Blue Bottle Coffee (BBC), a global coffee roaster and retailer, was facing significant challenges with its IT infrastructure. The infrastructure was insecure and distributed among several cloud providers, lacking a DevOps approach. This situation increased the Total Cost of Ownership (TCO) and slowed down the time to market for their products. BBC aimed to assemble and optimize the disjointed IT-infrastructure elements to increase the security of the entire ecosystem. They also wanted to implement Continuous Integration/Continuous Deployment (CI/CD) pipelines to accelerate and facilitate the deployment process by eliminating manual operations. Operational inefficiencies were causing high TCO and slow Time to Market (TTM), keeping BBC’s engineering team occupied with non-strategic tasks. BBC approached Provectus to prepare their IT-infrastructure for AWS migration, optimize and enhance its deployment process, and make BBC’s entire ecosystem more secure, which would allow the company to further spur its expansion, both in the USA and abroad.

Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals.

Appen, a leading provider of high-quality training data for AI systems, was facing a significant challenge in scaling its fraud detection mechanism. The company was using a partially automated but mostly manual system to detect and prevent malicious activity on their platform. This system, which relied on SQL and Python scripts, was not efficient enough to handle the increasing volume of work. Appen was struggling to monitor more than 50 jobs per day manually and considered hiring 20+ data analysts to keep up with the platform’s growth. The company needed a solution that would allow them to scale their fraud detection, increase the efficiency of their crowd workers, and attract new enterprise clients. The existing system also posed a challenge in terms of data quality, as it was prone to human error and could not efficiently eliminate low-quality contributions.

The Customer, a pioneer in Autonomous Systems, was faced with the challenge of migrating its computer vision cloud platform to the Amazon cloud within a four-month timeframe. The migration was necessary to enable the platform to perform highly scalable, real-time weapon detection to identify firearms and suspects in high-security environments. The goal was to provide security and safety to essential businesses, communities, and schools through real-time human behavior recognition and weapon detection technologies, enabled by AI & Machine Learning. The Customer was also looking to protect communities by bringing AI-driven visual imaging and human behavior recognition technology to every school, public building, and business across the country. They wanted to develop a weapon detection solution that they could integrate with their apps in the AWS cloud, to be able to deter, detect, and defend against shooters quickly and efficiently.

Forge Global, Inc., a leading fintech company, was facing challenges in managing and processing large volumes of complex corporate documents. The company's existing manual and semi-automated document processing pipelines were unsustainable due to their lack of speed, cost-efficiency, and scalability. The task of processing clients’ corporate documentation could not be outsourced due to security and compliance concerns. The documents often contained sensitive information and any data extraction process had to comply with privacy laws and regulations. The company's leaders recognized the need for a more advanced and automation-focused solution to expedite the processing of incorporation documents, automatically extract data from them, and generate incorporation reports. They sought to partner with an AI solutions provider and technology consultancy to design and build the desired platform, while maintaining the security and compliance of its document- and data-centric operations.

Audiobooks.com, a leading audiobook publishing company, faced a significant challenge in improving the Cost-per-Circulation (CPC) for digital content and audiobook titles on their platform. The company aimed to predict customer churn using AI to drive more value for their clients and solidify their bottom line. As a subscription-based business, customer retention was crucial for Audiobooks.com. The company operates in a competitive market where customers can easily switch to alternative providers. Any negative experience could prompt a customer to abandon the brand, resulting in a loss of revenue, affecting customer lifetime value (LTV), and increasing the cost of acquiring new customers. The company's goal was to minimize the churn rate to lower CPC, which is calculated as the total cost of their licensing fees vs. the total number of book checkouts. High customer churn negatively impacts book checkouts and makes it difficult for distributors to get government funding. The executive team set a bold goal to track the habits of problem users and proactively make personalized offers to minimize attrition by predicting churn while increasing checkouts.

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.

