Noitso, a company based in Copenhagen, Denmark, specializes in data science, data collection, and predictive analysis. They provide their customers with credit ratings, scorecards, and risk profiles using data science and AI. However, they faced challenges in deploying their models. The models took a long time to get to production and lacked explainability and monitoring. They were unable to determine when models needed to be retrained, and had to do it after a fixed period of time rather than when necessary. This approach was the only way to maintain accurate predictions and prevent issues such as data drift.

Exscientia plc is an AI-powered drug discovery organization that relies heavily on the accuracy and stability of its models. The company's model deployment process is unique as it is entirely automated, resulting in thousands of models being delivered, monitored, and retrained without human interaction. However, as Exscientia expanded its reach and goals, it needed an enterprise-grade scale solution. The team was looking for additional operational efficiencies and other ways to debug and stabilize models. The existing open-source deployment solution and inference platform were no longer sufficient for their growing needs.

Capital One, a leading US retail bank, was facing significant delays in their machine learning (ML) deployment pipeline. The data science teams were heavily reliant on the engineering department to test, deploy, or upgrade models. This resulted in month-long lag times and the need to redeploy entire applications for updates to existing models. Scaling up projects was only possible by using more developer resources and people power, which further strained the already overstretched teams. The bank needed a robust, scalable, and flexible approach to the deployment of ML models to support its millions of customers and users of their mobile banking app.

Covéa Insurance Plc, the UK underwriting business of leading French mutual insurance group Covéa, serves two million policyholders and generated over £725.7 million in premiums in 2020. The company is facing a significant challenge in the form of insurance fraud, which is costing the industry over £1bn a year. One of the most complex and hard-to-detect types of fraud they face is Ghost broking. This is when a policy is purchased by a middle person for a customer using false or stolen information to reduce the premiums. In the event of a claim, these policies would be legal and Covéa would have to pay out. As Covéa is mainly an underwriter, they often do not deal with the policy holder directly, so they had less data to work with to detect fraud. The call handling team were doing manual searches and checks on over two million new quotes per day. The scale was far too much to deal with in an efficient timeframe.