The company needed an efficient and cost-effective method to generate high-quality labeled data. This was essential for training offline machine learning models, evaluating live predictions, and enabling subject matter experts to verify the results accurately..
By implementing Databrewery’s tools, combined with an SDK-based workflow, the company provided actuaries with structured visibility into model behavior. This helped them understand how predictions were being generated and assess their accuracy with greater transparency.
The team is now able to better organize and interpret their unstructured data, ensure consistency in training data quality, accelerate model iteration, and support the development of AI-powered benefit recommendation systems.
The company provides an AI-driven solution designed to help individuals understand and select the most suitable employer-sponsored benefit plans. After completing a brief survey, users receive plan recommendations tailored to their individual circumstances. These recommendations are powered by machine learning models that support decision-making through personalized suggestions, financial literacy content, and bundled options.
To ensure accuracy and relevance in the recommendations delivered, the company implemented an automated feedback system that connects its customer success team with benefit providers. This closed-loop framework enables continuous assessment and interpretability of predictions generated by the models.
A significant obstacle in developing high-performance models was the ability to produce sufficient labeled data required for both offline training and real-time prediction analysis, as well as review by actuaries & domain experts. The models draw from a variety of structured and unstructured data sources, including insurance policies, pharmaceutical records, geographic information, and financial wellness indicators. By building a centralized annotation pipeline using Databrewery, the machine learning team was able to manage the growing demand for high-quality training data. Labeled datasets, organized via a feature store, supported both development and evaluation use cases at scale.
The team further strengthened this process by adopting an SDK-enabled system that allowed actuaries to review and understand the model’s prediction behavior. With programmatic access to annotation activities and performance monitoring tools through Databrewery’s Python SDK, the team established a workflow where feedback from subject matter experts was incorporated efficiently. This helped drive improvements in model development and validation.
By embedding these workflows into their MLOps framework, the company now benefits from reproducible, collaborative pipelines that enhance overall efficiency. Leveraging Databrewery’s infrastructure, the machine learning team has accelerated training cycles through more structured labeling processes and improved cross-functional coordination. For new or previously unlabeled datasets, a built-in quality assurance protocol supports faster cataloging and integration of data from diverse sources. These efforts have helped maintain high data quality standards while enabling faster experimentation and deployment of AI-powered products.