling global business could not afford to skip a beat even in the midst of this transformation. The challenge was to ensure seamless data sharing and cooperation across platforms, offering transformative agility, scaling power, and robust security.

The Customer, a pioneer in Autonomous Systems, was faced with the challenge of migrating its computer vision cloud platform to the Amazon cloud within a four-month timeframe. The migration was necessary to enable the platform to perform highly scalable, real-time weapon detection to identify firearms and suspects in high-security environments. The goal was to provide security and safety to essential businesses, communities, and schools through real-time human behavior recognition and weapon detection technologies, enabled by AI & Machine Learning. The Customer was also looking to protect communities by bringing AI-driven visual imaging and human behavior recognition technology to every school, public building, and business across the country. They wanted to develop a weapon detection solution that they could integrate with their apps in the AWS cloud, to be able to deter, detect, and defend against shooters quickly and efficiently.

The advent of education technology has revolutionized classrooms worldwide, providing a plethora of tools to enhance learning. However, this surge in educational tools also presents numerous challenges, such as identifying the most effective learning devices and equipping them with reliable management and monitoring tools. Blocksi, a company launched in 2011, initially provided internet filtering services through a Chrome-native app. The company soon realized that a significant number of families and schools were downloading its app, prompting it to tailor its tools for the education sector. The challenge was to expand its reach in education and power its business while ensuring the safe and compliant use of devices in schools.

ETH Zurich's Biomedical Informatics (BMI) Group is working on creating the world's largest-ever DNA search index by processing 4 petabytes of sequencing data. The goal is to make the world's genetic code more accessible for medical and scientific research. However, the team faced significant challenges in terms of data accessibility and processing. Despite having access to a vast amount of information in the National Center for Biotechnology Information (NCBI) repository, existing methods did not allow for the most effective use of these datasets. The team's ambitions were curtailed by their other major obstacle: efficient accessibility. Before the switch to Google Cloud, the BMI Group had to limit its operations to smaller sequencing datasets of several terabytes in size, just to keep download and processing times manageable.

bofrost*, a European market leader in direct distribution of frozen food, was looking to evolve its business model to keep up with the growing global market for frozen foods, projected to reach $282.50 billion by 2023. The company's IT infrastructure relied heavily on legacy systems, with two on-premises data centers in Germany and Italy. Information about products was provided to customers via a twice-yearly print catalog, and salespeople used a basic device to access details about orders to be delivered. However, they couldn't access multichannel order histories and other additional information. The company wanted to transform its IT infrastructure to enable new ways of meeting its customers' needs, including a new point of sale (POS) platform to streamline delivery schedules and provide personalized suggestions to facilitate upselling opportunities.

Botmaker, an AI platform that creates and administers voice and text enabled bots, aimed to provide exceptional customer service across various channels. The challenge was to automate conversations between brands and people using AI, requiring an infrastructure capable of processing millions of messages, understanding them, and providing accurate responses in real time, 24/7/365. This was crucial to maintain the brand's goodwill with its customers, making data processing speed and scalability a significant technical challenge. Additionally, Botmaker had to meet strict safety requirements imposed by various customers, including large banks and international insurance companies.

Digital marketing firms are often faced with the challenge of managing new leads efficiently. The traditional method involves manually checking Facebook Lead Ads every hour or two, or exporting a list of new leads at the end of each day for follow-up. This process is not only monotonous but also time-consuming, leading to delays between a lead entering their information and the firm's response. The firms also have to monitor mentions of their brand on social media and across the internet, which is a full-time job in itself. Keeping an eye on competitors adds to the workload, making it almost impossible to track new mentions without missing some. The firms needed a solution that would automate their lead workflow, eliminating the need for manual work and ensuring quick response to new leads.

Lenovo, a multinational technology company, was facing a challenge in balancing supply and demand for its products among Brazilian retailers. The company aimed to predict the sell-out volume, the number of units of a product that retailers sell to customers, but was constrained by resources. The team had started developing R code to predict sell-out volume, with a goal to have it updated weekly for their top ten retail customers. However, with only 2 people writing 1,500 lines of R code for one customer each week, reaching their target of predictions for ten customers each week was impossible. The team needed to either invest in more data scientists or find a tool that could automate all the modeling and forecasting steps.

