Normalized claim
High-value partner conversion rate increase: 30% increase
“achieved a significant 30% increase in the conversion rate of high-value healthcare groups interested in partnerships”
Clear.bio automates diabetes partnership targeting with Gemini and BigQuery
Use case typeMarketing analyticsUpdated Jun 13, 2026
Clear.bio used Google AI to scale personalized digital nutritional interventions by automating data prediction workflows and partner targeting. The company replaced manual identification of high-potential healthcare practices with an AI-driven predictive model and automated pipeline.
- Organization
- Clear.bio
- Industry
- Healthcare
- Location
- Netherlands
- Published
- June 2026
Reported outcomes
+30%
high-value partner conversion rate increaseRevenue & growth
−20%outreach time reduction92-94%model accuracy92-94%model accuracy upper bound
Strategic outcomes
Speed & agilityAutomated data prediction workflowScale & capacityImproved operational scalabilityCompetitive differentiationIncreased conversion of partnership prospectsCost efficiencyReduced outreach effort
Catalog median for revenue & growth deployments: +40% across 68 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Outreach time reduction: 20% decrease
“leading to an almost 20% reduction in time spent on outreach”
Normalized claim
Model accuracy: 92-94% increase
“boasting an impressive 92-94% accuracy in its predictions”
Normalized claim
Model accuracy upper bound: 92-94% increase
“boasting an impressive 92-94% accuracy in its predictions”
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Clear.bio
- Provider
- GCP
- Maturity
- Production
- Linked source
- Google Cloud Customer Story
The company needed to automate its end-to-end data prediction workflow to improve scalability and reduce operational costs
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Predictive Lead Scoring
- 2Sales Automation
- 3Workflow Automation
- Manual identification of high-potential healthcare practices was time-consuming and inefficient.
- The company needed to automate its end-to-end data prediction workflow to improve scalability and reduce operational costs.
- Clear.bio used Gemini to identify medical practices aligned with its growth strategy.
- Gemini Enterprise Agent Platform trained and deployed a machine-learning model that produced a Best_Class score.
- BigQuery handled data loading, extraction, and preprocessing for the AutoML model, and Cloud Run functions triggered the pipeline when new CSV files were uploaded.
- 30% increase in the conversion rate of high-value healthcare groups interested in partnerships.
- Almost 20% reduction in time spent on outreach by focusing on the top 20% of practices.
- Model predictions achieved 92-94% accuracy.
- The automated workflow improved operational scalability and replaced previously manual processes.
Architecture
AI Sprint with Google for Startups integrating Gemini, Gemini Enterprise Agent Platform, BigQuery, and Cloud Run functions. New CSV uploads trigger the pipeline; BigQuery supports loading, extraction, and preprocessing; the model emits a Best_Class score for practice prioritization.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 12, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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