🤖 Your Listing Changed Overnight. Amazon Didn’t Ask.
You wake up. Check your dashboard. Something’s off.
Your title is different. The bullets you spent weeks testing? Rewritten. Your product images? Replaced with AI-generated versions.
No notification. No approval. No warning.
This isn’t a bug. This is Amazon Project Starfish — and if you don’t understand what it is, you risk gradually losing control of your catalog.
What Is Project Starfish?
According to internal documents leaked to Business Insider, Amazon’s newest AI initiative is designed to become “the ultimate source of product information for all products worldwide.”
Here’s what it’s doing right now:
- Scraping 200,000 brand websites for product data
- Auto-rewriting titles, bullets, and descriptions across millions of ASINs
- Generating product images and videos from existing content
- Filling missing information by pulling data from across the web
- A/B testing AI-enhanced vs. standard listings without telling you
Amazon projects Starfish will add $7.5 billion in GMV in 2025 by improving conversions through better product data.
Translation: Amazon is tired of sellers writing bad copy and leaving fields blank. So they built an AI to fix it—whether you want them to or not.
Why This Matters (And Why Most Sellers Are in Trouble)
Here’s the uncomfortable truth:
If Amazon’s AI can’t find complete, accurate data from your sources, it will make decisions based on whatever it can scrape elsewhere.
That can mean:
- Your competitor’s description might end up in your listing
- Specs from a knockoff site could overwrite yours
- AI-generated images that don’t match your brand
- Generic bullet points that kill your conversion rate
The worst part? You won’t know it happened until your sales drop.
The Opportunity (If You Move Fast)
But here’s what most sellers miss:
This isn’t just a threat. It’s a massive opportunity.
Sellers who understand Starfish can:
- Save time on listing optimization
- Improve conversions with better product data
- Scale more consistently across marketplaces
- Stay ahead of competitors who leave data gaps
The difference between winning and losing:
Control what data Amazon’s AI consumes, or let it consume whatever it finds.
How Starfish Actually Works
Amazon’s AI follows a four-step process:
1. Data Collection (The Scraping Phase)
Starfish crawls:
- Your brand website
- Manufacturer sites
- Distributor websites
- Competitor listings
- YouTube videos
- Review sites
2. Data Synthesis (Making Sense of Conflicts)
When it finds different information across sources, AI:
- Prioritizes Brand Registry content
- Cross-references multiple sources
- Fills gaps where data is missing
- Corrects errors and inconsistencies
3. Content Generation (The Rewrite)
Using large language models, Starfish:
- Rewrites titles for conversion
- Generates natural language bullets
- Creates product descriptions
- Auto-generates images with clean backgrounds
- Produces video ads
4. Deployment (Case-Dependent)
In certain cases, Amazon may deploy AI-driven changes if it determines they improve customer experience — sometimes without prior seller notification.
As Starfish expands globally, expect AI involvement to increase, especially for incomplete listings.
The 6-Step Starfish-Proof Strategy
Step 1: Optimize Your Brand Website
Why: Starfish scrapes brand websites FIRST. Your site is the most trusted data source.
Action items:
- Complete product specs (dimensions, materials, certifications)
- High-resolution images (minimum 2000px)
- Add schema.org Product markup
- Don’t block Amazonbot in robots.txt
- Use the same terminology as your Amazon listings
Pro Tip:
Create a hidden “/amazon-data/” page with canonical specs for every SKU. Make it crawlable but not visible in navigation.
Step 2: Fill Every Seller Central Field
Why: Blank fields = AI makes assumptions. Complete fields = AI uses YOUR data.
The non-negotiables:
- All 5 bullet points (use full 1000 characters)
- Complete product description (2000 characters)
- 250 bytes of backend search terms
- All attribute fields (materials, certifications, dimensions)
- A+ Content with comparison charts
- At least one product video
- 5-10 proactive Q&A entries
Reality check: If you’re not using 90%+ of available fields, AI is filling the gaps—and making mistakes.
Step 3: Maintain Cross-Channel Consistency
Why: When Amazon finds conflicting data (your site says “6mm” but Amazon says “1/4 inch”), AI makes judgment calls. Usually wrong ones.
How to fix it:
- Create a master SKU spreadsheet with:
- Exact titles
- All 5 bullets
- Dimensions (standardized units)
- Materials
- Key features
- Use this EXACT data across:
- Amazon Seller Central
- Your brand website
- Distributor portals
- All other marketplaces
- Standardize everything:
- Same units of measurement
- Same color names
- Same terminology
Tools like Google Sheets or Airtable work well as a single source of truth.
Step 4: Use Amazon’s AI Tools First
Why: If you test AI-generated content on YOUR terms, you control what gets deployed. If you wait, Amazon does it automatically.
