Lix, a study platform for university students, was looking for a way to improve their platform's responsiveness and speed. They wanted to use containers to build a high-quality platform, but they faced challenges in running and managing data services like Elasticsearch, Cassandra, and Postgres in containers. Initially, they manually managed node labels for these services, which was time-consuming and prone to errors. Moreover, when a machine failed, they lacked the ability to temporarily move the Cassandra pod to another machine while the cluster recovered.

ReachForce, a company in the marketing automation space, was facing several challenges with their AWS data center. The data center, comprised of four environments (Dev, QA, Performance Lab, and Production), consisted of about 215 EC2 instances. The company had re-implemented their SaaS offering in a micro-services architecture, using a separate AWS instance for each microservice. However, this led to the need to maintain over 200 Linux OS instances, and their average CPU utilization was significantly below best industry practices. Additionally, their AWS EBS storage was severely under-utilized, with only 25% of purchased capacity being used. At the same time, EC2 instances were over-provisioned by 100%, increasing the cost of operating the ReachForce SaaS platform.

Aurea Software, the software engineering arm of ESW Capital, needed to create a single, multi-tenant Kubernetes platform capable of handling database workloads for 80 different SaaS companies. The company faced several challenges in achieving this goal. Traditional software-defined storage systems like Ceph and GlusterFS did not integrate well with Kubernetes or scale to the required levels. Furthermore, they were limited to only 40 EBS volumes per EC2 instance when using Amazon EBS for container storage, requiring them to overprovision VMs by 5x. The company also had to deal with the challenge of running their Kubernetes clusters multi-tenant, which meant that they had 80 different companies running on the same cluster, all simultaneously. This posed a significant challenge in isolating resources within the Kubernetes cluster, so that they do not interfere with each other.

Naitways, an IT service provider based in Paris, France, was facing challenges in scaling to meet customer demand, especially for the many stateful services they offer like WordPress, Drupal, Joomla, MySQL, & Redis. Most persistent storage options for containers didn’t provide both high performance storage for applications like MySQL, and multi-writer shared volumes for WordPress, both hard requirements for typical hosting customers. They needed a solution that could provide scalable, easy-to-use container-as-a-service offering and hosted web applications.

Inhibrx, a company that develops multivalent costimulatory agonists, checkpoint inhibitors, and therapeutics to invert the tumor microenvironment toward local immune activation, was facing several challenges. They did not have a formal tracking system, which resulted in plasmid maps being distributed across multiple scientists’ computers. This made it difficult to find the right plasmid map and often involved walking through the lab to find the right scientist. Additionally, spreadsheets were used to track requests and information about plasmids, which required extensive manual search and limited user compliance. Experimental notes were taken using a shared paper lab notebook, making it difficult to find experimental details and extract insights.

Adicet Bio, a company that employs gamma delta T-cells to target solid tumor cancer, faced several challenges in managing their data. Their data was scattered across paper notebooks, employee computers, and public drives, making it difficult to access and secure. Their previous data management platform struggled to provide varying levels of access permissions to data for the stratified teams across their various sites. As Adicet rapidly scaled their staff size and the complexity of their research and development process, they needed to be able to organize and standardize their data capture and record keeping.

Agenus, a global biopharmaceutical company, faced several challenges due to the lack of unified systems and a globally distributed R&D team. The absence of a centralized system made it difficult for scientists to find and share information effectively. The global distribution of the R&D team further impeded team collaboration. The complexity of R&D workflows exacerbated difficulties in tracking samples and experiments.

Zymergen, an industrial biotechnology company, was facing challenges with its custom software systems. The company had many siloed pieces of custom software, making it difficult for scientists to enter and extract data. The strain optimization and fermentation & production teams couldn’t share complete experimental context due to the disparate systems. Moreover, Zymergen’s systems couldn’t scale with their rapidly growing company. The company needed a solution that could integrate their custom tools and provide a central platform for data entry and extraction.

Arcturus Therapeutics, a leader in RNA medicines, faced several challenges in their research process. The lack of standardized molecular design tools hindered their ability to collaborate effectively. Their legacy Electronic Lab Notebook (ELN) was cumbersome and led to low usage among scientists. It took an average of 10 days for experimental data to be entered into the system, if at all. Additionally, the absence of a standard data store and sequence repository scattered data and prevented the capture of institutional knowledge.

Bolt Threads faced several challenges in their quest to develop new, high-quality biomaterials. Their project data was spread across more than ten systems, creating information bottlenecks and making it difficult to find and aggregate data from upstream experiments to inform downstream production processes. Their existing information management system was not flexible enough to adapt to Bolt’s ever-changing, materials-specific workflows. Legacy tools required time-intensive, manual data capture, and made collaboration logistically challenging – all of which slowed progress and hindered speed of innovation.

