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AMAZON_A.I.

v1.0.0

[SYS] Advanced listing optimization system: Content analysis + AI indexing enhancement

$ cat description.txt Comprehensive Amazon listing optimization workflow that leverages AWS Rekognition and Comprehend to analyze product images and text. This system identifies entity recognition patterns used by Amazon's search algorithms, then generates targeted suggestions to enhance visibility in both machine indexing and AI-assisted search results.

CORE_FEATURES:

Data Extraction

> Amazon listing scraping
> ASIN identification
> Image & text isolation

AI Analysis

> AWS Rekognition integration
> AWS Comprehend processing
> Entity recognition scoring

Optimization Engine

> GPT-4o insights
> Entity relevance scoring
> Indexing enhancement

Reporting

> Google Docs integration
> Actionable recommendations
> Confidence scoring

EXAMPLE_OUTPUT:

Prompt Input:

"Amazon Product URL": "https://www.amazon.com/Zhou-Collagen-Peptides-Grass-Fed-Certified/dp/B01IG59DTM/",

"Analysis type": "comprehensive",

"Output format": "Google Doc"

RESULTING ANALYSIS REPORT
🔍

Amazon Listing Optimization: AI Analysis Report

💡
This report provides AI-powered insights to optimize your Amazon listing for better visibility, search rankings, and conversion. Based on AWS Rekognition and Comprehend analysis of your product content.

Product Overview

Zhou Collagen Peptides - 18 oz container with 46 servings. This product is manufactured in Utah and contains Type I and Type III collagen with 18 essential amino acids.

Text Analysis (AWS Comprehend)

Detected Entities with Confidence Scores
Entity: Zhou | Type: ORGANIZATION | Confidence: 97%
Entity: 18 oz | Type: QUANTITY | Confidence: 89%
Entity: 46 Servings | Type: QUANTITY | Confidence: 91%
Entity: Utah | Type: LOCATION | Confidence: 94%
Entity: Type I and III | Type: OTHER | Confidence: 77%
Entity: Collagen | Type: QUANTITY | Confidence: 57% ⚠️
Entity: 18 Essential Aminos | Type: QUANTITY | Confidence: 88%
Entity: Over 90% | Type: QUANTITY | Confidence: 92%
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⚠️ Listing has misclassified entities. 'Collagen' is incorrectly detected as QUANTITY (57% confidence) instead of as a product ingredient.
Recommended Text Improvements
  1. Standardize brand references. Use "Zhou Collagen Peptides" consistently rather than switching between "Zhou" and "Zhous"
  2. Clarify ingredient descriptions. Replace "Type I and III" with "Type I & Type III collagen" to improve entity detection
  3. Add context for key terms. Change "collagen" to "100% pure hydrolyzed collagen peptides" to prevent misclassification as QUANTITY
  4. Structure numeric information consistently. Use "18 oz container with 46 servings" format throughout
  5. Include clear ingredient language. Add phrases like "key ingredient" or "main component" near collagen mentions

Image Analysis (AWS Rekognition)

Detected Labels with Confidence Scores
Label: Advertisement | Confidence: 99%
Label: Bottle | Confidence: 92%
Label: Poster | Confidence: 89%
Label: Text | Confidence: 97%
Label: Paper | Confidence: 86%
Label: Herbs | Confidence: 57% ⚠️
Label: Syrup | Confidence: 56% ⚠️
Label: Coffee Cup | Confidence: 58% ⚠️
Label: First Aid | Confidence: 61% ⚠️
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⚠️ Image has low-confidence and incorrect labels. Several labels (Herbs, Syrup, Coffee Cup, First Aid) have confidence scores below 62% and are not relevant to the product.
Recommended Image Improvements
  1. Use a clean white background to isolate the product and reduce background patterns that may trigger incorrect labels
  2. Center the collagen container as the main focus of the image, making it larger in the frame
  3. Improve text clarity on product labels by ensuring high contrast and readable font sizes
  4. Remove any decorative elements that could be misinterpreted as unrelated objects (like patterns that trigger "herbs" or "syrup" labels)
  5. If including supplementary images (like nutrition panels), use clear, structured formats with high contrast

Conclusion & Impact Analysis

This analysis identified several opportunities to optimize your Amazon listing for better AI recognition and improved visibility in search results. By implementing the recommended text and image changes, you can expect the following improvements:

  • More accurate product categorization by Amazon's AI systems
  • Increased visibility in relevant search results
  • Better entity recognition with higher confidence scores
  • Reduced incorrect categorization that could hurt visibility
  • Improved overall listing performance and potential for increased conversion
📈
Next steps: Apply the recommended changes to your listing and monitor performance for 2-3 weeks to observe improvements in search visibility and sales.

This is a stylized example of an analysis report created with our template 

$ system_requirements
                    
MODELS: gpt o3-mini, gpt 4o-vision
STORAGE: none required
SERVICES: rapidAPI parazun, AWS rekognition, AWS comprehend
OUTPUT: google docs
PRICING: gpt - per token, 
         rapidAPI parazun - free tier,
         AWS comprehend - free, 
         google docs - free,
         AWS rekognition - per token
EST. PER RUN COST: less than €0.01
>_

PROCESS_FLOW:

[INPUT] -> Amazon Product URL | [EXTRACTION] -> ASIN + Images + Text Features | ├── [IMAGE ANALYSIS] -> AWS Rekognition | ├── [TEXT ANALYSIS] -> AWS Comprehend | [ASSESSMENT] -> GPT-4o Evaluation | [OUTPUT] -> Optimization Report

OPTIMIZATION_BENEFITS:

  • > Understand how Amazon's AI categorizes your products
  • > Increase visibility in category-specific searches
  • > Optimize images for better algorithm recognition
  • > Enhance text for improved entity detection
  • > Gain advantage over competitors with AI-driven insights
€69
PURCHASE_TEMPLATE

* Compatible with all n8n installations v1.0.0+

*Superflowz is a subsidiary of CARDUME ESBELTO UNIP. LDA. Your purchase will be from, and your receipt will list, CARDUME ESBELTO