The process:
- Identify underperforming ASINs (low conversion, high returns)
- Click “Generate with AI” in Seller Central
- Review AI suggestions
- Run 2-4 week A/B test (original vs. AI version)
- Keep what works, reject what doesn’t
Start with: Your worst-performing 10% of listings. You have nothing to lose.
Step 5: Build Brand Registry Content
Why: Brand Registry-verified content has higher “authority” in Amazon’s AI hierarchy. It’s harder to override.
Priority actions:
- Enroll in Amazon Brand Registry (free with trademark)
- Create A+ Content for every parent ASIN
- Build an Amazon Brand Store
- Upload brand assets (logos, style guides)
- Add trademarked keywords AI won’t replace
Content authority hierarchy:
- Brand Registry A+ Content (highest)
- Seller-created content in complete fields
- AI-enhanced content from brand website
- AI-generated from scraped sources
- AI-guessed from incomplete data (lowest)
Goal: Get all your content into categories 1-2.
Step 6: Monitor for Changes Daily
Why: Amazon makes changes without notification. Catch them in 24-48 hours or lose sales.
What to track:
- Title changes
- Bullet modifications
- Description rewrites
- Image swaps
- Backend keyword updates
- Suppressed listings
How to monitor:
Free option: Check top 20% of SKUs daily, screenshot weekly
Paid option: Use automated monitoring tools with email alerts
Response protocol:
- Document what changed (screenshot before/after)
- Check performance impact (conversion rate, sales)
- If negative → Revert immediately and fill data gaps
- If positive → Keep it and update master spreadsheet
- If neutral → Test 2 weeks before deciding
The Starfish-Proof Checklist
Run this monthly on your top SKUs:
Brand Website:
- Complete specs posted
- High-res images (2000px+)
- Schema markup implemented
- Amazonbot crawler allowed
- Terminology matches Amazon
Seller Central:
- All 5 bullets filled
- Full description used
- Backend search terms complete
- All attributes filled
- A+ Content created
- Video uploaded
- Q&A populated
Consistency:
- Master SKU sheet created
- Same data across all channels
- Standardized units/terminology
- Regular audits scheduled
Brand Protection:
- Brand Registry enrolled
- A+ Content on all ASINs
- Brand Store built
- Assets uploaded
Monitoring:
- Change alerts configured
- Daily top SKU checks
- Weekly full reviews
- Revert process documented
Common Mistakes That Kill Your Strategy
Mistake #1: Ignoring It Completely
Assuming AI won’t touch your listings if you don’t opt in.
Reality: AI-driven changes can still occur.
Fix: Engage proactively.
Mistake #2: Blocking Amazon’s Crawler
Blocking Amazonbot prevents Amazon from seeing your data.
Fix: Allow Amazonbot and control what it sees.
Mistake #3: Fighting Every AI Change
Not every AI update is bad.
Fix: Test objectively and keep what improves performance.
Mistake #4: Leaving Gaps for “SEO”
Blank fields invite incorrect assumptions.
Fix: Fill everything now.
Mistake #5: Inconsistent Cross-Channel Data
Conflicting specs force AI to guess.
Fix: Maintain one source of truth.
What to Do Next
Stop reading. Start implementing.
- Audit your top 20 SKUs using the checklist above
- Fill critical gaps (bullets, attributes, videos, backend terms)
- Test “Generate with AI” on 3–5 underperforming ASINs
- Set up listing change monitoring
- Create a master SKU spreadsheet for data consistency
Over the following weeks, monitor results, expand tests to additional SKUs, and refine based on performance.
The Bottom Line
Amazon Project Starfish is already rewriting millions of listings. The question isn’t whether AI will touch your catalog.
The question is: Will it make your listings better or worse?
The answer depends on what data you give it to work with.
Sellers who optimize source data, fill every field, maintain consistency, and monitor proactively will see:
- Higher conversion rates
- Better discoverability
- Reduced returns
- Time savings from automation
Sellers who ignore this will wake up wondering why their carefully crafted listings disappeared — and their sales went with them.
The window to adapt is still open. But it’s closing fast.
FAQ
No. Amazon may make changes automatically if it believes they improve customer experience, which is why monitoring matters.
Yes, but if underlying data gaps remain, AI may change them again.
Partially. Brand Registry content has higher authority, but Amazon can still make updates in certain cases.
Use them strategically. Testing gives you more control than ignoring them.
It’s rolling out globally, with expansion continuing through 2024–2025.
About Seller Labs: We help Amazon sellers monitor listing changes, analyze product data, and make informed optimization decisions so they can respond faster to marketplace shifts.
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