Rubius Therapeutics faced several challenges in their cell engineering processes. Their manual record-keeping systems were disorganized and cluttered, making it difficult for teams to collaborate effectively. The company's legacy request management protocols were inefficient, requiring scientists and managers to sift through scattered records on cell cultures and quality control assays. Furthermore, their engineering processes were out of sync and hard to track due to decentralized information storage systems and unreliable reporting on project progress.

Incyte’s antibody discovery group was starting from the ground-up and knew it would be pivotal to deploy a world-class informatics system as soon as possible. With a growing team and fluid processes, the antibody discovery group needed a flexible system, or else their data would be unreliable and difficult to track. Being able to work with external collaborators (including international collaborators) was a must.

Synlogic, a company developing microbe-based therapeutics, faced several challenges in their operations. Their complex workflows were being sketched out step-by-step on paper, which hindered collaboration and reproducibility. Without a workflow system linked to a registration system, it was difficult for Synlogic to trace the lineages of their candidates. Additionally, placing requests, uploading results from instruments, and collating data across experiments were cumbersome and unreliable without unified, intelligent systems.

Zymergen, an industrial biotechnology company, faced several challenges as it scaled rapidly. The scientists at the company were having difficulty collaborating and sharing information due to the lack of an efficient system. The growing R&D teams needed an informatics solution that could quickly adapt to organizational changes. Moreover, Zymergen has extensive custom software and instrumentation, so they needed a platform that could integrate easily with their internal tools.

Obsidian Therapeutics was facing several challenges with their previous Electronic Lab Notebook (ELN). The system was clunky and didn't allow for linkages to registered samples, which hindered adoption and experimental detail. Without a formal registration system, Obsidian scientists couldn't draw connections between results data and upstream entities. Their legacy sample tracking system was unintuitive and saw low scientist usage, leading to data loss and compliance concerns.

Syngenta, a global leader in agricultural science and technology, faced several challenges. One of the main challenges was balancing customization and standardization. Syngenta wanted standardized data for cross-company analysis, but each department had its own preferences and needs for recording and recalling data. Another challenge was collaboration across the globe. Handoffs between different geographies, languages, time zones, and areas of expertise while using paper records led to bottlenecks. Lastly, managing many regulatory requirements was a challenge. Different countries maintain complex, shifting regulatory requirements around product safety that all need to be met.

Cue Biopharma, a company that designs novel biologics to modify the immune system to fight diseases, was transitioning from a research-centric organization to a clinical-stage organization. This transition involved bringing early development activities in-house to achieve greater control over the types and quality of work done by their teams. However, they faced challenges with non-standardized data capture, barriers to communication and handoffs between teams, and the need for a digital infrastructure to support their growth. They were using shared spreadsheets to manage information, which posed risks such as unintended edits and duplicates. As the company tripled in size, it became increasingly difficult for key pieces of information to efficiently flow through the organization.

Anagenex, a drug discovery company, is combining DNA encoded library (DEL) technology with machine learning (ML) to accelerate the traditionally time- and labor-intensive portion of drug discovery. However, they faced several challenges. Treating DNA sequencing readouts as an intermediary step to understanding the molecule of interest is an indirect use case, creating an additional layer of complexity. Each experiment can introduce batch effects that mask the biologically relevant effects and confuse the machine learning process. Anagenex needed a robust solution that could start tracking from the very first experiments and scale quickly as the company grows.

Pairwise, a company that uses CRISPR and gene editing to develop new varieties of fruits and vegetables, faced several challenges. The nature of plant genetics meant that each plant had its own phenotypic and genotypic characteristics, requiring individual tracking. This resulted in sample sets often containing hundreds of entities, each with its own optimization needs and custom steps. This is significantly larger than a typical biopharma sample set. Additionally, plant-based workflows required more complex infrastructure, more team handoffs, and longer timelines compared to cell-based workflows. Pairwise needed a centralized, easily-accessible way to organize and communicate experiment data to every team, from discovery to development. Program leaders also needed to turn thousands of data points into insights to drive decision making. They needed pre-computed reports aggregating metrics for successful plants each week to move forward with.

Inzen Therapeutics, a biopharma startup, was facing challenges in integrating wet lab and informatics teams. The scientists were using different terminologies to describe the same processes and data, which was slowing down data integration and discovery. They were unable to track samples through the entire experiment lifecycle, which hindered their understanding of the cell of interest. As a small startup with limited resources, they needed a solution that was intuitive to use and easy to maintain.

