Data Lake / Data Warehouse Modernization · Marketing Personalization · Real-time Analytics
Clear.bio is transforming diabetes management by using Google AI to scale its personalized digital nutritional interventions through automated data prediction workflows, thus replacing inefficient manual processes. As Clear.bio 's impact grew across the Netherlands and expanded into Germany and France, they encountered a critical two-fold challenge that hindered their ability to scale effectively.
Reported impact
Clear.bio sees 30% lift in high-value partner conversion rate; Sales focus on top 20% practices led to ~20% less outreach time; AI model achieves 92-94% prediction accuracy; 69% of patients saw a clinically meaningful HbA1c drop.
To turn these challenges into opportunities, Clear.bio embarked on an AI Sprint with Google for Startups, integrating a powerful suite of Google AI tools into their core operations. They strategically leveraged Gemini to identify medical practices that fit their growth strategy, using the model to analyze crucial factors like technology adoption, patient demographics, and treatment patterns. This allowed Clear.bio to pinpoint practices with the highest potential for successful referrals. For the predictive heavy lifting, they used Gemini Enterprise Agent Platform to train and deploy a high-accuracy machine learning model, which makes the final predictions and delivers the 'Best_Class' score. Furthermore, BigQuery served as the robust data warehouse, loading, extracting, and preprocessing the extensive data used for training the AutoML model. To automate the entire pipeline, Google
stored or unified in BigQuery, orchestrated via Gemini Enterprise Agent Platform, and surfaced through Gemini Enterprise, Gemini, Cloud Run
69% of patients saw a clinically meaningful HbA1c drop
AI model achieves 92-94% prediction accuracy
Clear.bio sees 30% lift in high-value partner conversion rate
Sales focus on top 20% practices led to ~20% less outreach time
They strategically leveraged Gemini to identify medical practices that fit their growth strategy, using the model to analyze crucial factors like technology adoption, patient demographics, and treatment patterns.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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