Marmon Holdings, a global industrial organization with over 100 autonomous manufacturing and service businesses, was facing a challenge in optimizing its manufacturing appliances at the McKenzie Valve Plant. The company sought to integrate a Computer Vision (CV) solution to analyze and enhance the use of its machines. The primary goal was to gain more visibility into the run versus idle time of the machines, thereby eliminating production bottlenecks and improving return on investment (ROI). The challenge was to achieve this while ensuring low implementation costs.

TripActions, a corporate travel management organization, faced a significant challenge in enabling secure banking transactions without the need for third-party services. The company aimed to accept customer payments directly, track all banking transactions processed through the platform, and securely collect and store critical and client-sensitive data. To achieve these objectives and spur revenue growth by attracting new enterprise clients, TripActions needed to migrate its platform to a secure PCI-DSS-compliant infrastructure. However, the existing infrastructure had several network, user access, monitoring, alerting, and CI/CD issues that needed to be addressed. The company approached Provectus to upgrade their infrastructure as part of their preparation for PCI-DSS compliance certification.

Earth.com, a leading internet platform for environmental enthusiasts, aimed to accelerate the development and delivery of EarthSnap, an AI-powered image identification application. The goal was to modernize and automate the application’s machine learning (ML) infrastructure, simplify the deployment of new models, and reduce administrative costs. The company insisted on following best practices for end-to-end ML, DevOps, and app development. However, Earth.com lacked an in-house ML engineering team, which made it challenging to add new datasets, improve existing models, release new ones, and scale the ML solution. The models delivered by their previous partner were satisfactory in terms of accuracy but required manual sequential execution for data processing and model retraining. The deployment of endpoints also had to be done manually. Earth.com sought a new strategic partner to streamline the delivery of EarthSnap to market, and Provectus, an AWS Premier Consulting Partner, was chosen for the role.

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.

FireworkTV, a decentralized short video network, was facing challenges with its existing machine learning (ML) infrastructure. The ML team recognized the limitations of their current system, which included lagging productivity, growing overhead costs, and a lack of automation. These issues were hindering the performance, quality, and reliability of their video recommendation model. The model, which is crucial for engaging users and driving ad revenue, needed to deliver highly accurate and real-time recommendations based on user-video interactions and specific content features. However, the existing infrastructure, based on Lambda and PyTorch, was not only expensive but also cumbersome, limiting the project's potential to scale and grow. The team sought to build a new, more efficient infrastructure on AWS to drive improvements.

Appen, a leading provider of high-quality training data for AI systems, was facing challenges with its manual ticketing system. The system was inefficient, time-consuming, and prone to errors, particularly in ticket categorization. The Contributor Success team, consisting of only two members, was overwhelmed with over 11,000 tickets per month, leaving them with only seconds to resolve each issue. The manual categorization often resulted in miscategorized tickets, leading to delays in response times and dissatisfied customers. Appen needed a solution to automate its ticketing system, improve support efficiency, reduce ticket handling time, and decrease contributor churn.

Lane Health, a healthcare lending company, was facing challenges with its HSA Advance applications. The applications were developed on a no-code platform, which limited their flexibility and posed ownership and maintenance challenges. The applications lacked a proper versioning system or database rollback mechanisms, making each new product release a risky endeavor. They also lacked tools for testing, logging, monitoring, alerting, and database migration and management. Lane Health wanted to improve the HSA Advance applications by making them more secure, scalable, flexible, reliable, and cost-efficient. They were looking for ways to quickly introduce a more advanced technology stack and infrastructure, and to migrate the applications to the AWS cloud. The team hoped to gain capabilities to innovate faster while achieving more stable releases, reducing the Total Cost of Ownership, and achieving HIPAA compliance to store PHI data. The product had to be launched in less than four months, requiring a reliable partner to get it all done.

