Renato Goncalves, the founder of Aqqua, identified a significant challenge in the Brazilian boating industry. Boats are prohibitively expensive in Brazil, costing around 120 months of minimum wage, compared to 14 months in the U.S. This high cost makes boat ownership inaccessible for many Brazilians who wish to enjoy a day on the water. Renato, with his 13 years of experience in the boating industry, saw an opportunity to make boating more accessible by creating a platform where boat owners could list their boats for rent at affordable prices. However, Renato faced another challenge: he needed a platform that could support the creation of his app without requiring extensive coding knowledge.

Armadillo Rent was founded by Daniel González and his brother to help their father, a contractor in Mexico, rent out his unused construction equipment during off-seasons. The construction industry is seasonal, leading to periods of intense work followed by long periods of unemployment. This situation led to a lot of heavy equipment lying idle during the off-season. The challenge was to connect construction companies that own heavy equipment with those looking to rent it. Additionally, they needed a system that would allow equipment owners to keep track of their inventory while it was being rented out. The founders of Armadillo Rent had no coding experience or significant funds to develop a traditional app.

Ataeum, an innovative platform for entrepreneurs, was faced with the challenge of creating an all-in-one solution that would allow entrepreneurs to connect with co-founders, run startups in an automated, low-cost, and paperwork-free manner. The team wanted to create a platform that would minimize uncertainty and conflicts among co-founders, facilitate fair ownership based on the amount of work done, and manage a joint venture to enable entrepreneurs to run their startups in a lightweight and automated way. The challenge was to create a platform that would eliminate the need for entrepreneurs to worry about things like incorporation, bylaws, stock options, state qualifications, and bank accounts until they make revenues or decide to incorporate.

The founder of Avanti, Alex Fantappiè, identified a gap in the market where high school students, despite being capable of performing gig-type jobs, lacked opportunities to earn money. Additionally, he noticed that many large marketplace companies had lost the trust of their customers. The challenge was to create a safe, affordable marketplace that would connect high school students with job opportunities in their neighborhood, such as tutoring, dog walking, car washing, and technological help. The platform needed to be user-friendly, allowing customers to easily find and hire local high school students. Furthermore, the solution had to be developed quickly and efficiently, as Fantappiè was a college student with limited resources and time.

NASA’s Jet Propulsion Laboratory (JPL) is on a mission to find signs of life in our solar system, focusing on the presence of water, a vital element for life. The Ocean World Life Surveyor (OWLS) project at JPL is preparing to send a spacecraft to either Europa, a moon of Jupiter, or Enceladus, a moon of Saturn, where ice and water vapor have been discovered. The spacecraft will be equipped with microscopes to collect video data from water samples, looking for evidence of microbes. However, sending this microscopy data back to Earth is a complex and costly task due to the vast distance. Traditional compression methods are inadequate, and the energy cost of downlinking the data is extremely high. The Machine Learning Instrument Autonomy (MLIA) group at JPL faced the challenge of building a machine learning (ML) model that could identify videos most likely to contain signs of life, capture short clips, and prioritize them for downlinking back to Earth.

Sharper Shape, a company that creates technology for safe, efficient transmission and distribution solutions for utilities, was facing challenges in developing their machine learning (ML) models. The company uses computer vision models in advanced aerial sensor systems to power the automatic collection and analysis of unmanned aerial inspection data. A common use case for their technology is the identification of dangerous setups with electric wiring, such as vegetation growing too close, broken insulators, and more. However, training multiple computer vision models required a vast amount of accurately labeled images. Prior to using Labelbox, the Sharper Shape team relied on heavily manual workflows and experimented with open-source labeling tools that did not provide the required amount of configuration needed for their needs. Additionally, each data scientist had spent up to a third of their time on infrastructure and experiment management.

