Pepperdine University, a leading institution for higher education, was facing challenges in effectively managing long-term follow-ups and setting appointments with prospective students. The university's staff was struggling to engage with prospective graduate student inquiries in a timely and efficient manner. This was particularly problematic for the Graduate Schools of Psychology & Education admissions team, who were tasked with nurturing and qualifying these leads. The lack of prompt engagement was leading to a lower response rate from prospective student inquiries, which in turn was affecting the university's ability to meet with more prospective students and increase their student intake.

Brainly, a leading global learning platform, faced a challenge with their machine learning-powered feature, Snap to Solve. The feature allows users to upload a photo of a problem, which the system then detects and provides solutions for. The Visual Search team, responsible for the Visual Content Extraction (VICE) system of Snap to Solve, used Amazon SageMaker to run their computing workloads and serve their models. However, as the number of training runs on their large compute architectures increased, they found that their logs from Amazon SageMaker needed to be trackable and manageable to avoid workflow bottlenecks. They needed a tool that could scale regardless of the experiment volume. While they tried using SageMaker Experiments for tracking, they found the tracking UX and Python client unsatisfactory.

deepsense.ai, an AI-focused software services company, was tasked with a project for a leading Central and Eastern European food company. The project involved using machine learning to analyze the impact of promotional campaigns on sales. The food company runs various promotional campaigns for different products and wanted to create a model that predicts the number of sales per day for a given product on a promotional campaign. The challenge was the complexity of the data involving a large corpus of data sources, hundreds of different products, contractors, thousands of contractors’ clients, different promotion types, various promotion periods, overlapping promotions, and actions of the competition. It was also difficult to determine whether the sales increase was caused by any of the promotions applied, by the synergy between them, or it took place regardless of any campaigns.

Waabi, a company focused on developing the next generation of self-driving truck technology, faced a significant challenge in managing their large-scale experimentation workflow. Their Machine Learning teams, organized around different technical pillars, constantly launched experiments for different tasks, seeking model improvements by iteratively fine-tuning them and regularly comparing results against established benchmarks. The data involved in these experiments was diverse, including maps, LiDAR, camera, radar, inertial, and other sensor data. Keeping track of the data collected from these experiments and exporting it in an organized and shareable way became a challenge. The company also identified a lack of tooling for planning and building consistent benchmark datasets. They needed a solution that would allow them to share benchmark results in a constant place and format and retain data for later comparison after the end of a project.

Woven Planet, a subsidiary of Toyota, is focused on building the safest mobility in the world with a particular emphasis on automated driving. The Automated Mapping team at Woven Planet is tasked with creating automotive-grade maps for use in automated and autonomous-driving vehicles. This requires the use of aerial orthographic projection, a method that has been used in the development of consumer-grade navigational maps. However, using this data to meet the rigorous requirements of automated driving at a continental scale is a significant challenge. The maps for automated driving applications need a level of detail, accuracy, and precision far beyond those of their consumer-grade counterparts. This requires processing large volumes of data. The Automated Mapping team needed an orchestration system that could scale to meet elastic workloads, easily toggle between structured and unstructured datasets, and provide long-lived pipeline stability for continuous, region-based map updates.

Carson Group Holdings LLC, a comprehensive ecosystem for advisors, was seeking ways to enhance their marketing efforts to help their investment advisor clients acquire new customers more effectively. They decided to adopt AI/ML, starting with a machine learning model for scoring leads received from Salesforce. The goal was to narrow down their leads, focusing on customers with the highest likelihood of investing, thereby reducing time spent filtering leads that are less likely to convert. This would optimize costs and drive growth for their clients more efficiently. Carson Group had the right data for training ML models and saw the potential to streamline the entire process of evaluating and scoring leads by their sales and marketing teams. They aimed to replace their existing predictive system, which relied on complex rules and heuristics, with a self-training machine learning solution for higher accuracy and efficiency in lead scoring.

Dynamo Software Inc., a leading cloud provider of alternative investment management software, was seeking to enhance its document classification platform through AI and automation. The platform was designed to store, classify, and transfer information and metadata from various documents to appropriate investments. However, Dynamo wanted to improve the accuracy of document classification and gain the ability to make predictions based on a document's content. The goal was to reduce the amount of repetitive manual work performed by their data team, lower operational costs, increase performance, and minimize the time needed for making decisions on client investment portfolios. The existing platform received thousands of various types of documents every month, some of which were manually added by managers. Dynamo wanted to significantly improve the accuracy of their existing ML tool, automate a portion of the data processing pipeline, and achieve at least 85% accuracy on new data.

Brandfolder, a Denver-based company offering digital asset management (DAM) solutions, was seeking to enhance its service offerings to provide high-impact customer experiences and increase its competitive edge. The company was looking to introduce new data-driven features without complicating the user experience. Key to meeting customers' unique business needs and competing in the fast-moving DAM industry was the integration of big data, artificial intelligence (AI), and machine learning (ML). However, Brandfolder needed a public cloud provider that could help it scale its data pipeline cost-effectively while providing access to advanced AI technologies. After trying two other cloud providers, Brandfolder decided to standardize on Google Cloud.