StarKist, a leading food company in the United States, was facing significant challenges in managing its supply chain. The demand planning function and the financial planning and analysis team were using separate sets of spreadsheets to track inventories and orders for 100 key accounts and 200 product SKUs. This disjointed approach was not only inefficient but also prone to errors, especially when dealing with new SKUs, discontinued SKUs, and customer allocations. The supply chain's reliance on an SAP ERP for production planning resulted in a massive amount of data accumulating in a complex spreadsheet that was becoming increasingly difficult to maintain. Furthermore, the lack of real-time data between the supply planning and demand planning functions made it impossible to conduct in-depth analysis of SKU traffic or to quickly respond to volatile market conditions.

Keller Williams, the world's largest real estate technology franchise, was driven by a desire to foster meaningful relationships and provide a seamless user experience for agents and customers. They wanted to create a platform that would allow agents and customers to navigate the real estate market with ease. However, they faced the challenge of finding a solution that was highly flexible and customizable, as opposed to a Software as a Service (SaaS) suite that did not allow for customization. The company needed a solution that would enable them to manage contacts, marketing profiles, campaigns, and listings effectively.

GE Healthcare, a global leader in medical technology and digital solutions, faced several challenges in managing support requests from its channel partners. The company provides medical equipment and services to clinicians worldwide, and when a piece of equipment is down, the channel partner may reach out to GE Healthcare for help. Capturing and managing these support requests was a complex task, often leading to hours spent searching for information. Additionally, GE Healthcare needed to ensure that channel partners were delivering the same level of support to their customers as those supported directly by GE. However, with their existing customer support process, GE managers had little visibility into support requests. They were unaware of the number or status of requests and who was responsible for each. This lack of process control and visibility into channel partners led to the realization that a change was necessary.

Israel is currently undergoing the largest infrastructure development in its history, with projects ranging from lite-trains and underground trains to cross-country highways and heavy railways. However, coordinating these large-scale projects in a small country like Israel, where resources are limited, is a significant challenge. Each project requires multiple approvals from various entities, including the Israel Water Company, the Israel Electric Corporation, and several federal bodies. This process was previously handled manually, leading to inefficient paperwork, misunderstandings, and delays in the coordination process, which in turn delayed project delivery. The Israeli government recognized the need for a more efficient system to manage the coordination of these infrastructure projects.

In the highly competitive real estate market, quick response to leads and efficient management of these leads are crucial. Jonathon Kerester, the managing broker at Realty Investment Advisors (RIA), was faced with the challenge of connecting email marketing and customer support tools. He needed a system where Zendesk tickets would be automatically filled out whenever leads subscribed to specific newsletters in Mailchimp or updated their profiles with RIA. The challenge was to reduce the time spent on cold calling and to connect with leads faster. The goal was to automate the initial follow-ups and lead management to increase efficiency and productivity.

SelectQuote Insurance Services, a company providing insurance quotes, was faced with the challenge of providing reliable insurance quotes to clients in an efficient manner. The company wanted to streamline the process of cross-application functions without the need for a developer to build a series of APIs. The process involved a potential client filling out a form with their phone number, assigning the client to a SelectQuote agent, pulling quotes from SelectQuote's vendors, and then sending the quotes to the client. The company wanted to automate this process to save time, money, and resources.

Chang, a popular beer brand in Thailand, was facing a significant challenge. The brand had not updated its advertising or packaging since its inception twenty years ago. As a result, it had earned the reputation of being the 'old man's beer' and was rapidly losing market share to more modern competitors. The beer, the bottle, and the brand were all in desperate need of a change. The challenge was not just about rebranding, but also about reconnecting with a younger, more modern audience and changing the perception of the brand.

AirAsia, a leading airline in the Asia Pacific region, aimed to become a 'data-first' business and a digital airline. The company needed technologies and services that could capture, process, analyze, and report on data, while delivering value for money and meeting its speed and availability requirements. The airline also wanted to minimize infrastructure management and system administration demands on its technology team. AirAsia realized that only a cloud service could meet its needs and began evaluating the market. The company also faced challenges with its legacy on-premises directory as it expanded to new markets and regions. Managing multiple servers across a number of on-premises data centers and the public cloud proved costly and time-consuming.