Case Study: Leading CPG Company reduces ML model scaling time by 85% with Sigmoid

Centralized AI deployment environment reduced time to scale ML models by 85%

Leading CPG Company, a producer of health, hygiene, and nutrition products, had nearly 300 machine learning models spread across R&D, marketing, supply chain, sales, HR, and finance. These models were developed and run in silos across geographies, creating duplication of effort, limited visibility, and difficulty scaling models globally. The company turned to Sigmoid to help centralize and industrialize its ML model deployment process.

Sigmoid built a centralized AI deployment environment that let multiple business teams configure, run, share, and download ML model outputs from one portal without writing code from scratch. Integrated with Google Cloud, Airflow, Firestore, Kubernetes, and Azure Active Directory for SSO, the platform streamlined production deployment and reporting. Sigmoid’s solution reduced the time to scale ML models by 80–85%, cut time to move models to production by 70–80%, and lowered model-running costs by 2X.