Advanced.farm, a company focused on automating agricultural tasks using robotics, was facing a challenge in refining its apple-picking capabilities. With numerous apple varieties and a short picking season, it was difficult to maintain pace. As they developed their computer vision machine learning (CVML) capabilities for apples, they needed a labeling solution that would allow them to regularly create new projects and receive a quick turnaround on labeled images. To succeed in their first apple-picking season, it was crucial for them to quickly process a large number of images through the annotation pipeline, adapt to the changing variety of apples, and ensure that their models were as accurate and efficient as possible on real data.

Ambi Robotics provides AI-powered robotic systems to customers, enabling them to scale their operations and handle increasing supply chain demand. The company's machine learning (ML) system is responsible for identifying an object and its location, and moving the robot hand to that location to grasp the object. The pick success rate, which is how often a robot successfully picks up an object, is the most important marker of success. However, Ambi Robotics faced a challenge in obtaining high-quality annotations for their data, which is crucial for improving their models. Initially, the company was managing the annotation process in-house, but this approach was not scalable for the amount of data they needed. When working with new clients and locations, Ambi Robotics would sometimes see lower pick-and-place success rates, simply because the environment looked different. The best way to improve performance was to mine data from the new location, annotate it, and then retrain their ML model. However, the company lacked the infrastructure to process this large quantity of data on a recurring basis.

Brex Inc., a financial service and technology company, was facing a significant challenge in automating its financial workflows. The company's goal was to provide an all-in-one finance solution for businesses, including features like Bill Pay, which allows businesses to manage and pay their bills in one place. However, much of the industry still relied on manual, error-prone workflows, particularly in document processing. Brex found that traditional OCR solutions were not reliable enough. The processed information from uploaded receipts or bills was often incorrect, requiring verification and re-typing. Even solutions that claimed to use machine learning were not achieving high enough accuracy and required substantial upfront work from the Brex team to set up templates. The challenge was to find a solution that offered high accuracy and low latency.

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.

Flexport, a technology platform for global logistics, was facing a significant challenge in processing logistics documents such as bills of lading, commercial invoices, and arrival notices. These documents, which are critical for clearing shipments past customs and establishing ownership of goods, were traditionally processed using template-based and error-prone OCR (optical character recognition) solutions or manual labor. This method was not only time-consuming but also prone to errors, leading to delays in cargo movements and slowing down internal operations. Flexport realized the need for a machine learning-based document processing solution that could automate the process and extract valuable information accurately in seconds. However, the challenge was to find a partner with deep expertise in AI and machine learning who could operationalize this solution without Flexport having to build out a team of machine learning engineers or data scientists.

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.

The Datta Lab at Harvard Medical School is engaged in studying the neural mechanisms associated with behavior in rodents. Their research involves recording the behavior of mice using cameras and measuring their neural activity using neural implants. The challenge lies in the analysis of this data, particularly in interpreting the behavioral data. This requires the researchers to label the poses of the mouse over time. While machine learning models can automate this process, a significant amount of video footage needs to be manually annotated first. This annotation process is time-consuming and detracts from the time that researchers could be spending on other aspects of research that require their expertise. The lab was in need of a solution to speed up their data annotation process.

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.

Kodiak Robotics, an autonomous technology company, is developing self-driving capabilities for the long-haul trucking industry. The company uses a unique sensor fusion system and a lightweight mapping solution to navigate highway driving and deliver freight efficiently. However, the company faced a significant challenge in training its software to handle rare scenarios, such as pedestrians walking on the highway. These edge cases are crucial for a production-level autonomous vehicle system, but collecting enough real-world examples to train the models reliably was proving difficult.

Nuro, a robotics company specializing in autonomous vehicles for delivery services, faced a significant challenge in identifying infrequent but meaningful scenarios in their training data. The company's autonomous vehicles, designed to deliver goods from produce to prescriptions, needed to be able to identify and respond to a variety of obstacles, including pedestrians in unusual postures, animals, occluded and backlit pedestrians, and infrequently encountered vehicles such as excavators. However, these labels were not present in the ground truth of their training data. The company's internal tool was only able to identify a limited number of these scenarios, falling short of the thousands of images that needed to be identified and labeled for comprehensive training of their autonomous vehicles.

