InMarket, an omnichannel marketing platform, was grappling with an inefficient legacy data platform that was unable to handle the growing volume of real-time location data collected from multiple sources. The platform, built using 50 AWS nodes and 400 bare metal nodes managed by Apache Mesos, was processing over 5 billion events daily. However, it was plagued with delays, bottlenecks, and inefficiencies. The platform's job success rate was a mere 40%, with 60% of Apache Spark jobs being randomly aborted in the system. This led to developmental delays, inaccurate timeline projections, and a significant reduction in InMarket's ability to attract marquee brands, thereby slowing down revenue growth. The time taken to hand off a data pipeline from data scientists to data engineers and then to operations for deployment in production was estimated to be up to twelve months, which was unacceptable given InMarket's business model.

Nitrio, an AI company specializing in sales optimization, was facing significant challenges with its Natural Language Processing (NLP) platform. The platform relied heavily on manual rules and heuristics-based models, which led to bottlenecks and scalability issues, hindering Nitrio's growth. The existing platform was unable to ensure the required level of accuracy for sentiment analysis of rep-to-lead messages, resulting in a significant number of messages being outsourced to a third party for manual analysis. This not only increased service costs but also created further bottlenecks and scalability issues. The platform's infrastructure demonstrated tight coupling between services, increasing their dependencies and negatively impacting team performance, causing data quality and consistency issues. Nitrio's platform was designed to efficiently analyze inbound rep-to-lead messages to extract their intent and collect useful data about every sales representative's performance. However, the reliance on manual processes and the inability to ensure 95% certainty in message intent identification were major setbacks.

Pr3vent, a Silicon Valley-based diagnostic company, was faced with the challenge of improving patient diagnosis and eye screening availability through computer-aided diagnosis. The company aimed to scale doctors’ expertise through AI, with the goal of reducing the per-screen cost for better accessibility to 4M infants in the US alone while increasing diagnosis accuracy. The challenge was to utilize the power of AI to combat preventable vision loss in infants. Due to the scarcity of trained doctors who can diagnose eye diseases by a newborn’s retina, the team’s vision was to marry Deep Learning and data to scale the expertise of ophthalmologists who can, to cut per-screen cost, increase accuracy, and improve screening availability. The solution needed to be highly accurate in detecting pathology in a newborn’s retina, to receive FDA approval. This required Pr3vent to accurately label a database of 350K fundus and retina images by a team of experienced ophthalmologists, build an AI-driven image analysis and anomaly detection engine, and develop an application for ophthalmologists to handle retina images.

Second Genome, a biotechnology company, was seeking to accelerate and scale its microbiome drug discovery and development. The company wanted to improve data ingestion and staging, and refine the codebase of its data platform. Operating in a highly regulated pharmaceutical industry, Second Genome needed to enhance data security compliance to create a safe drug research and development environment for its clients and partners. The company was also looking to handle microbiome data more efficiently to speed up microbial research, drug trials, and discovery. As part of the healthcare industry's transformation towards personalized medicine, Second Genome was aiming to identify responder/non-responder populations and determine the optimal approach to therapy. The challenge was to enhance its data platform to make it faster, more scalable, secure, and compliant.

AllTrails, a digital platform providing information on over 350,000 trails to 40 million users worldwide, faced a significant challenge in managing their data architecture. The company's analysts and engineers had developed a homegrown JSON schema library to define event collection, which was built into a custom SDK platform. However, maintaining this custom SDK generator for each platform was time-consuming and often led to delays in the collection of new analytics events. Furthermore, the GitHub repository used for this process was difficult for non-technical team members to navigate, making it challenging to ensure that it met their requirements. The company realized the need to move away from their homegrown SDK library and adopt a customer data platform (CDP) solution.

Canva, an online design and publishing platform, was facing a challenge in managing and utilizing its vast data. The company wanted to empower non-technical stakeholders with self-serve data to explore different areas as needed. They had a data warehouse, but the barriers to entry were too high for the average user. To grow Canva at scale, non-technical people needed to segment audiences and create funnels. It was difficult for product managers to dive into new releases and see how new features performed or get a breakdown of a funnel. Shortly after launch, the team realized the need for a more detailed product analytics solution.

