At a fintech company, a fraud prevention AI application efficiently blocks potentially fraudulent transactions by using a highly performant in-memory database like Couchbase, which processes transaction data in under 50 milliseconds. Couchbase Capella is leveraged for real-time fraud detection and other high-speed use cases by fintech companies like Revolut, due to its Database-as-a-Service (DBaaS) capabilities that simplify AI application development. Key to this process is feature engineering, which involves transforming raw data into usable features for machine learning algorithms. Feature stores, both online and offline, streamline the management and access to ML features, significantly reducing the time developers spend on feature engineering. Couchbase supports this with Apache Spark for feature engineering and provides tools like Feast plugins and PySpark connectors, which enhance the efficiency of ML applications by enabling faster feature processing and model training. Capella also facilitates the training and inference pipelines, allowing for data synchronization between offline and online stores, ultimately supporting real-time applications like fraud detection with minimized latency.