OpenSea, the world's leading marketplace for non-fungible tokens (NFTs), was facing a significant challenge in detecting and mitigating copymints and fraud. Copymints are duplicates or imitations of popular NFTs, which can deceive users, especially those new to the world of NFTs. Trust and safety are crucial for welcoming new people into the Web3 ecosystem, and OpenSea was looking for a vendor to help advance their detection and removal capabilities. The team had already used rule-based systems to capture forms of deception, but it was a challenge to achieve the desired speed, recall, and precision needed to effectively address fraud in the marketplace.

Orchard Robotics, a company providing AI-first precision crop management solutions to farmers, faced a significant challenge in collecting and utilizing precision data across vast commercial orchards. The company developed tractor-mounted, AI-powered camera systems to collect precision data about every tree. However, the company needed to accurately count every fruit on every tree, a task that proved to be incredibly difficult and tedious, especially when the fruit was small. As a small team, Orchard Robotics struggled to scale these annotations in-house. They initially tried using three other major data-labeling services, but they could not achieve the consistent quality they needed. The quality varied dramatically between batches, and they could not provide feedback to the annotators on the quality of the labels. These platforms also did not offer ellipses as an annotation type, forcing Orchard Robotics to rely on bounding boxes, a less-than-ideal option when labeling spherical fruit.

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.

The TimberEye team faced a significant challenge in enhancing their mobile application's log scaling capabilities. The app, which uses computer vision and LiDAR mapping technology, was designed to help lumber suppliers and buyers categorize and scale logs faster, more safely, and with better accuracy. However, the team wanted to experiment with an instance segmentation model to further improve the app's scaling capabilities. The process of annotating images for segmentation proved to be a daunting task. TimberEye CEO and Founder Scott Gregg attempted to annotate a segmentation dataset on his own, but after three days and only 1,000 images labeled, he was burned out. The process was significantly more challenging and time-consuming than annotating images for object detection, requiring 100-200 mouse clicks per image instead of just 4. The team was overwhelmed and stuck, with only 5% of the dataset they needed to annotate complete.

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.

Vistapath, a pathology lab, was facing a significant challenge in the grossing process, a critical step in diagnosing diseases like cancer. Grossing involves assessing and documenting the physical characteristics of tissue samples, a process that is prone to human error and can lead to misdiagnoses. Vistapath aimed to reduce these errors by leveraging computer vision and artificial intelligence. However, they faced a problem in developing a robust tissue detection model. The model required hundreds to thousands of accurately annotated images, a task that required a tool that could be easily used by their histologists and experts. Initially, Vistapath used an open-source annotation tool, but it lacked automation and scalability. They then tried a tool with more automation, but it failed to meet their security and compliance requirements. Therefore, Vistapath needed a partner who could provide an annotation automation tool that could meet their strict security and compliance requirements.

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.

Yuka, a mobile application that provides health impact information for food products and cosmetics, faced a significant challenge in managing its rapidly growing database. The database, which already contained over 4 million products, was expanding at a rate of approximately 1,200 new products daily. Yuka's small team was unable to manually review each new product added to the platform, a process that often required multiple transcription tasks. The application initially used OCR to scan product images for nutritional information and ingredients, but this process was not always accurate. OCR struggled with images featuring inconsistent lighting, obstructions, or irregular text surfaces. As a result, about 60% of the images submitted to Yuka needed to be outsourced to a human annotator. This was a daunting task for Yuka's small team, especially considering their goal to provide a product's health score within 2-3 hours of its addition to the database.

A globally renowned consulting firm, with a history spanning over a century, was seeking to enhance its auditing capabilities by leveraging artificial intelligence. The firm's reputation hinged on its ability to conduct thorough audits, irrespective of their size, complexity, or location. The firm's experts were spending significant time manually reviewing various accounting, auditing, and industry information, a process that was both time-consuming and costly. The firm estimated that each auditor search lasted 10 minutes and cost $50-60 on average. The firm's data science team was tasked with streamlining news monitoring to anticipate changes in capital markets, regulatory trends, or technological innovation. They aimed to use custom NLP models to automatically analyze, categorize, and extract key client information from various sources. However, they faced challenges in labeling training data for the machine learning algorithms. It took three experts a week to label 500 training data points, and they found it nearly impossible to adapt to changes in data or business goals on the fly.

