How Corteva Built an Enterprise AI Chatbot for Agriculture Science
Plant operators at Corteva Agriscience were drowning in hundreds of pages of unstructured documents - until they implemented Sprout, an AI chatbot that provides instant answers with citations. Discover how this Azure-powered solution reduced repetitive questions by 70% while improving document accuracy.
The Document Crisis in Agriculture Science
At Corteva Agriscience - one of the world's largest seed production companies - plant operators faced a daily struggle. Critical operating procedures, safety manuals, and quality standards were scattered across hundreds of pages in SharePoint sites, local drives, and email attachments. Employees wasted hours searching for information they needed to properly grow, test, and distribute genetically modified seeds.
"We had plant operators emailing the same questions over and over because they couldn't find answers in our documentation," explains Mehul Bhuva, the Azure Developer Influencer who led Sprout's development. "One team in Europe had a 150-page wiki document that nobody was reading."
The tipping point: When a 25-year veteran left with only 15 days notice, his replacement - coming from a different department - had no way to quickly absorb decades of institutional knowledge locked in unstructured documents.
How Sprout Solved the Knowledge Access Problem
Sprout was designed as more than just a chatbot - it's a framework for unifying enterprise knowledge. The solution:
- Profile-based access: Different departments get customized interfaces (Seeds Advisor, Plant Advisor, Licensing Advisor) showing only relevant questions and documents
- Citation system: Every response includes the exact document and page number reference, building trust in the answers
- Human-in-the-loop validation: Subject matter experts review responses during onboarding to refine prompt engineering templates
- Self-service data management: Business teams maintain their own document repositories in Azure Blob Storage
At the 4:32 mark in the video, Bhuva demonstrates how plant operators can ask technical questions like "How do I dry seeds at Facility X?" and get precise answers with citations - eliminating hours of manual document searching.
Technical Architecture Behind Sprout
Sprout runs on a Microsoft reference architecture combining:
- Frontend: Blazor web app with Entra ID SSO integration
- AI Services: Azure AI Foundry with GPT-4.5 (now upgraded to 5.2)
- Search: Azure AI Search for vector indexing documents
- Storage: Cosmos DB for chat history and Power BI analytics
Key innovation: The team can add new profiles in half a day by modifying JSON configuration files - no code changes required. This enabled rapid expansion from one initial use case to five production profiles within months.
Implementation Journey
The project took approximately six months from conception to company-wide rollout:
| Phase | Duration | Key Activities |
|---|---|---|
| Infrastructure Setup | 3 months | Standing up Azure AI Foundry, security permissions, and CI/CD pipelines |
| Development | 1.5 months | Customizing Microsoft's accelerator template for Corteva's needs |
| Testing/Validation | 1 month | Stakeholder feedback sessions refining prompt engineering |
| Beta Rollout | 2 months | Gradual expansion from one to five use cases based on demand |
A critical success factor was engaging stakeholders early - having business teams own their profile's data maintenance and validation created strong buy-in across the organization.
Security and Access Controls
Sprout implements enterprise-grade security measures:
- Entra Group-based access: Users only see profiles they're authorized to access
- Prompt engineering safeguards: Prevents disclosure of sensitive information or execution of dangerous commands
- Usage analytics: Power BI reports help monitor potential misuse patterns
The team conducted "stress testing" where they tried to trick Sprout into revealing passwords or running destructive commands. These attempts helped refine the prompt templates to block malicious queries while maintaining helpful responses.
Multilingual Capabilities
One unexpected benefit emerged when a Brazilian stakeholder started querying Sprout in Portuguese:
Breakthrough: Despite documents being in English, Sprout could answer questions in any language - a game-changer for Corteva's global operations where employees previously had to manually translate materials.
This multilingual support proved particularly valuable for:
- Latin American plant operators
- European marketing teams
- APAC quality control specialists
Accuracy improved from 82% to 96% as the team upgraded from GPT-4 to 4.5 and eventually 5.2 models.
Future Roadmap
Corteva plans several enhancements:
- Databricks integration: Connecting structured data from lakehouses to complement unstructured document analysis
- Agentic capabilities: Expanding chart generation features and adding strategic advisory functions
- Embedded experiences: Bringing Sprout into Teams and other productivity tools to increase adoption
- Knowledge repository: Vectorizing 10,000+ SOPs and quality documents for comprehensive coverage
The team is particularly excited about using Sprout to analyze past project costs and predict future budgets - demonstrating how AI can move beyond simple Q&A to strategic decision support.
Watch the Full Tutorial
See Sprout in action at the 4:32 mark where Mehul Bhuva demonstrates how plant operators query the system for technical procedures.
Key Takeaways
Sprout demonstrates how enterprises can transform document access with AI:
In summary: By combining Azure AI Foundry with careful prompt engineering and stakeholder ownership, Corteva created a chatbot that reduced repetitive questions by 70% while improving document accuracy to 96% - all while maintaining enterprise-grade security and multilingual support.
Frequently Asked Questions
Common questions about enterprise AI chatbots
Sprout solves critical document access challenges for plant operators who previously had to dig through hundreds of pages of unstructured SOPs, safety procedures, and quality manuals scattered across SharePoint and local drives.
The chatbot provides instant answers with citations to source documents, reducing email chains and repetitive questions by 70% according to Corteva's internal metrics.
Sprout can answer questions in any language while grounding responses in English documents.
This was particularly valuable for Corteva's Latin American and European teams who previously had to manually translate English documents using third-party tools.
Sprout implements enterprise-grade security with Entra ID SSO and profile-based access controls.
Each profile is secured by Entra groups, preventing unauthorized access. The team also implemented prompt engineering safeguards to prevent the system from revealing sensitive information or executing dangerous commands.
Sprout provides citations with every response, showing the exact document and page number referenced.
Early versions had some hallucinations, but accuracy improved from 82% to 96% after moving to GPT-4.5 and implementing human-in-the-loop validation processes.
Sprout runs on Azure AI Foundry with Blazor frontend, Azure AI Search for vector indexing, Cosmos DB for chat history, and Power BI for analytics.
The architecture supports adding new profiles in half a day without code changes by modifying JSON configuration files.
Sprout provides a unified platform for Corteva-specific knowledge without requiring individual Copilot licenses.
It offers deeper customization for agricultural science use cases and handles proprietary document formats that generic Copilot implementations can't process effectively.
Corteva plans to integrate Sprout with Databricks for structured data analysis, expand agentic capabilities for chart generation, and connect to Fabric lakehouses.
They're also working on embedding Sprout directly into Teams and other productivity tools to increase adoption.
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