uniopen is a digital communication and membership platform launched by Taiwan’s Uni-President Enterprises Group, connecting customers to ecommerce, membership benefits, and other retail experiences across web, tablet, and mobile channels. Across those channels, uniopen applies a moderation policy that classifies each interaction along two axes.
What happened
uniopen is a digital communication and membership platform launched by Taiwan’s Uni-President Enterprises Group, connecting customers to ecommerce, membership benefits, and other retail experiences across web, tablet, and mobile channels.
Where the sources line up
Across those channels, uniopen applies a moderation policy that classifies each interaction along two axes. The first is what behavior occurred (nine categories), and the second is what subject the behavior refers to (brand, other, or forbidden). Both must be correct for a moderation decision to be useful, and both are specific to uniopen’s business rather than something a general-purpose model can be expected to learn out of the box.
Practical impact for readers
In this post, we show how the team adapted Amazon Nova 2 Lite to these business-specific moderation policies through supervised fine-tuning in Amazon SageMaker AI and a final prompt-level output optimization. The AWS approach kept correction data, managed training, evaluation, and deployment controls in one repeatable workflow. Model availability varies by AWS Region. See Supported models by AWS Region in Amazon Bedrock .
Who should pay attention now
Across these channels, the same moderation taxonomy and release criteria help the team make consistent decisions as interaction formats and topics change.
What is still unclear
The architecture separates the production moderation path from correction, training, evaluation, and deployment. Amazon Nova 2 Lite handles the primary moderation requests. Amazon Nova 2 Pro supports candidate correction generation for reported errors, but a human reviewer must verify each correction before it can enter the training set. With this separation, the team can improve domain-specific behavior without treating generated labels as ground truth.
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