Bayer Crop Science Brings Generative AI into Discovery — Accelerating Data Integration and Hypothesis Generation

The Problem: "The Self You Don't Know You Have" in Research Data

A major agrochem firm accumulates decades of research data across multiple labs, multiple systems, multiple formats. When a researcher asks "has anyone tested this hypothesis before?", the search infrastructure is fragmented and slow.

The Solution: A Researcher Hub on Vertex AI

Bayer Crop Science built the following on Google Cloud:

• **Cross-corpus search**: Historic experiment data, publications, and internal knowledge searchable in one query.
• **Hypothesis generation**: Gemini synthesizes related knowledge to propose hypothesis candidates from a researcher's question.
• **Literature summarization**: Recent publications filtered to the researcher's specialty.
• **Experiment planning**: AI proposes appropriate experimental designs; the researcher selects.

Outcomes

• **Compressed exploration phase for researchers**
• **Fewer redundant experiments**—prior similar work surfaces immediately
• **Cross-domain synthesis**—hypotheses across chemistry, biology, data science
• **Faster onboarding for junior researchers**

Design Choices That Made Production Stick

1. Respecting the Researcher's Trust Boundary

Researchers don't take AI output on faith. Bayer always surfaces sources—which paper, which experiment—so the researcher can use the output as judgment input, not as a conclusion.

2. Access Control on Sensitive Data

Research data carries regulatory sensitivity. Bayer used Vertex AI's permission tooling to enforce role- and project-based data access controls.

3. Compliance at the Design Table from Day One

Life sciences is heavily regulated. Bayer pulled compliance, regulatory affairs, and legal into the design conversation at the PoC stage—avoiding the late-stage "back to the drawing board" pattern.