Kahoot!, a platform for creating, sharing, and playing learning games or trivia quizzes, has experienced significant growth since its inception in 2012. With over 550,000 paying users and more than 1.5 billion participating players in 200 countries, the company faced the challenge of effectively managing and utilizing its vast product usage data. Despite having adopted Amplitude, a product intelligence platform, Kahoot! was only using a few functionalities and tracking minimal events. The company was suffering from a classic bottleneck where all data requests had to go through the data analysts. This situation was not sustainable given the company's growth and the increasing need for data-driven decision making across different departments.

UXPin, a code-based design tool company, was facing a significant challenge in consolidating and analyzing their subscription data. Initially, they used both PayLane and PayPal to process payments. However, when they moved to the United States, they were unable to migrate their Poland-based PayPal subscriber data. This was a significant issue as these customers constituted a large portion of their revenue. To address this, they decided to maintain their Polish PayPal account while also establishing a separate payment processor in the US. This decision led to the challenge of maintaining two separate payment processors and the need to consolidate and analyze data from both. UXPin built internal tools to analyze activity, but these tools required ongoing maintenance, often crashed, and did not provide the insights they needed. By October 2020, 99% of UXPin’s customers were paying via Stripe, and they needed a solution to efficiently analyze this data and support their growth.

Blue Corona, a digital marketing agency based in Maryland, specializes in helping home service companies improve their marketing performance and ROI. A significant part of their strategy involves providing clients with consistent NAP (Name, Address, Phone number) data, which is crucial for local businesses to attract local customers. They were using a popular citation provider for this purpose, which was effective but came with a significant cost issue. The team needed to migrate 155 locations from their existing citation tool, but they were concerned about the time it would take and the security of their client’s information.

Blue Corona, a digital marketing agency based in Maryland, was facing a significant challenge with their citation building process. The agency, which specializes in helping home service companies improve their marketing performance and ROI, was using a popular citation provider to provide clients with consistent NAP data. This was particularly important as their clients were local businesses needing to attract local customers. However, the cost of the citation service was prohibitively high. The high price point meant that Blue Corona was not consistently able to offer all their clients citation building services. This was in direct conflict with their goal of reducing marketing costs for their clients. The team at Blue Corona was therefore in search of a cheaper, yet equally effective alternative.

PuroClean, a leading franchise system for emergency property damage remediation, faced a significant challenge in managing their Local SEO initiatives across their 360 franchise locations in the USA and Canada. The marketing team struggled to get all franchise locations on the same page and bought into their structured, consistent Local SEO initiatives. Being a small team, they had to commit additional time to overcome this challenge, which led to other key functions of the marketing team being neglected. They identified the need to improve their understanding and management of NAP (Name, Address, Phone number) in relation to local business listings, but lacked the time to address this. The scale and scope of the challenge were largely unknown due to the novelty of Local SEO efforts to the business.

John Gilmore, the Sales Operations Manager at ClassHero, a startup, was tasked with identifying opportunities for product improvement and ensuring conversions were happening as expected. However, he noticed a significant drop in the onboarding rates on the site, from 75% to 39%, a 48% drop. This was a major concern as users who didn't complete onboarding in their first session rarely returned to the app. The drop in conversions was alarming, and the cause was unknown due to recent product changes. John needed to identify the problem quickly to prevent further loss in sales. He turned to Hotjar, a tool he had installed a few months earlier, to analyze session recordings and identify the issue.

Pluralsight, a technology workforce development company, was grappling with lower growth than anticipated due to issues surrounding rep productivity. Despite having access to data on win rates and pipeline coverage, the company struggled to identify the behaviors that led to consistent, predictable revenue. Two key challenges stood in the way of Pluralsight’s growth targets: the quality of CRM data and inconsistent execution from their reps. The data in Salesforce, their 'single source of truth', was often biased or incorrect due to human error in data input. As the company rapidly expanded its sales force, rep productivity declined, leading to longer ramp times, lower pipeline generation, and lower billings and ARR growth than expected. The company had several hypotheses for these challenges but lacked concrete sales engagement data to validate them.

Centriq Training, a leading institution in advancing IT careers under the Tek Ladder brand, was facing a significant challenge. Despite their top-tier status, they were struggling to reach out to live leads and aged inquiries due to a lack of staff and time. The inability to promptly respond to and qualify leads was a significant hurdle in their growth strategy. The situation was further complicated by the need to re-engage past student inquiries and schedule appointments for prospective students. The challenge was not only to manage the current live leads but also to rekindle interest among past inquiries and ensure a smooth appointment scheduling process.

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.