Case Study: Laurel achieves faster ML pipeline development with Astronomer, Inc.'s Astro

Laurel’s Timekeeping Transformation with AI and Airflow

Laurel, a company focused on improving trust and accuracy in hourly timekeeping, was struggling with time-consuming manual data extraction and ETL work that slowed its machine learning efforts. To support its AI-driven timekeeping platform, Laurel turned to Astronomer, Inc. and Astro with Airflow to help its data team spend less time on operations and more time building pipelines.

Astronomer, Inc. implemented managed Airflow on Astro to orchestrate Laurel’s data pipelines, from data ingestion and model retraining to testing, validation, and deployment. The result was faster ML development, reduced manual work, better scalability as Laurel onboarded more customers, and measurable gains such as improved “time to time” and development velocity; the company also reported that a compliance model made the process twice as efficient.

Laurel

Andy Ward

VP, Product and Data