Bandages Plus, an eCommerce site selling compression therapy supplies, bandages, tapes, and ready-made kits, was facing challenges in identifying and reaching its target audience online. The company was struggling to get in front of potential customers without wasting resources on audiences that were not interested in their products. The challenge was to find the best audience opportunities for the client, which required understanding who was already finding Bandages Plus and why. The company needed to segment products into categories that included best sellers, high-margin items, and others, and reflect these categories in shopping ad group segmentation. This would allow them to set bids according to profitability targets.

Gaia, a lifestyle media hub, was facing a significant challenge with its content strategy. The company's blog was primarily filled with self-promotional posts, which were not resonating with potential customers or engaging the targeted community effectively. This lack of engagement was not generating natural links back to the Gaia website, which was crucial for improving the site's Domain Authority (DA). A low DA was hindering organic traffic to the site, which was negatively impacting Gaia's visibility and potential for growth. The company needed a solution that would not only increase its DA but also foster a deeper connection with its audience and promote organic growth.

Houseplans.net, an eCommerce site selling ready-designed house plans to consumers, experienced a significant drop in revenue. This decline was suspected to be linked to Google's Panda and Penguin Algorithm updates, which had a history of causing revenue declines for online retailers. The challenge was to identify the root cause of the problem and implement necessary changes to reverse the revenue decline. An initial SEO audit revealed issues that could be addressed with a thorough link audit and cleanup. However, the challenge was not just to remove detrimental links but also to strategically build new, quality links. Additionally, a significant portion of the site's content was underperforming, which required a comprehensive content audit and cleanup.

TotalHomeSupply.com (THS), an eCommerce website selling products for homes and businesses, was facing significant challenges with their eCommerce platform, Volusion. As their inventory grew and the site’s customization needs increased, they required a more robust enterprise solution. They decided to migrate to Mozu, a more advanced version of Volusion. However, post-migration, THS experienced a significant drop in organic traffic and transactions. Organic traffic, which historically made up more than 65 percent of visits and more than 40 percent of all conversions, dropped nearly 35 percent. Transactions were down 21 percent. Although the conversion rate improved by 24 percent, the overall revenue was down by 7 percent. Further analysis revealed that traffic to category pages was down by 25 percent and product page traffic was down by more than 50 percent. The migration coincided with Google's rollout of Panda 4.2, which further complicated the situation.

TotalHomeSupply.com (THS), an eCommerce site specializing in selling products for private homes and businesses, was facing challenges in tracking conversions and determining the full value of their campaigns. The lack of call tracking made it difficult to identify where some conversions were coming from. Additionally, the client had preconceived ideas about their best-selling products, which were not aligned with the data available in Google Analytics. The challenge was to overhaul the shopping strategy to focus on high-margin, best-selling products and implement a data-oriented approach to improve campaign effectiveness.

Armedia, a veteran-owned systems integrator specializing in digital transformation, IT modernization, and enterprise content management (ECM) solutions, was seeking to optimize its clients' complex digital transformation initiatives and case management workflows. The company, which serves both public and private-sector organizations, including U.S. government agencies, education, transportation and logistics, manufacturing, retail, finance, healthcare, and technology sectors, needed to provide real-time, actionable insight on demand. Armedia frequently recommends its own case management solution, ArkCase, which integrates ECM, business process management (BPM), and customer relationship management (CRM) functionality into a single platform. However, to take its solution to the next level, Armedia looked to embed reporting and analytics capabilities into the ArkCase platform.

Fannie Mae, a leading financial services company, was facing a challenge in managing its vast amount of business data. The company, which enabled the acquisition of more than 2 million home purchases and refinancings, and financing of approximately 598,000 rental units across the United States in 2022, was becoming increasingly digital and data-centric. To leverage all its business data across new and legacy applications, and to break down existing data silos, the company wanted to create an agile and dynamic enterprise data lake. However, the process of managing this data lake was complex and time-consuming. Every single one of its 15,000 datasets went through an initial registration process to assign a unique identifier, and every field had to be documented manually. This approach increased compliance and transparency but made the process slow due to the need to add an elaborate set of metadata to every dataset.

MarketAxess, a fintech company with a digital trading platform used by over 1,800 financial institutions, needed to strengthen its ability to make data-driven decisions. The company's Credit and Market Risk team was responsible for providing accurate and fast reporting to support optimized decision-making across the business. However, they faced challenges with their existing ETL (Extract, Transform, Load) solutions, which were inadequate for quickly and easily consolidating data from a plethora of different sources for analysis. The team needed a solution that could simplify data consolidation and analysis, thereby reducing business risk and improving the efficiency and quality of critical business reporting.

A multinational telecommunications company, operating in 26 countries predominantly in Europe, Africa, and the Asia-Pacific region, was seeking to improve the speed and quality of its customer service interactions. The company provides IT and other solutions to corporate clients globally and wanted to enhance its interactions with its vast subscriber base. Traditionally, customer support has been a labor and cost-intensive service with an impersonal, mechanized interactive voice response (IVR). The company recognized that a more efficient and personalized customer service approach would give it a competitive edge.

Nexway, a leading global vendor of e-commerce solutions, faced the challenge of gaining rapid intelligence on sales performance and the cost of payments. With over 700 clients and activities in more than 140 countries, Nexway specializes in enabling software publishers and software-as-a-service (SaaS) providers to build and manage online stores. As the company grew from a Europe-centric business to a global player, the variety of payment methods and providers it supported also increased significantly. This growth amplified the importance of analyzing payment routes and costs. Reporting to clients on sales performance was a crucial part of Nexway's services, requiring the company to consolidate and analyze data from several underlying systems. As Nexway's appetite for analytics grew, it continued to rely on Data Integration and Analytics to deliver rapid insight to decision-makers within its own organization and its clients.

