A UK retail AI company scaled image annotation for shelf and product-placement models while maintaining accuracy, workflow integration and cost control.
The challenge
Retail AI models depend on precise labels for shelf visibility and product-placement analysis.
The client needed to scale annotation rapidly while reducing cost and maintaining quality.
The approach
SBL trained a dedicated annotation team, integrated labeling workflows and applied quality checks across large image batches.
The process supported model-training cadence while giving the client a more predictable operating cost.
Train
Prepare annotators on retail image classes and model requirements.
Label
Process large image batches through controlled annotation workflows.
Audit
Apply quality reviews before model-ready delivery.
The result
The retail AI team moved most annotation work into a scalable delivery model with high accuracy and lower cost.
- Millions of retail images were labeled in less than five months.
- Accuracy stayed above the required threshold through review routines.
- The delivery team scaled to more than 80 trained annotators.
- The client reduced annotation operating cost while keeping roadmap velocity.