AI Workflows allow teams to review generated output before synchronization or publication.
Reviewing AI generated content is an important part of maintaining:
catalog quality
SEO consistency
translation accuracy
marketplace compliance
product data reliability
The moderation and review interface provides visibility into how AI generated its results and allows teams to approve or reject outputs before synchronization.
Why reviewing output matters
AI can automate large parts of enrichment and translation workflows, but generated output should still be reviewed when:
content is customer facing
SEO quality is important
translations require localization review
supplier data is inconsistent
workflows process sensitive catalog data
Reviewing output helps prevent incorrect or low quality content from being synchronized automatically.
Where reviews happen
Generated output can be reviewed directly inside workflow actions.
Examples:
Attribute Extraction results
Content Enrichment output
Translation results
Category Mapping suggestions
Results are typically available inside the Results tab of the action.
What reviewers can see
The review interface may display:
generated values
generated content
AI reasoning
confidence scores
synchronization options
moderation states
This helps reviewers understand both the result and the AI decision making process.
Reviewing generated attributes
For Attribute Extraction workflows, reviewers can inspect:
extracted attribute values
source product information
AI confidence
reasoning behind extracted data
Example:
Generated values:
Flavor → Salmon
Lifecycle → Adult
Reviewers can validate whether the extracted data matches the original product content.
Reviewing generated content
For Content Enrichment workflows, reviewers can inspect:
generated descriptions
shopping titles
SEO content
formatting
tone of voice
keyword usage
This helps maintain consistent catalog quality.
Reviewing translations
For Translation workflows, reviewers can inspect:
grammar
localization quality
terminology consistency
formatting
ecommerce tone of voice
This is especially important for multilingual storefronts and marketplaces.
AI reasoning
Certain workflow actions provide AI reasoning.
Reasoning explains why the AI generated specific output.
Example:
"The description references salmon based cat food for adult cats, therefore the AI selected Flavor as Salmon and Lifecycle as Adult."
Reasoning improves transparency and helps reviewers evaluate the reliability of generated output.
Confidence scoring
Generated results may also contain confidence scores.
Confidence scores indicate how certain the AI is about the generated output.
Higher confidence often means:
stronger source data
clearer product information
more reliable output
Lower confidence may indicate:
ambiguous descriptions
incomplete data
uncertain extraction logic
Confidence scores help prioritize moderation efforts.
Approving output
If the generated output is correct, reviewers can approve the result.
Approved output can then:
continue through the workflow
synchronize automatically
update product data
Approval helps maintain workflow throughput while still applying quality control.
Declining output
If generated output is incorrect or unsuitable, reviewers can decline the result.
Examples:
incorrect attributes
poor translations
invalid categorization
low quality descriptions
formatting issues
Declined output will not synchronize.
Synchronizing approved output
Once approved, generated data can synchronize back into the connected platform.
Examples:
Magento
webshop product catalogs
marketplace feeds
Synchronization updates the product data using the approved workflow output.
Reviewing large workflows
Large workflows may generate many review tasks.
Examples:
supplier catalog imports
multilingual translation workflows
marketplace enrichment projects
Efficient review strategies become important for maintaining operational speed.
Best practices for reviewing AI output
Prioritize low confidence results
Low confidence outputs are more likely to require manual review.
Focus moderation effort on:
uncertain outputs
complex products
inconsistent supplier data
multilingual edge cases
This improves moderation efficiency.
Review prompts regularly
Repeated moderation issues often indicate:
weak prompts
unclear instructions
insufficient source data
Improving prompts reduces future review workload.
Use category specific workflows
Different product categories often require different review expectations.
Examples:
Fashion workflows
Electronics workflows
Pet food workflows
Category focused workflows improve both AI quality and moderation efficiency.
Moderate customer facing content carefully
High visibility content should usually receive stronger review processes.
Examples:
SEO descriptions
shopping titles
marketplace content
translated storefront content
Example review flow
Example:
A webshop generates German Google Shopping descriptions.
Workflow:
Trigger selects products missing German content
Content Enrichment generates optimized descriptions
Translation converts content to German
Reviewers inspect:
grammar
SEO quality
terminology
confidence scores
Approved content synchronizes to Magento
This creates a controlled multilingual enrichment pipeline.
Why reviewing output is important
Reviewing AI generated output allows businesses to:
maintain content quality
reduce catalog errors
improve SEO consistency
control synchronization
scale AI safely
The review process creates a balance between automation speed and human quality control.