Portfolio+ is a leading player in the open banking movement in Canada, offering advanced technology, a secure API, and a proven core banking platform. As the company continues to evolve alongside the largest financial institutions in Canada, it must continually innovate to stay ahead of customer expectations. With open banking rapidly increasing in popularity, Portfolio+ needed to provide scalable, fully managed cloud services to meet customer demand. Initially, Portfolio+ planned to migrate to the cloud over three to five years, gradually upskilling its teams and experimenting. However, in 2019, it recognized the need to accelerate its plans to stay competitive.

PÖTTINGER Landtechnik GmbH, an Austria-based leading international manufacturer of grassland and arable farming machines, as well as digital agricultural technology, was facing a challenge. The company, which generates annual revenues of approximately €400 million and employs almost 2,000 people worldwide, was looking to strengthen its data security and resiliency to support increasing sales of its smart farming solutions. With a growing global population, sustainability and efficiency in the agricultural sector were becoming increasingly important. PÖTTINGER had been focusing on digitalization and delivering intelligent precision farming with smart tools, which allowed farmers to monitor their fields and tailor their activities to individual soil conditions. However, with increasing food prices and demand for local produce, PÖTTINGER was growing in many markets around the world and needed to streamline its manufacturing processes with industrial internet of things (IIoT) solutions and data-driven automation. This required additional data storage resources.

CellarEye, Inc. is a company that aims to revolutionize the management of private and professional wine collections by leveraging state-of-the-art Computer Vision (CV) and Artificial Intelligence (AI) technologies. Their goal is to provide a seamless management system that automatically tracks each wine bottle in a cellar, storing both the brand and location into inventory tools without manual entries. However, the team at CellarEye faced a significant challenge in realizing their vision. They needed to develop a reliable object detection model to recognize and track wine bottles as they were registered to and removed from the inventory. The cellar environment, with its thousands of wine bottles, presented a complex scenario with numerous edge cases. The company initially struggled with bad or inconsistent annotations, which made achieving an accuracy rate of over 80% a challenge. They needed a better way to detect problems with their data, understand their model failures, and enable their Machine Learning (ML) team to collaborate with their annotation team to catch labeling mistakes faster.

Goodcall, a company providing businesses with intelligent phone agents, faced a significant challenge in managing and annotating the high volume of data generated by their chatbots. The chatbots, which use automatic speech recognition (ASR) to convert speech-to-text and AI analysis to interpret customer requests, required regular fine-tuning with real-world production data. However, the process of labeling this massive amount of data with high-quality annotations was time-consuming and resource-intensive. Furthermore, Goodcall was unable to match the scale of available data due to their in-house data annotation process. This meant that every piece of unlabeled data was a missed opportunity to improve their models. To enhance model performance and customer experience, Goodcall needed a scalable, sustainable approach for labeling large quantities of data.

Kaleido AI, a Vienna-based company, is dedicated to simplifying complex technology by creating tools that accelerate workflows and foster creativity. The company introduced remove.bg, an automatic image background remover, and Unscreen, a video background remover, which gained immense popularity and led to its acquisition by Canva in 2021. However, Kaleido AI faced a significant challenge in improving its machine learning models. The company's models required a large volume of high-quality data, but they encountered several edge cases in a specific segmentation task where their model performed poorly. Collecting and labeling tens of thousands of real-world images with a large diversity of patterns, images, backgrounds, and textures was difficult. Open datasets did not have enough high-quality images of this particular class. Kaleido AI initially relied on real-world data to train its segmentation models, but this approach was complex, resource-intensive, and costly.

SAP, a leading software corporation, was facing a challenge in improving its products around document processing, particularly those dealing with invoices, purchase orders, and payment advices. The team had a vast collection of customer documents but required a partner to create a comprehensive dataset to enhance their accounts payable products while respecting data ownership, privacy, and sensitivity. The need for high-quality data was paramount for performant models. SAP needed superior quality training data to train models for processing and extracting crucial information from purchase orders and invoices in English, German, and Spanish. The variability in customer data, with some providing thousands of documents a week and others taking months for a fraction of the same volume, added to the complexity of the challenge.

Velodyne Lidar, a company that builds lidar sensors for safe navigation and autonomy across various industries, was facing a challenge in managing and selecting relevant training data from the large quantities of sensor data they collected. The data team found it relatively easy to classify common indoor robotics scenes as these scenarios made up a large portion of the datasets captured on their test robots. However, finding rarer scenarios, such as a warehouse employee stacking boxes on the top of a scissor lift, proved to be a difficult task. The team was in need of an out-of-the-box solution that could provide the necessary tools for efficient data selection and management.

Voxel, a company leveraging AI and computer vision to manage risk and operations, faced two significant challenges. Firstly, they needed to maintain high-quality training data for their computer vision system. Secondly, they sought to automate their labeling process for faster throughput while retaining their in-house annotation team. Voxel had already invested in an in-house annotation team of subject matter experts, but they were struggling with efficiency in their labeling operations. They had been using an open-source solution, Computer Vision Annotation Tool (CVAT), which was causing bottlenecks as they increased the volume of annotations needed for model training. From an operational perspective, Voxel found it difficult to efficiently collect data and insights on the data labeling process, leading to significant manual effort. The tool couldn’t effectively link data quality to individual annotators, making it hard to identify the cause of low-quality labels. On the engineering side, Voxel had to custom-build data pipelines for new customer projects, a process that took multiple engineers four weeks for each project.