Bayer's Model Store Copilot Plugin Saves Researchers Months by Surfacing Existing Predictive Models
Bayer· GermanyMicrosoft Copilot for Microsoft 365 · Microsoft Teams · Power Apps
Bayer's Crop Science division, working with Microsoft Teams engineering, built the Model Store, a Microsoft Copilot plugin using Teams extensibility and PowerApps to search Bayer's scientific knowledge repository with natural language, helping data scientists and laboratory researchers quickly locate existing studies and predictive models instead of digging through decades of unstructured slide decks and reports, preventing duplicate model development and saving a researcher two to three months of work.
Overview
Bayer's Crop Science division, working with Microsoft Teams engineering, built the Model Store, a Microsoft Copilot plugin using Teams extensibility and PowerApps to search Bayer's scientific knowledge repository with natural language, helping data scientists and laboratory researchers quickly locate existing studies and predictive models instead of digging through decades of unstructured slide decks and reports, preventing duplicate model development and saving a researcher two to three months of work.
This entry has 15 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Every day, new data is added to the decades of existing research at Bayer's Crop Science division, creating difficulties for teams using unstructured data sources recorded in slide decks or reports. Teams faced obstacles identifying information in studies, finding existing models, and knowing who was conducting what research because of the overwhelming volume of information.
The solution
Working with Microsoft Teams engineering, Bayer developed the Model Store, a Copilot plugin that leverages extensibility provided by the Teams platform to search for information using natural language, with PowerApps embedded in Teams as the interface to the scientific data stored in Bayer's knowledge repository. Researchers and data scientists use a few key phrases to quickly locate studies and predictive models, and the Model Store also identifies who is responsible for the research, making it easier to connect with the expert source.
Reported business value
By using Copilot to investigate the Model Store, a researcher in the United States identified a predictive model already developed by a researcher in Germany, preventing a duplicate model from being developed and saving two to three months of work. Previously, it could take days, if not weeks, to find the information; Microsoft Copilot immediately extracts the relevant models for a certain task and connects researchers to colleagues in Microsoft Teams.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other life sciences entries in the register.
Novo Nordisk accelerates clinical documentation and drug development with Claude
Novo Nordisk, a global pharmaceutical company headquartered in Europe, built NovoScribe, an AI-powered documentation platform using Claude Code, Amazon Bedrock and MongoDB Atlas, to automate clinical study report (CSR) generation. Claude helped cut writing times on CSRs by 90%, reducing time spent producing clinical study documentation from 10+ weeks to 10 minutes, and delivered a 95% reduction in resources needed to create device verification protocols. The platform expanded from clinical trial reports to device protocol documentation and patient materials, and an 11-person development team, including non-technical staff, now prototypes features using Claude Code.
Novo NordiskClaude Code · Amazon Bedrock · MongoDB AtlasProtecting Crops Today to Feed Billions Tomorrow
Syngenta built Gaia, the first crop protection R&D data platform for analytics and innovation, on Databricks to unify siloed data across 70-plus countries. The platform cut data access times from as long as nine months to minutes, reduced time to value by 70%, decreased engineering costs by 50%, and now publishes over 200 data products handling more than 2 million queries per month.
SyngentaDatabricks · Delta Lake · Unity Catalog +3
Novo Nordisk uses machine learning and computer vision to automate pharmaceutical manufacturing quality checks
Novo Nordisk, a Danish pharmaceutical company that supplies nearly 50 percent of the world's insulin, built an automated ML pipeline on AWS using Amazon SageMaker Pipelines to train, deploy, and monitor computer vision models on edge devices. The system automates previously manual tasks including counting drug cartridges in boxes and detecting bacterial anomalies on agar plates, using a robotic arm, camera rig, and edge inference, with results monitored via an Amazon QuickSight dashboard. The solution deploys ML models at scale to different edge devices, automates quality-assurance tasks, improves time to market, and was successfully repurposed from the cartridge-counting use case to the agar-plate anomaly detection use case with minimal changes.
Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation
Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.
NovartisAmazon Web Services (AWS) · Databricks · Amazon S3 +8
Was this helpful?
Your feedback helps us improve our use case database