Coming soon.

Quality Scoring will allow AI Workflows to automatically evaluate the completeness and quality of product content using AI driven scoring models.

Instead of manually reviewing products one by one, workflows will be able to calculate quality scores for catalog data automatically.

This will help teams identify:

at scale.

Quality Scoring will analyze product data and assign quality indicators or scores based on configured evaluation logic.

Examples may include:

The scoring system will help prioritize optimization efforts across large catalogs.

Large product catalogs often contain products with varying levels of quality.

Some products may have:

while others may still contain:

Quality Scoring helps identify these differences automatically.

Quality Scoring workflows may be used for:

Example:

This creates a self improving catalog automation pipeline.

Planned scoring areas may include:

Capabilities may expand over time.

Quality Scoring will likely integrate with other workflow actions.

Example:

This creates automated optimization loops inside AI Workflows.

Quality scores may help teams:

This improves operational visibility for large catalogs.

Quality Scoring workflows may support:

This helps teams better understand why products receive certain scores.

Additional scoring capabilities may include:

Quality Scoring is currently under development and will become available in a future release of AI Workflows.