Clean Up Your Catalog:
Automating Product Listing Management with AI

1. The Catalog Chaos Challenge

As e-commerce platforms grow, so does the complexity of their catalogs. Third-party sellers upload products with varying titles, descriptions, and image qualities. The result is a fragmented user experience where the same "iPhone 13 Case" might appear twenty times with slightly different names.

Manual curation is impossible at scale. Rule-based systems fail because they can't handle the nuance of human language or visual similarity. This is where Generative AI and multimodal models shine.

2. The Solution: AI-Powered Catalog Intelligence

By combining computer vision and natural language processing, we can build a system that "understands" products like a human merchandiser but operates at the speed of software.

Key Capabilities:

• Visual De-duplication: Identifying that two images show the same product, even from different angles or lighting.
• Semantic Matching: Understanding that "Men's Running Shoe" and "Male Jogging Sneaker" are the same category.
• Attribute Extraction: Automatically pulling "Color: Blue" and "Material: Leather" from unstructured descriptions.
• Golden Record Creation: Merging the best data from multiple duplicates to create one perfect listing.

3. Technical Blueprint

Here is the architecture for an automated listing management system using Google Cloud Vertex AI.

[Ingestion] -> [Processing Pipeline] -> [Resolution] -> [Output]

1. Ingestion:
   - Product Feed (JSON/CSV)
   - Images (GCS Bucket)

2. Processing (Vertex AI):
   - Vision API: Generate image embeddings
   - Text Embedding API: Generate title/desc embeddings
   - Vector Search: Find nearest neighbors (potential duplicates)

3. Resolution (LLM):
   - Gemini Pro: Compare candidate pairs
   - "Are these the same product?" (Yes/No + Confidence)
   - "Merge attributes into golden record"

4. Output:
   - Cleaned Catalog DB
   - Merge Report
                        

Step-by-Step Implementation

Step 1: Embedding Generation

First, we convert all product data into vector embeddings. This allows us to perform semantic search rather than just keyword matching.

from google.cloud import aiplatform

# Generate text embeddings for titles
def get_text_embedding(text):
    model = aiplatform.TextEmbeddingModel.from_pretrained("text-embedding-004")
    embeddings = model.get_embeddings([text])
    return embeddings[0].values

# Generate image embeddings
def get_image_embedding(image_path):
    model = aiplatform.ImageEmbeddingModel.from_pretrained("multimodalembedding")
    return model.get_embeddings(image=image_path).image_embedding
                        

Step 2: Vector Search for Candidates

We use a vector database (like Vertex AI Vector Search) to find items that are close to each other in vector space. These are our "candidate pairs" for duplication.

Step 3: LLM Verification

Vector search gives us *similar* items, but an LLM determines if they are *identical*. We prompt Gemini to act as a merchandiser.

prompt = """
You are an expert e-commerce merchandiser.
Product A: {title_a}, {desc_a}
Product B: {title_b}, {desc_b}

Are these the same product? 
If yes, create a merged 'golden' title and description that combines the best details from both.
Output JSON: {is_duplicate: bool, confidence: float, merged_data: dict}
"""
                        

4. Benefits & ROI

• Improved SEO: Consolidating duplicates concentrates page authority, leading to higher rankings.
• Better UX: Customers find what they want faster without wading through clutter.
• Operational Savings: Reduce manual moderation teams by 80%.
• Trust & Safety: Easier to detect counterfeit or prohibited items when the catalog is clean.

5. Conclusion

Automated product listing management is no longer a luxury for large marketplaces—it's a necessity. With Generative AI, the technology is finally accessible and effective enough to solve the problem of catalog chaos once and for all.