Illustrative sample · AEO/GEO Fix Pack
100W USB-C charger repair pack.
This sample shows what an ecommerce seller receives after one SKU is tested against AI shopping prompts. A paid pack replaces this public example with live model outputs, platform-specific evidence gaps, and publishable repair assets.
Managed from Workspace
This is the full delivery artifact for one SKU. Use Workspace to manage the queue, publish work, client handoff, monitoring, and retests.
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Methodology
What this audit checked and where evidence stops.
- Prompts tested against buyer questions.
- Models/surfaces tested across AI shopping answers.
- Captured product evidence from the product page and seller inputs.
- Citations reviewed from returned AI sources.
- Limits: AI outputs are evidence leads, not platform guarantees. Seller verification required before publishing.
Priority command
Proof layer first.
Start with the PDP compatibility and safety block. The detailed diagnosis below explains why; the assets section contains the publishable draft.
Primary issue
Evidence trust gapAI sees the charger as technically plausible, but not well-proven.
Main competitors
Competitor A + BBoth brands surface clearer compatibility, safety, and review proof.
Top missing signals
Compatibility, safety, proofThe SKU lacks extractable evidence across the key shopping prompts tested.
First action
Repair the product proof layerStart with compatibility, heat behavior, and wattage-sharing clarity.
Why AI hesitates.
Diagnosis
Why AI recommends competitors first.
The issue is not just content volume. The issue is that competitor evidence is easier for AI to extract, trust, and restate in shopping answers.
AI can see a charger, but not enough extractable proof around compatibility and safety.
Competitors have more review, comparison, and retailer evidence for AI to cite safely.
AI falls back to known brands when the SKU cannot prove fit, wattage behavior, and trust.
Diagnosis narrative
AI trusts the competitors before it understands the SKU.
In trust-led prompts, AI is likely to recommend Competitor A and B first because their public evidence is easier to extract: device fit, safety claims, review volume, retailer proof, and comparison mentions. This charger may still be competitive, but it does not currently present enough structured, verifiable proof for AI to confidently defend that recommendation.
Prompt cluster tested
- Best 100W USB-C charger for MacBook travel
- Compact charger for laptop, phone, and watch
- Safe GaN charger that stays cool
- 100W charger alternative to Apple 96W adapter
Likely shelf winners
- Competitor A: premium trust, review depth, charger safety proof
- Competitor B: compact value, clear 100W positioning, retailer mentions
- Competitor E: price-sensitive angle and travel-adapter variants
Where the recommendation case breaks.
Evidence gaps
What is missing from the recommendation case.
These are not copy problems alone. These are evidence problems that weaken AI confidence when buyers ask trust-sensitive questions.
High priority
Compatibility
Model fit is too vague.
Buyers and AI both need exact device guidance: which MacBook models charge at full speed, partial speed, or not ideally at all.
High priority
Safety proof
Heat behavior lacks proof.
The product mentions GaN and safety, but does not provide enough extractable reassurance about sustained load, temperature, and protection behavior.
Medium priority
Wattage sharing
Multi-port output is not explicit.
AI cannot easily explain what happens when a laptop, phone, and earbuds are connected together, so competitor answers sound safer.
Medium priority
Third-party proof
External validation is too thin.
Without reviews, comparison pages, or credible mentions, AI has fewer safe citations to justify recommending this SKU over leaders.
What should ship first.
Fix priorities
What should be fixed first.
The right sequence is to strengthen the recommendation case before producing volume. The first wins should improve clarity, trust, and extraction quality.
Priority 1
Repair the PDP proof layer.
- Add exact compatibility guidance for major MacBook models.
- Explain heat behavior and safety protections in answer-ready language.
- Clarify output behavior for one-port, two-port, and three-port use.
Priority 2
Propagate the same facts outward.
- Rewrite marketplace bullets with clearer wattage and travel positioning.
- Deploy FAQ, schema, and comparison content using the same verified claims.
- Give creators a brief that captures real-world proof, not generic praise.
