Product description generation and image enhancement at scale for ecommerce sellers

Imagine you're managing a catalogue of 500 product listings across Amazon, your own Shopify store, and eBay. You've got new inventory arriving weekly. Your descriptions are stale, your images don't match brand guidelines, and you're spending 20 hours a week just trying to keep things consistent. Your competitors, meanwhile, seem to push fresh listings out effortlessly. The difference isn't that they work harder; they've automated the parts that don't require human judgment. This workflow solves that problem. We're going to wire together three AI tools to take raw product data, generate compelling descriptions, enhance product images, and push everything back to your database without touching a single file manually. The entire process runs on a schedule you define. New stock arrives, descriptions and images are ready within minutes.

The Automated Workflow

The best orchestration tool for this job is n8n, which gives you fine-grained control over API calls, conditional logic, and error handling without requiring a credit card to start. If you prefer a visual builder with slightly less technical setup, Make works equally well. We'll build this to work with whatever ecommerce platform you use; the example below assumes a REST API endpoint where you can fetch and update products. How the workflow moves: 1. Trigger: A webhook fires when a new product is created in your inventory system, or run on a daily schedule to process all products without descriptions.

2. Fetch product data: Retrieve product title, category, price, and key features from your database.

3. Generate description: Send that information to Copy.ai to create a compelling, SEO-aware product description.

4. Enhance product image: Take the main product image URL, send it to Pixelcut AI for background removal, upscaling, and enhancement.

5. Generate backup image: Use AI Boost to generate an alternative product image in case the original photo is poor quality.

6. Write back: Update your product record with the new description and image URLs. Let's build this in n8n. Here's the core flow:

CopyWebhook / Schedule Trigger ↓
HTTP Request: Fetch Product Data ↓
Copy.ai Node: Generate Description ↓
Pixelcut AI Node: Enhance Main Image ↓
AI Boost Node: Generate Secondary Image ↓
HTTP Request: Update Product in Database ↓
Error Handler: Log failures to Slack

Step 1: Create the trigger

Set up a webhook in n8n that listens for POST requests from your inventory system. If you don't have that integration, use a schedule trigger to process products on a cadence (e.g. every hour, every 6 hours).

CopyPOST https://your-n8n-instance.com/webhook/products
Content-Type: application/json { "product_id": "SKU-12345", "title": "Blue Cotton T-Shirt", "category": "Apparel", "price": 24.99, "image_url": "https://cdn.example.com/products/sku-12345.jpg", "features": ["100% organic cotton", "Machine washable", "Sustainable dye"]
}

Step 2: Fetch full product data

If your webhook only contains basic info, add an HTTP Request node to pull the complete record from your API:

CopyGET /api/v1/products/{product_id}
Authorization: Bearer YOUR_API_KEY

Store the response in a variable so subsequent nodes can reference it.

Step 3: Generate the product description

Copy.ai has a straightforward REST API. You'll need your API key from your Copy.ai dashboard. Format your request like this:

CopyPOST https://api.copy.ai/api/v1/copy
Authorization: Bearer YOUR_COPY_AI_KEY
Content-Type: application/json { "prompt": "Write a compelling product description for a product with the following details. Title: Blue Cotton T-Shirt. Price: $24.99. Category: Apparel. Key features: 100% organic cotton, machine washable, sustainable dye. Keep it under 150 words, highlight quality and sustainability, and include a call-to-action.", "model": "gpt-4o-mini"
}

Copy the description text from the response and store it in a variable. If you need multiple description variations (short form for thumbnails, long form for the main page), run this step twice with different prompts.

Step 4: Enhance the main product image

Pixelcut AI's API accepts an image URL and returns an enhanced version. Use this endpoint:

CopyPOST https://api.pixelcut.ai/v1/enhance
Authorization: Bearer YOUR_PIXELCUT_KEY
Content-Type: application/json { "image_url": "https://cdn.example.com/products/sku-12345.jpg", "operations": ["upscale", "background_remove", "auto_enhance"], "output_format": "webp"
}

The response will include a URL to the enhanced image. This becomes your primary product image in the database.

Step 5: Generate a secondary image

AI Boost offers broader image generation and manipulation. If your product image is weak or you want to show the product in different contexts, call AI Boost to generate a complementary image:

CopyPOST https://api.aiboost.com/v1/generate
Authorization: Bearer YOUR_AI_BOOST_KEY
Content-Type: application/json { "prompt": "Product photography style image of a blue cotton t-shirt on a clean white background, professional lighting, studio quality", "style": "product_photography", "dimensions": "1200x1200"
}

Store the generated image URL.

Step 6: Update your product database

Send everything back to your system:

CopyPATCH /api/v1/products/{product_id}
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json { "description": "[generated description from Copy.ai]", "image_primary": "[enhanced image URL from Pixelcut]", "image_secondary": "[generated image URL from AI Boost]", "updated_at": "[current timestamp]", "status": "automated_processing_complete"
}

Step 7: Error handling

Add a final node that catches any failures and posts them to a Slack channel. This is crucial because API rate limits, timeouts, or authentication issues will occasionally stall your workflow. You want visibility:

CopyPOST https://hooks.slack.com/services/YOUR/WEBHOOK/URL
Content-Type: application/json { "text": "Product processing failed", "blocks": [ { "type": "section", "text": { "type": "mrkdwn", "text": "*Product ID:* SKU-12345\n*Error:* API rate limit exceeded\n*Timestamp:* 2026-03-15T14:32:00Z" } } ]
}

Set this error handler on all nodes that call external APIs. In n8n, use "On Error" conditions to route failures to the Slack node.

The Manual Alternative

Not everyone wants to automate immediately. If you'd rather test the workflow first, you can run it semi-manually using Make's visual interface. Create a Make scenario with the same steps, but trigger it manually from the dashboard for a handful of products before scheduling it. This gives you a chance to review generated descriptions and images before they go live. Alternatively, use Copy.ai and Pixelcut AI's web dashboards directly. Paste your product title and features into Copy.ai, get a description, then upload your image to Pixelcut for enhancement. Download the results and update your inventory system by hand. It's slower, but you maintain full editorial control. Most teams start here, then automate once they see the pattern working.

Pro Tips

Rate limiting and backoff logic.

Copy.ai and Pixelcut both have rate limits.

If you're processing 500 products and hit a limit after 100, your workflow stops. Build in exponential backoff: if an API call fails with a 429 status code, wait 10 seconds, then 30 seconds, then a minute before retrying. n8n and Make both support this natively.

Image format consistency.

Different marketplaces want different image dimensions. Amazon prefers 1000x1000 or larger, eBay prefers square images, and your Shopify store might use 800x800. Use Pixelcut's output options to standardise formats before writing back, or store all three and let your database layer handle the conversion.

Prompt engineering for descriptions.

Generic prompts produce generic descriptions. Include brand voice guidelines in your Copy.ai request. Instead of "Write a product description," try "Write a product description in the tone of a premium sustainable brand, emphasising ethical production and durability. Avoid clichés like 'significant' or 'new.'" This takes 30 seconds to set up once and pays off across hundreds of products.

Validate descriptions before publishing.

Generated text is usually good but occasionally nonsensical, especially if your product data is sparse. Add a human approval step for the first batch. Have a team member review 20-30 descriptions and flag any that miss the mark. Once you're confident, remove the approval step and let the automation run.

Cost savings with model selection.

Using GPT-4o for every description is expensive. Use GPT-4o mini instead; it's faster and cheaper for straightforward copywriting tasks. Reserve the larger models for edge cases where your product is complex or niche.

Cost Breakdown