Trekka Logistics, a company specializing in servicing small businesses, was facing a challenge in providing the same level of service that an enterprise business would receive. The company was founded to cater to the ecommerce and distribution needs of small and emerging businesses, which often did not receive the necessary services from their previous fulfillment partners due to their relatively lower order volumes. Trekka Logistics aimed to offer a space for these companies to grow, but this required a warehouse management system (WMS) with a high degree of flexibility in their workflows. Automation was crucial for operating at scale, but the company also needed to be dynamic enough to provide a high level of customization to their customers.

Quality Distribution, a player in the highly competitive third-party logistics sector, was facing significant challenges that were hindering its growth potential. The company was relying on a 20-year-old, on-premises warehouse management system that was not only unreliable but also expensive to maintain. This outdated system was incompatible with the needs of modern customers, making it difficult for the company to stay competitive. Additionally, Quality Distribution was heavily dependent on manual processes to manage daily operations. These processes were rapidly becoming unsustainable, posing a threat to the company's ambitious growth targets.

Indigo, a Canadian retail company, faced a significant challenge in mid-2019 when it diversified its delivery network from a single nationwide carrier to an array of both national and regional options. This expansion from one to eight carriers meant that delivery data would now arrive from eight different sources, requiring substantial resources to track. Simultaneously, Indigo aimed to improve the customer experience surrounding order delivery, as research indicated that more than two-thirds of shoppers won't return to a retailer after a bad delivery experience. When they used only a single delivery carrier, Indigo received ‘after the fact’ reporting letting them know a package was delivered, but no indication when a package was stalled or delayed along the way. As Indigo expanded its network of parcel delivery carriers, it became increasingly challenging to get a timely, centralized view of delivery performance and issues across the country at any time, and difficult to establish procedures to proactively address them before a customer called to report an issue.

The online marketplace brand was struggling with providing efficient customer support due to the overwhelming growth of the ecommerce industry. The brand was only providing customer support through phone and email, with a poor turnaround time that lagged between 12-24 hours. This resulted in a low morale among the support team, high turnover, and a deteriorating customer experience that was driving shoppers away. The brand was in dire need of an automated solution that could provide instant, 24/7 support to customers at scale. The brand was seeking a solution that could decrease customer support response turnaround times, create meaningful interactions with customers to drive customer engagement, loyalty, and revenue, without increasing costs, and could be easily built and managed by non-technical CX teams.

IPSY, the world's largest beauty subscription brand, was founded on the merger of two leading beauty subscription companies, IPSY and BoxyCharm. However, the two companies were using different AI support solutions to manage product, subscription, and delivery inquiries. BoxyCharm, in particular, was struggling to meet the growing popularity of their beauty subscription services with a comprehensive customer experience (CX) strategy. They needed a solution that could provide customers with real-time support and resolutions on easy-to-answer brand interactions, create a seamless customer experience, and establish an efficient handoff to a live agent support when needed. Prior to implementing Ada, BoxyCharm's average first response time was around 29 hours, the total average resolution time was 53 hours, and the customer satisfaction (CSAT) score was 58%.

The client, a global company with over 3,000 employees, was facing a significant challenge in providing IT and HR support across different time zones and languages. The traditional approach of creating a ticket and waiting for an email response was proving inefficient, often taking days to resolve a single ticket. The IT and HR departments, consisting of only six and four full-time employees respectively, were overwhelmed with the growing number of support tickets and project lists, especially during organization-wide events that launched tens of thousands of tickets. The client needed a solution that could automate the employee experience, relieve the overworked IT and HR teams, and handle mass incidents at scale.

LiteBit, a leading cryptocurrency broker in Europe, faced a significant challenge during the cryptocurrency boom of 2017. As the user base of the platform grew exponentially, the volume of brand interactions doubled, putting immense pressure on their live support agents. Despite having introduced Zendesk ticketing, LiteBit was concerned about maintaining the personalized customer experience that was integral to its brand reputation. The agents were struggling to manage the high volume of tickets, and the support was limited to 9-5 service hours and available in only two languages. This situation threatened the brand's promise of a simple and human crypto trading experience.

Wave Financial, a company offering a suite of products and services for small business owners, faced a significant challenge in managing customer experience (CX). With over half of their staff dedicated to customer support, they experienced a massive spike in customer support volumes during the first quarter of the year, often increasing by 200-300%. In the early days, they managed this by pulling in employees from different departments and having agents work overtime, but this was unsustainable as the company grew. The demand for higher standards for both customer and employee experience became more urgent. They needed a solution that could reduce wait times, negative customer interactions, and missed revenue opportunities. They had two options: hire more support staff to handle the volumes during the busy season, which would require more investment in hiring and training, or find a more efficient, automated approach to their CX that provides customers with immediate and reliable access and resolution.