Priority 3
Create external recommendation support.
- Earn or publish credible review and comparison mentions.
- Support AI with clearer structured fields and retailer trust signals.
- Retest after each proof layer is published to see which signals move fastest.
Publishable assets
Execution-ready deliverables.
Once the diagnosis is clear, these are the exact assets a seller or operator can publish to repair the evidence story across channels.
PDP block
Compatibility proof
Works best for: MacBook Air, MacBook Pro 14, iPad Pro, iPhone, and USB-C travel setups. For MacBook Pro 16, use the primary USB-C port for the fastest single-device charging.
- Open Shopify Admin and navigate to Products.
- Select the target SKU and click Edit.
- Switch the description editor to HTML mode.
- Paste this block at the bottom of the description.
FAQ block
Buyer objection answer
Q: Does the charger get hot?
A: It may warm under continuous laptop charging, but the housing, GaN chipset, and safety protections are designed for sustained travel and desk use.
- Log in to your website CMS or Store builder.
- Navigate to the Product page's FAQ section.
- Add a new Q&A item and paste the copied text.
- Save and publish changes.
Listing rewrite
Amazon bullet
100W USB-C output for laptops and multi-device travel, with clear wattage sharing when charging a MacBook, phone, and earbuds at the same time.
- Log in to Amazon Seller Central.
- Go to Inventory > Manage Inventory.
- Find your SKU and click Edit.
- In the Product Details tab, paste this into one of the Bullet Points.
- Save and finish.
TikTok Shop asset
Creator proof prompt
Show a travel desk setup: MacBook charging, phone charging, charger surface temperature after 20 minutes, and bag space comparison versus a stock laptop adapter.
- Log in to TikTok Affiliate / Creator Marketplace.
- Select the Creators you are targeting.
- Paste this instruction into the campaign brief or direct message.
- Ensure they include this specific proof in their video hook.
Comparison
Competitor page angle
Compare against Competitor A and B on sustained output, port behavior, size, warranty, included cable, travel use, and price per watt.
- Create or edit your "vs Competitors" landing page.
- Structure the comparison matrix around these specific vectors.
- Provide hard numbers (e.g., exact temperatures, wattage splits) rather than just checkmarks.
Schema/feed
Structured data fields
Product, Offer, shippingDetails, hasMerchantReturnPolicy, aggregateRating where accurate, material, wattage, compatibleDevice, certification, and variant availability.
- Update your site's JSON-LD script or feed management app.
- Ensure these fields are dynamically populated with correct variant data.
- Run your URL through the Google Rich Results Test to verify.
Retest plan
How progress should be measured.
Fixes should not be shipped blindly. The goal is to see whether AI recommendation confidence improves after each proof layer is added.
Baseline prompts
Save the baseline
Keep the exact prompts, AI answers, competitor mentions, and citations from this run.
Priority proof
Publish priority proof
Ship the highest-impact evidence repairs first so movement can be attributed to a real proof change.
Same questions
Rerun the same prompts
Use the original buyer questions and compare recommendation language rather than changing the test.
Movement read
Compare movement
Review mention rate, resolved gaps, competitor defaulting, citations, and explanation quality.
Retest
Movement schedule.
- Day 0: baseline shelf test before publishing fixes.
- Day 7: first retest after PDP, listing, and FAQ updates.
- Day 14: retest after comparison and schema/feed updates.
- Day 30: final movement read and next repair backlog.
Success criteria
- Higher mention rate in shopping prompts.
- Cleaner explanation of compatibility and safety.
- Reduced competitor defaulting in trust-led prompts.
- More extractable proof across page, FAQ, schema, and comparisons.
Sources and limits
What informed this sample diagnosis.
These are not live AI answers. They represent the type of public evidence and comparison material a real shelf test would evaluate before the repair assets are created.
Ready for your SKU?
Turn one product into a repair plan.
Send a product URL, platform, competitor, and concern. The first audit finds where AI recommendations break and what evidence to fix first.