AI Product DescriptionGeneration
Fill a catalogue of thousands of products with consistent, factual descriptions written from your own attribute data, not from imagination.
AI Product Description Generation: what the work involves
A store that adds products faster than anyone can write about them ends up with empty descriptions, supplier text copied across dozens of shops, or one-line entries that tell a shopper nothing. Writing each by hand for a catalogue of several thousand items is unrealistic, and copying manufacturer text creates duplicate content that search engines ignore.
We start from your structured data: title, brand, material, dimensions, variants, care notes, and supplier specifications. A language model writes a description from those fields only, following a template per product category, such as length, order of points and tone. Attributes the data lacks are never invented; the output marks them as missing. A validation step checks numbers and units against the source fields. Images can be read by a vision model to suggest colour or style tags, which a person confirms. Results appear in a review grid, and approved items are pushed in bulk to Shopify, WooCommerce or your custom store, in English or Urdu.
Core features
Attribute-only writing
Descriptions are built from your product fields, so claims about size, material or compatibility come from real data.
Category templates
Each product type gets its own structure, so shoes, laptops and skincare read as they should.
Missing-data flags
Where an attribute is absent, the system says so instead of inventing it, giving your team a list to complete.
Number and unit validation
Measurements, quantities and specifications in the text are checked against the source fields automatically.
SEO fields included
Titles, meta descriptions and alt text are generated alongside the main copy within your length limits.
Bulk review and publish
A grid lets staff approve, edit or reject in batches, then push to Shopify or WooCommerce through their APIs.
What we get right before launch
Invented specifications
A wrong battery size or an unsupported material claim causes returns and legal exposure. Output is restricted to supplied fields, and a verifier compares numbers before anything goes live.
Sameness across thousands of pages
Templated text across a catalogue looks repetitive. We vary structure within rules, add category-specific detail, and check similarity across items.
Regulated product claims
Cosmetics, supplements and children's items face strict claim rules. We include a restricted-claims list and send those categories through mandatory human review.
Tools and technology
- OpenAI
- Google Gemini
- Python
- FastAPI
- Shopify API
- WooCommerce REST API
- PostgreSQL
- n8n
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Common questions, answered
Can it describe a product from just a photo?
A vision model can suggest visible traits such as colour or style, but it cannot know size, material or specifications. We use images as supporting hints, with a person confirming, and rely on real data for facts.
Does it work in Urdu?
Yes, for ordinary product language. We test your categories, since terminology varies, and recommend a native-speaking reviewer for the first batches and any culturally sensitive items.
Will it overwrite descriptions we already like?
No. You choose the scope, such as only empty descriptions. New text is staged for review first, and originals are kept so you can revert any item.
How do you handle variants like size and colour?
Variant data is passed as structured fields, so the description covers the shared product and variant-specific parts are generated per option or left to your store's selectors.
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Ready to start your AI Product Description Generation project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.