GoCheck Kids (GCK), a comprehensive photoscreening and visual acuity app, was seeking to enhance the image classification component of its pediatric photoscreening application through machine learning. The cloud-based system was data-rich, and it was hypothesized that machine learning could improve the app's usability by supplementing image analysis and improving user actions to capture the best image possible. GCK needed a robust and resilient machine learning infrastructure to run experiments on a dataset of over one million images faster and more cost-efficiently, to train a highly accurate image classification model for the application. Additionally, GCK wanted to alert the user in real-time to retake the image when the image was captured while the child was not looking directly at the camera. This required a machine learning infrastructure to prepare data, run experiments on the dataset, and build and train new image analysis and classification algorithms faster and more efficiently.

IMVU, the world’s largest avatar-based social network, was facing challenges with its aging on-premise data platform. The company wanted to enhance and re-architect their platform to support advanced analytics and Machine Learning use cases. However, with an exponentially growing data volume and a monolithic Hadoop architecture, the IMVU team was struggling to efficiently utilize user-generated data. The existing infrastructure limited innovation and capacity for advanced analytics. IMVU’s analysts lacked the tools to rapidly generate business-critical reports on customer in-game behavior at scale. They were working with historical data in batches, which resulted in late reports, inaccurate assumptions about customer in-game purchases, slower sales, and loss of profit. The analytics team also lacked a test environment to efficiently check analytics assumptions. The platform was powered by a 90-node on-premise Hadoop cluster, which was not cost-efficient and resulted in high costs and low efficiency.

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, leading to delays, bottlenecks, and inefficiencies. The platform's performance was subpar, with a job success rate of only 40%, and 60% of Apache Spark jobs were randomly aborted in the system. This inefficiency led to developmental delays, inaccurate timeline projections, and a slow handoff process from data scientists to data engineers and operations. These issues were detrimental to InMarket's ability to attract marquee brands and slowed down revenue growth.

LeadGenius, a marketing automation and demand generation company, was facing significant challenges with its data processing pipeline. The pipeline was inefficient due to a high amount of manual processes, including data incorporation and verification. This inefficiency led to bottlenecks, slowing down data delivery to customers. The data, parsed from various sources, had to be verified carefully, which when done manually, further slowed down the process. The pipeline was also lacking in terms of data quality and data consistency due to the variety of data sources and the reliance on manual processing. The company needed a solution that was not only automated but also fault-tolerant and scalable, capable of running on-demand in case of any issues with its components.

The client, a global healthcare leader based in the United States, was looking to accelerate and scale the adoption of AI/ML across its organization. The company generates substantial amounts of data from various sources, including customer interactions, sales transactions, social media activity, and product usage. However, without a robust Machine Learning Operations (MLOps) platform, it was a challenge for the organization to effectively scale and manage their AI/ML workflows. This resulted in inefficiencies, increased costs, and slower time-to-market for new products. The client’s data scientists and ML engineers were looking for ways to simplify the deployment of AI/ML into production environments, particularly when using MLOps practices and the Amazon SageMaker suite of services. The client was transitioning from legacy infrastructure, but its engineers could not access and discover the unified and integrated workloads quickly and efficiently enough to meet the company’s vision for AI transformation.

PSC Biotech, a global life sciences consultancy, was facing challenges with its document processing operations. The company was looking to enhance its operations by automating its existing pipeline with AI, specifically to process FDA Form 483 observations faster, more accurately, and on a larger scale. The company's existing processes were manual, leading to high processing costs, risks of errors due to human factors, and a throughput rate that depended entirely on the number of employees. The accuracy of document processing was also low and fluctuated significantly over time. Given the sensitive nature of the HCLS business operations, any process that is slow, inefficient, and prone to errors poses a huge risk. PSC Biotech handles thousands of FDA Form 483 observations per year, and the need to automate its document processing pipeline was long overdue. The company expected to decrease time spent on manual review of observations, decrease costs of form processing, mitigate risks of infractions made by mappers and reviewers, increase throughput rate, and increase the accuracy of document processing by adopting AI and implementing an ML-powered Intelligent Document Processing (IDP) solution.

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.

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.