The Center for Security and Emerging Technology (CSET) at Georgetown University was faced with the challenge of building NLP applications to classify complex research documents. The goal was to surface scientific articles of analytic interest to inform data-driven policy recommendations. However, the team found that a large-scale manual labeling effort would be impractical. They initially experimented with the Snorkel Research Project, which allowed them to programmatically label 90K data points within weeks, achieving 77% precision. However, the collaboration between data scientists and subject-matter experts was time-consuming and inefficient, involving spreadsheets, Slack channels, and Python scripts. This workflow made improving data and model quality a slow process. The team was constrained by inefficient tooling to auto-label, gain visibility into data, and improve training data and model quality. The lack of an integrated feedback loop from model training and analysis to labeling also meant that data scientists and subject matter experts had to spend long cycles re-labeling data to match evolving business criteria. These challenges limited the team’s capacity to deliver production-grade models, shorten project timelines, and take on more projects.

A global custodial bank was facing a significant challenge in its Know Your Customer (KYC) process. Analysts and investment managers were spending over 10,000 hours annually reviewing and transcribing 10-Ks, which are critical for verifying a company’s identity, establishing a risk profile, and informing multiple business processes. The bank was processing over 10,000 documents each year, with each document taking 30-90 minutes to review. The process was further complicated by the fact that 10-Ks come in various formats, and if any information was missing or incorrect, analysts had to spend additional time hunting it down. This not only lengthened the customer onboarding process but also gave competitors an opportunity to swoop in. The bank had tried to solve the problem using a rule-based system, but it proved to be rigid and could only identify a narrow scope of information for certain document formats/layouts. The system also required frequent updates due to constant changes in regulations across several regions, which took months to implement.

Memorial Sloan Kettering Cancer Center (MSKCC), the world’s oldest and largest cancer center, was faced with the challenge of identifying patients as candidates for clinical trial studies by classifying the presence of a relevant protein, HER-2. The process of reviewing patient records for HER-2 was laborious and time-consuming as it required clinicians and researchers to sift through complex, variable patient data. The data science team at MSKCC wanted to use AI/ML to classify patient records based on the presence of HER-2, but the lack of labeled training data was a significant bottleneck. Labeling data, especially complex patient records, required clinician and researcher expertise and was prohibitively slow and expensive. Even when experts were able to manually annotate training data, their labels were at times inconsistent, limiting model performance potential.

Pixability, a data and technology company, provides advertisers with the ability to accurately target content and audiences on YouTube. However, with over 700 million hours of YouTube content being watched daily, Pixability faced the challenge of continuously and accurately categorizing billions of videos to ensure ads run on brand-suitable content. Their existing natural language processing (NLP) model for classifying videos was not performing strongly enough. The process of labeling training data for the machine learning solution was slow due to reliance on external data labeling services that required multiple iterations. Collaboration was constrained due to limited time domain experts and data scientists had to solve for ambiguous labels. Additionally, valuable information within titles, descriptions, content, and tags was difficult to normalize.

Schlumberger, a leading provider of technology and services for the energy industry, faced a significant challenge in extracting crucial information from a vast array of daily reports. These reports, ranging from daily drilling reports to well maintenance logs, each had their unique structure and format, making it difficult for Schlumberger’s team to quickly extract the necessary information. The team attempted to automate the information extraction using Named Entity Recognition (NER), but off-the-shelf ML models failed to identify the scientific terms related to the Exploration and Production (E&P) industry. Creating a domain-specific training dataset was time-consuming and not scalable, taking anywhere from 1-3 hours per document. The team needed to identify 18 different industry-specific entities and automatically associate data with these entities. However, the rich information was buried within tabular and raw text in PDFs with varied formatting across reports from different companies. There was also poor collaboration between domain experts and data scientists, with cumbersome file sharing and ad-hoc meetings.