Seed intake decisions were made differently by every shift — and quality problems surfaced too late to avoid waste.

In their words

Seed intake decisions depended on manual review of test reports before any processing decision could be made. Decisions on whether to mix, process, or discard a batch varied across teams and shifts, creating inconsistency and compliance risk. Quality issues were sometimes caught too late, resulting in avoidable waste and rework that analysts could not prevent with the existing manual workflow.

One operating system around the risk pattern

• Built an AI agent that automatically ingests incoming seed test reports and analyses them at intake
• Created a decision engine that recommends the correct action — mix, process immediately, or discard — based on quality parameters
• Routed each recommendation to the relevant operations team as a clear, actionable instruction with supporting evidence
• Layered a workflow view so leadership can track batch-level decisions and quality trends over time
• Deployed a unified data warehouse for data lineage, governance, and role-specific reporting access