Tango Card, a B2B company that provides digital reward and incentive solutions, faced a significant challenge when the pandemic-induced digital transformation led to an unprecedented demand for its services. The company interacts with a variety of stakeholders, including business partners, reward recipients, and new prospects, each with unique needs. However, by the fourth quarter of 2020, Tango Card was grappling with a 54% increase in support case volume, particularly from reward recipients. This surge in demand threatened their ability to maintain their Service Level Agreements (SLAs) and deliver on their brand promise of providing a VIP digital experience to every stakeholder. The company needed an automation strategy that could introduce automated brand interactions across the customer journey, with personalized experiences for each persona, all within a single platform.

Technosetbee International, a leading manufacturer and supplier of advanced beehive technology, was facing a challenge in increasing awareness and sales of its unique, long-lasting synthetic beehives. Traditional wooden beehives typically have a lifespan of 2 to 3 years, but Technosetbee's modular, synthetic beehives, made with food-grade BPA-free plastic, last over 10 years. Despite this advantage, the company was struggling to reach potential new customers and boost its sales. The company wanted to not only increase awareness of its innovative product but also focus on list growth and future sales. The challenge was to introduce the product to new customers in a way that would stand out and engage them.

Summit Academy OIC, a tuition-free accredited vocational school in North Minneapolis, was seeking a cost-effective method to generate high-quality potential students and increase enrollments to its programs. The school had been running campaigns on various advertising channels including terrestrial radio, cable TV, digital TV, and Facebook Ads, with Facebook Ads being the most effective. However, these campaigns only led people to the website where only an average of 4% of visitors completed an application to attend an information session. The school wanted to increase this number and believed that more engaged and educated prospective students would be more likely to apply to info sessions and eventually enroll.

Customers.ai Inc, a chatbot marketing software platform, was facing a significant challenge in broadening its reach and decreasing the cost of lead acquisition. The company was using Facebook ads that directed users to their website, a strategy that was proving to be expensive and inefficient. The cost per click for these ads was between $3 to $5, and with a typical website traffic conversion rate of 2%, the cost per lead acquisition was a staggering $150 to $250 each. This high cost was a significant barrier to the company's goal of increasing awareness of its Facebook ad tools and bot marketing platform among potential new users.

Car Loans Canada, a leading online automotive financing company, was facing the challenge of generating high-quality loan applicants in a cost-efficient manner. The company was seeking an effective ad objective that would not only raise brand awareness but also reduce the cost to acquire leads. The traditional method of acquiring leads through website forms was not yielding the desired results, with conversion rates hovering between 5-8%. The company was also grappling with a high cost per applicant (CPA) of $45 on average. The challenge was to find a solution that would streamline the application process, increase conversion rates, and reduce the CPA.

Lincoln Davies Building Supply, a family-owned business for over 145 years, was facing the challenge of offloading customer support for their small brick-and-mortar business. The company, which has evolved into one of the finest building supply companies in Central New York, was looking for ways to incentivize customer loyalty. They wanted to make their website work harder for the business by helping customers and potential customers find the products they need faster and support sales goals. The company was also looking for a solution to reduce the time spent on answering frequently asked questions.

Sharkey’s Cuts For Kids, a franchised hair salon in Odessa, TX, aimed to break the corporate grand opening record of 78 services without spending a fortune on ads. The salon, managed by a husband and wife team, wanted to maximize ad results while reducing the time spent in customer support. The challenge was to generate high-quality, cost-efficient salon bookings for the grand opening. The goal was to shatter previous franchise records and launch the largest Grand Opening in the most cost-efficient way. The challenge was to create a system that would not only attract potential clients but also assist them in booking their appointments for the Grand Opening Day.

Playtika, a leading Game-Entertainment company, faced significant challenges in scaling the production of real-time machine learning. With over 10 million daily active users, 10 billion daily events, and over 9TB of daily processed data, the company's existing batch and web services deployment methods were unable to scale to meet their needs or produce predictions in real-time. The REST APIs in their ML Pipelines led to service exhaustion, client starvation, handling failures and retries, and performance tuning of bulk size for batch partitioning. Playtika’s event-driven ecosystem required a solution that could support real-time streaming of their production models and scale without downtime. They also needed a solution that could integrate with various processes including Airflow and Spark, and handle bursts, peaks, and fast creation of new ML Pipelines.

Seagate Technology, a global leader in data storage and management solutions, faced significant challenges in deploying a defect detection system across their global manufacturing facilities. The system had the potential to improve ROI by 300%, significantly reducing time processing defects and at a much lower cost. However, Seagate's legacy workflows made it difficult to deploy their model at scale. The team experienced low efficiency at many stages of the workflow due to manual tasks that prolonged the workflow, causing bottlenecks within the pipeline. Seagate was also experiencing low server utilization of their hybrid cloud infrastructure, as they had to run each workload separately, and did not have a mechanism in place to run different workloads on optimal machines. The team required an infrastructure to automate the pipeline components, such that the resources will be scheduled automatically, in real-time with maximum efficiency. At the production level, Seagate required advanced deployments that could serve on TensorFlow and Kafka endpoints.