Brandwatch, a digital consumer insights company, was facing a significant challenge in its business intelligence (BI) operations. The company needed to understand and support its clients' needs by analyzing trends and patterns in the way its platform was used. To achieve this, Brandwatch combined service usage data from Mixpanel with customer data in Salesforce. However, its existing infrastructure was not supportive of this integration. The data lived in disparate silos that required manual, time-consuming aggregation, making the insights less useful by the time the data was pulled together for analysis. The company was unable to analyze customer information together with data on how customers used its platform. This meant that the BI team had to manually combine both types of information on spreadsheets to create reports. The process was not only time-consuming but also inflexible, limiting the analysis Brandwatch could do.

Breaktime, a Taiwan-based company founded in 2017, provides data consulting services to bloggers and publishers to generate advertising revenue. The company operates the Zi Media Network and the Zi Power Ads AI advertising allocation system. The goal of Zi Power Ads AI is to simplify the use of 'supply-side platforms' for publishers to sell digital advertising impressions via automated auctions. However, Breaktime faced challenges with their initial infrastructure. They launched Zi Power Ads AI on infrastructure in an internet data center in Taiwan and then moved to a cloud infrastructure provider to support increasing traffic volumes. However, they encountered reliability and stability issues, including virtual machine shutdowns that prevented Zi Power Ads AI from being available to publishers for extended periods. This led Breaktime to evaluate other providers.

Cvent, a global event management platform, faced significant challenges with its API discovery due to its adoption of a microservices architecture. The company's portfolio of private, partner, and public APIs had grown immensely, making API discovery almost impossible. This was further complicated by the presence of hundreds of software development teams located around the world. Without a central source of API truth, engineers often recreated functionality that was already available elsewhere in the company. Additionally, the lack of a common API toolset across the company led to less productive collaboration between teams and longer onboarding times for both internal and partner developers. The company's engineering leadership recognized the need for a solution that would address their API discoverability and exploration challenges while maintaining speed and ease of development.

Launchpad LA, one of the world’s top startup accelerators, was facing a significant challenge in managing their applicant tracking process. Initially, they relied on a spreadsheet to track their applicants, which proved to be inefficient and cumbersome, especially with non-numeric data filling the spreadsheet's rows and columns. The problem was exacerbated when the number of applicants increased to over 1,000 per class. The selection committee had to sift through more than 300 rows in a Google Spreadsheet to pare the pool down to eight startups. The process was not scalable and became 'very ugly, very quickly', according to Kyle Taylor, Launchpad’s former director of operations. The existing software solutions were targeted at large businesses with large budgets, which was not suitable for Launchpad LA. They needed a cost-effective solution that could handle a high volume of applications and streamline their review process.

BarkBox, a subscription service for dog treats, toys, and gifts, faced several operational challenges. The company, which also runs a charitable donations program, was struggling with the volume of donation requests from dog-related events and organizations across the United States and Canada. Each request had to be vetted and approved, a process that was causing a bottleneck. Additionally, managing in-office dog time, training requests, and remote work requests was proving to be a logistical challenge. The company needed to find a way to automate these processes without taking resources away from the development team.

Be Epic, Inc., a digital marketing agency based in Edmonton, faced the challenge of managing complex processes for their clients, including social media management, SEO, web design, and video production. A significant part of their work involved social media and advertising, where they had to generate and manage leads for their clients. They utilized Facebook Lead Ads for this purpose, but the process of managing multiple ad campaigns, targeting different audiences for each client, and transferring new leads between different apps was time-consuming and complicated. Without a proper process in place, they risked losing leads and wasting valuable time.

Cottage, a San Francisco-based startup, is redefining the process of design, permitting, and construction in residential projects, particularly accessory dwelling units (ADUs). The company's unique approach involves handling the entire development process, from initial design to acquiring planning permissions, and through to construction. However, coordinating this multi-stage process with various stakeholders, including designers and general contractors, posed a significant challenge. The company needed to remain lean and agile, which required efficient tools to manage the process. Additionally, Cottage faced the challenge of integrating various tools used by different teams, such as sales and marketing, with industry-specific software used for site feasibility, design, and construction coordination. The company needed a solution that would allow them to collect and manage data from these disparate tools without overburdening their tech development resources.

Designation, a UX/UI designer bootcamp based in Chicago, was facing a challenge in managing their admissions process. With a small team of less than 10 full-time staff, every person was vital to the day-to-day operations. The admissions team, which initially consisted of only one person, had to contact and schedule interviews with each applicant to qualify them for the bootcamp. This process was time-consuming and inefficient, especially when dealing with unqualified leads. The challenge was to find a way to automate the process, pre-qualify leads, and make the operations more efficient, even in the absence of the team.

Digioh, a platform that runs eCommerce quizzes for businesses worldwide, faced a significant challenge as it grew. The company needed to scale its operations, particularly in sales and customer onboarding, without expanding its lean team of 15 employees. The CEO, Rishi Shah, who also led the company's sales activities, found it increasingly difficult to manually email new prospects as the company's customer base expanded. The process of triaging new sales leads needed to be automated to keep up with the growing demand. Additionally, the company needed an efficient way to onboard new customers once a deal was closed. The challenge was to automate these processes without compromising the high-touch, personalized service that Digioh was known for.

Dollar Flight Club, a small and remote startup, was facing a challenge in managing its resources efficiently. The company, which was bootstrapped into existence, had a significant overlap of responsibilities, especially in the early stages. Engineers hired for specific tasks often found themselves creating custom reports or connections for the sales and marketing teams. As the engineering demands increased, it became increasingly difficult to allocate resources for one-off projects on other teams. The company needed a solution that would allow them to do more work and save time, without constantly relying on the engineers.