Keeping product listings polished across an entire catalog is one of those tasks that’s easy to put off. Titles, descriptions, categories, tags, variation details — there’s always something that could be tighter, but it’s hard to know where to start or where your time is best spent. Today we’re releasing the first public beta of WooCommerce AI Product Advisor, a plugin that reviews your catalog, identifies the products with the most room to grow, and suggests targeted improvements you can apply in one click.
This plugin is experimental. We’re releasing it early to learn from real-world usage. Your feedback will directly shape what it becomes.
About AI Product Advisor
AI Product Advisor adds a dedicated screen to your WooCommerce admin. After a short onboarding flow where the plugin analyzes your existing store content, it builds a tone profile so that suggestions match your brand’s voice rather than sounding like generic AI copy. From there, the plugin generates field-level suggestions across titles, descriptions, short descriptions, categories, tags, and variation details.
The plugin gives you three main views to work with:
- Overview – a dashboard with pending suggestions, your approval rate, and weekly usage. Products where you’ve already applied changes are listed with a local lift/dip indicator showing whether orders moved after the update.
- Suggestions – a ranked queue of pending improvements. Click into any product for a side-by-side diff with inline editing.
- History – a full audit log. Every change you’ve accepted can be reverted.
Getting started
- Download the plugin ZIP from GitHub.
- In your WordPress admin, go to Plugins → Add New → Upload Plugin and upload the ZIP.
- Activate the plugin and open Product Advisor from the admin menu.
- Complete the onboarding flow to connect your store and set up your brand tone.
How to Use:
Where to find more information
- GitHub repository – source code
- GitHub Issues – report bugs or request features
- Comments under this post – we’ll be watching them as well.
Your testing shapes what comes next. If something works well, we want to hear about it. If something doesn’t, we especially want to hear about it.
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