Jounce Therapeutics, a pioneer of precision immunotherapies for cancer, was facing challenges in managing all the assay and antibody data from concept to IND across different teams. They were using multiple software platforms that did not communicate well with each other, leading to productivity loss as wet lab and informatics groups had to spend time transferring files and copying/pasting data across these platforms. The informatics group also had to spend valuable time maintaining and connecting these multiple software ‘solutions’. Critical data was stored in multiple software applications across different servers, complicating report generation, insight generation, and regulatory compliance. The friction of multiple logins and the need for specific training on how to best use each different software platform frustrated scientists and impacted productivity.

Enveda Biosciences, a company that uses a computational metabolomics platform to discover new chemicals for drug development, was facing challenges in organizing and scaling their foundational data. The company was generating a large amount of structure-activity relationship (SAR), biomarker, and mechanistic readout data that they could no longer manage with siloed data solutions. They needed a data platform that could automatically structure experimental data and feed it into their machine learning pipelines. The platform also needed to be intuitive and user-friendly, as well as capable of handling robust and iterative data models customized to Enveda’s use case.

uniQure, a leading pioneer in gene therapy, faced several challenges in their operations. As an international company with worksites in Amsterdam and Lexington, Massachusetts, they found it challenging to share data and collaborate across research sites. Their paper-based workflows made it difficult to track results and streamline experiment execution. A lack of standardized Notebook templates led to nonstandard experimental workflows and impeded quality control. These challenges were further exacerbated by the COVID-19 pandemic, which forced some uniQure scientists to work from home.

TurtleTree Labs, a biotechnology company with a mission to create lab-produced milk, faced challenges as it grew. The company found it difficult to track and optimize processes in the lab through spreadsheets and emails. Without a centralized system for experimental data, the executive team couldn’t easily get an overview of research progress, making it difficult to set new goals and timelines. Additionally, R&D teams distributed across Singapore and Pakistan needed an easy way to share progress and results.

Novozymes, a company focused on harnessing the power of enzymes and microbes to solve global challenges, was facing issues with its data management. The company's strain engineering teams were dealing with an increasing volume of data, from hundreds to thousands of samples, which required a more modern digital infrastructure for better connectivity and scalability. The company had many point solutions and regularly brought on new or custom software to address specific scientist needs. Over time, data about plasmids, strains, and assays were spread across multiple systems. Data scientists spent up to 90% of their time trying to reformat data to glean insights. Routine reports were hard to create and even harder to automate. Additionally, scientists had to hunt through Excel sheets, emails, and databases for past results, taking scientists away from science.

AC Immune, a clinical-stage biopharmaceutical company, was facing challenges in harmonizing data quality and standardization across the company. The R&D teams were using lab notebooks and spreadsheet-based software for data storage, analysis, and sharing, which was time-consuming. The company wanted to speed up crucial handoffs by offering the ability to easily search and interact with data across teams, experiments, and sites. Furthermore, AC Immune was looking for a systematized approach for capturing, sharing, and referencing data to boost data-driven decision-making for cross-functional projects.

Gilead, a global biopharmaceutical company, was facing challenges in managing data across its process development teams. The teams were using multiple systems and databases, managed through a mixture of email, Excel, and an on-premise electronic lab notebook (ELN), which resulted in data silos within and across teams. This made data capture challenging and required time-consuming manual handoffs between teams. Furthermore, aggregating data and conducting trend analysis was difficult and time-consuming, leading to less data-driven decision making.

The customer, a major mobile communications vendor, was faced with the challenge of designing, developing, and manufacturing a high volume of 5G-ready remote radio units (RRUs) for their customers. These RRUs needed to cover a variety of frequency bands and power levels. The emergence of 5G has led to an increase in demand for a wider variety of RRUs with different frequency and power level requirements. The customer needed a partner with strong design, new product development, test engineering, and global manufacturing capabilities as well as scale.

The cloud industry is experiencing significant growth, and keeping pace with infrastructure build-out demands is a major challenge. A leading cloud service provider was in need of a partner to design and manufacture high-quality motherboards on a massive scale. The size of the project left no room for error. Even the smallest errors could result in delayed deployment and increased total cost of ownership (TCO). The customer needed an attentive partner they could trust with their design-led manufacturing. They also needed to navigate unplanned obstacles such as sourcing issues and the impact of tariffs, and hit their cost and schedule targets.

In the automotive industry, style and energy efficiency are paramount. Automakers are increasingly using energy-efficient LED lighting in designs, from taillights to instrument panels. As more products become “smart”, the underlying technologies need to adapt to new shapes and offer powerful functionality in ever-smaller packages. The challenge was to optimize LED solutions for customers in the automotive industry by making the flexible circuit technology lighter, simplifying the design, and reducing costs.