Swiftmile, a universal charging platform for micromobility, was preparing for its next phase of global expansion. However, the company's growth potential was limited by its existing platform's monolithic infrastructure and backend logic. The platform could not support more than 600 stations while handling about 30 messages per second, causing substantial time losses when processing transactions beyond this limit. This major bottleneck limited the platform’s performance, scalability, and cost-efficiency. Swiftmile needed a robust, highly scalable, cloud-based platform that could maximize scaling potential, simplify the integration of new communication streams, and prepare for worldwide expansion while keeping down infrastructure costs. Additionally, Swiftmile sought to make their platform data-driven, with the ability to collect, process, and analyze streaming data in real time to improve user experience and explore data business opportunities.

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.

Nugs.net, a music streaming and live recording platform, aimed to enhance its user experience by delivering a superior platform for music fans who enjoy live performances. The company wanted to increase user retention, conversion rates, and profitability by improving UX personalization. The challenge was to incorporate AI recommendations to improve content discoverability, listening diversity, catalog observability, and the scope of artist following. Nugs.net also faced growth seasonality, with users typically sticking to newly performed and released concerts and recordings. This was highly dependent on artists’ schedules, which could be disrupted by various factors, making for an unpredictable growth strategy. Another challenge was the user tendency to stick to one or two favorite artists or genres, making Nugs.net even more dependent on growth seasonality for retention.

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.

Nexant, a globally recognized software, consulting, and services company, was facing challenges with its on-premises IT infrastructure. As part of a multiyear turnaround effort, the company sought to transform its IT infrastructure and migrate its operations to the cloud. The goal was to enable faster iterations, assess, deploy, and support customer applications more efficiently, speed up development and release cycles, eliminate unnecessary heavy lifting, and scale its entire system for faster growth. However, deploying new resources and maintaining existing ones was time-consuming, slowing down the development and delivery of applications, and causing operational inefficiencies. To overcome these challenges, Nexant partnered with Provectus, an AWS Premier Consulting Partner, to reinvent its IT infrastructure and migrate it to the AWS cloud.

VTS, a commercial real estate leasing and asset management software and data company, aimed to become the decision-making platform for the commercial real estate industry. To achieve this, they wanted to efficiently productionize Machine Learning (ML) models and build new models iteratively using AWS services. Their goal was to accelerate the time to market for ML applications, reduce human errors, and lessen the effort from their Data Science (DS) team. One of the ML models they prototyped was designed to predict leasing deal outcomes. However, they faced challenges in integrating this predictive model into the core user experience. While their data scientists were capable of delivering the model in ad hoc environments, they found it difficult to deploy the model in production using the existing infrastructure of the VTS platform. In essence, VTS had excellent data scientists but lacked the necessary AWS and MLOps expertise to complete the task.

TripActions, a corporate travel management organization, was facing a significant challenge with its existing infrastructure and data storage solution. The increasing volume of historical indexed data was straining the company’s infrastructure and primary storage solution, slowing down its performance and causing an ever-increasing cost of ownership. The historical data was never cleaned while analytical data was stored across various databases in different formats, creating multiple data silos and making data unavailable for analytics and machine learning. The company’s existing data solution was failing in terms of analytic capacity and scalability, which increased operational costs, slowed down onboarding of new clients, and stifled business growth. The initial architecture and data solution were based on Amazon Elasticsearch, which proved to be inefficient and expensive when data volumes increased. Data was schemaless, and there was no mechanism to join data from different databases. Partial data in Amazon S3 was stored in JSON format and synced with one-day lag, with no partitioning, which delayed TripActions’ reaction to issues or changes in data.

Lumin Digital, a fintech company specializing in digital banking solutions, was seeking to build a modern, multi-tenant data platform to support advanced analytics, reporting, and enable various AI/ML use cases. The initial use cases identified were focused on improving abilities to monitor and detect risk involving user sessions and ACH transactions. The company's mission was to help financial organizations preserve and nurture their relationships with customers in today's evolving market. To meet their ambitious goals, Lumin needed a trusted partner to design and build the desired data platform, implement AI/ML use cases, and ensure that the platform was optimized for stable, cost-efficient performance.