University of the Pacific, a leading institution in higher education, was facing challenges in contacting live leads, particularly outside of traditional business hours. The university was struggling to maintain a high level of engagement with prospective students, which was crucial for their admission process. The lack of after-hours coverage was a significant issue, as it was leading to missed opportunities to connect with potential students. The university needed a solution that could not only increase their response rate to student inquiries but also schedule virtual appointments for prospective graduate students with the admissions team.

Express Flooring, a leading flooring company, was grappling with several challenges that were hindering its growth and ability to meet customer needs. The primary issue was connecting with customers who were interested in flooring. The company was struggling to establish effective communication channels that could facilitate seamless interactions with potential clients. Additionally, the scalability of their call center operations was another significant concern. The process of hiring and training new staff to handle the increasing volume of calls was proving to be a daunting task. Lastly, the company was finding it difficult to reach a broad audience through internet forms. This was limiting their market reach and potential for growth.

Continuum Industries, a company in the infrastructure industry, developed a product called Optioneer to automate and optimize the design of linear infrastructure assets. However, operating the Optioneer engine presented several challenges. The engine needed to be robust enough to handle different problems fed by different customers. Moreover, the company needed to ensure that the solutions provided by the engine were accurate and agreed upon by civil engineers. The team also had to constantly improve the optimization engine without breaking the algorithm. The nature of the problem they were trying to solve presented additional challenges. They could not automatically tell whether an algorithm output was correct or not. They needed a set of example problems that was representative of the kind of problem that the algorithm would be asked to solve in production. The team initially developed a custom solution to these problems, but it proved to be extremely clunky and complex to maintain.

Shotgun, a global live entertainment solution, was seeking to integrate AI into their platform to enhance their services. However, they faced several challenges. Firstly, they had limited experience in AI and needed an efficient, flexible, and intuitive AI platform that would enable their small team of engineers to deliver AI quickly. Secondly, they found most platforms to be fragmented and constrained to only using the compatible computing vendors or tools. Their first AI project was to implement a recommender system that would take user history from various sources to offer advanced recommendations for events based on user’s event and music tastes. The system also required quick recommendations with a cache solution to give users real-time and relevant recommendations. As Shotgun embarked on their AI journey, they needed a platform that was flexible and scalable, so that their team could quickly build and support new AI innovations as they grew.

Wargaming, an award-winning online game developer and publisher, faced a significant challenge in scaling their AI across business units. With over 110 million players worldwide, 15+ game titles, and almost 2PB of data, the company had 1,500+ models running in production on a single server solution. This severely limited infrastructure and constrained data scientists, as the overhead cost of adding servers was extremely high due to per core licensing. The existing platform also limited data scientists to a language and packages pre-approved by the platform. Wargaming needed a solution that could support large-scale models in production, scale their servers, minimize overhead costs, and provide flexibility for their data scientists.

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.

The cloud revolution is in full force, with AI being a major driver of new cloud adoption. IDC estimates that fast-growing AI workloads will account for up to 50% of the total cloud market by 2025. However, this progress comes at a significant cost to the environment. Information and Communications Technologies (ICT) already account for an estimated 9% of total global electricity use, a figure that could more than double by 2030. AI’s portion of electricity consumption is growing much higher than other technologies. Deep Learning models and the data sets they train upon are growing at an extraordinary rate. In less than 5 years, the leading language model will have increased in size by over 100,000x. The competitive challenge presented by hyperscale CSP AI development platforms (Azure Machine Learning, AWS Sagemaker, GCP AI) required an immediate response. Neu.ro provided a market proven platform with unique advantages.

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.

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.

Monday.com, a work operating system (Work OS) that allows organizations to manage every aspect of their work, faced significant challenges in implementing machine learning (ML) solutions. The company's data team, BigBrain, was responsible for the data and analytics platform and ML initiatives. However, as demand for ML solutions grew, the data scientists found themselves heavily reliant on engineers to bring models to production. This resulted in a high time to value, with models often waiting for deployment until a developer was available to set up the infrastructure. Furthermore, the data scientists were siloed and had a disconnected workflow between where the model was trained, deployed, and monitored, creating unnecessary complexity. Key pain points included excessively high time to value due to production bottlenecks, dependency on developers and engineers for deployment, missing critical MLOps capabilities, inability to consolidate distinct endpoints into a multi-model endpoint pattern, and disjointed workflow due to each data scientist working with different machine learning tools.

Altis, an innovative consumer AI startup, aimed to revolutionize the home fitness industry by offering personalized fitness training using AI vision technology. The proposed AI system needed to accurately track and analyze movements, identify a wide range of exercises, including those using popular gym equipment, weights, machines, and detect errors in form. The system also needed to support multiple cameras and operate in real-time. To control costs and maximize asset utilization, Altis wanted to implement pipelines and MLOps on their existing on-premise GPU servers, while retaining the ability to scale globally on the cloud of their choice. The challenge was to develop a solution that was suitable for both hardware and cloud-based inference, and could scale globally without being tied to a single cloud provider.

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.