Vision
Scaling Agricultural Data With Smarter Matching
Enable AGDATA to unlock operational efficiency and scale across their complex, customized data pipelines by deploying advanced AI to automate core data harmonization tasks, while preserving the trust, accuracy, and data governance their Fortune 500 agriculture clients expect.
Challenge
Solving One of Agriculture’s Most Complex Data Problems
AGDATA is a $70M agricultural data firm serving North America’s largest manufacturers. It collects and cleans data across the entire agricultural supply chain, from manufacturers to distributors, retailers, and growers, delivering actionable insights that power targeted sales, marketing, and rebate programs.
AGDATA built a network of tailored data pipelines to meet diverse client needs, but the resulting complexity made scaling and automation challenging.
One of the toughest challenges was matching incoming transactions to the right grower records, which meant sorting through hundreds of thousands of entries with inconsistent formats, missing fields, and complex business rules. AGDATA saw AI as a solution, but needed a clear strategic roadmap and a trusted approach to prioritize where to begin.
Breakthrough
Turning Messy Transactions Into a Scalable AI Pipeline
KUNGFU.AI partnered with AGDATA to assess and prioritize dozens of AI use cases, ultimately focusing on a high-impact opportunity: automating the grower-matching process. Solving this at scale required a custom AI solution that could handle inconsistent inputs, enforce strict data access rules, and deliver high-accuracy clustering across a massive dataset.
KUNGFU.AI built a solution with four core components:
- Embeddings and Model Architecture
Trained a transformer model using triplet loss to generate embeddings from combined fields such as farm names, business owners, addresses, and contact information. This enabled the model to identify and cluster transactions belonging to the same grower even when the details varied across records.
- Tenant-Aware Matching
Integrated data permission constraints to ensure transactions were matched only to grower records a client was authorized to access, maintaining strict compliance with access rules.
- Confidence-Based Automation
Developed a tiered confidence scoring system using embedding distances. High-confidence matches are fully automated, medium-confidence results are routed for human review, and low-confidence outputs are flagged for manual processing.
- Retraining and Deployment
Delivered a production-ready solution integrated into AGDATA’s core data pipeline, along with retraining logic and infrastructure guidance to support ongoing improvements.
Outcome
An Intelligent, Scalable Matching System That Powers Growth
Automating the grower-matching process helped AGDATA eliminate a major operational bottleneck and unlock a more scalable, efficient data pipeline. It also demonstrated how a focused, well-executed AI initiative can lay the foundation for broader transformation.
- The model automated 87% of grower assignments with high confidence, significantly reducing manual labeling and freeing up valuable internal resources.
- Accuracy reached up to 95% on a real-world test set, validating that AI could meet the precision requirements of AGTATA’s high-stakes data workflows.
- Embedding the system into AGDATA’s core data pipeline improved scalability across clients, allowing them to onboard new manufacturers more efficiently and with less customization.
- The project established a repeatable framework—from use case selection to deployment—that positioned AGDATA to pursue future AI projects with greater speed and confidence.
By automating one of its most labor-intensive challenges with a smart, scalable solution, AGDATA is now positioned to deliver more value to clients and accelerate AI adoption across the business.
