Product Description Generator
A product description generator writes unique, search-aware descriptions across your whole catalogue, working from your product data and images. For a retailer with 8,000 SKUs carrying supplier text or nothing at all, it fixes in days a problem that has been on the list for three years.
Who it's for
E-commerce sellers with thousands of thin product pages.
What changes
Every product page becomes a page that can rank.
- Starting at
- ₹55,000
- Timeline
- 3–6 weeks
- Category
- AI Content & Creative
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Duplicated supplier text across retailers is a direct cause of poor product-page rankings.
- Descriptions must be genuinely different per product, not a template with variables swapped.
- Attribute completeness caps quality — the generator cannot describe what it does not know.
- Typical cost is under ₹2 per SKU including generation and quality checks.
- Projects run 4–8 weeks from ₹1,80,000.
Why supplier descriptions cost you rankings
Most Indian retailers import the manufacturer's description with the product feed. So does every other retailer selling the same item.
The result is that the identical paragraph appears on forty sites. Search engines pick one to rank and it is usually the manufacturer or the largest marketplace, not you. Your product page has no unique text to rank on at all.
Rewriting 8,000 descriptions by hand is the obvious fix and never happens, because at fifteen minutes each it is two thousand hours of work.
The difference between generated and templated
This distinction is the whole quality question, and it is worth being concrete about.
| Approach | What it produces | SEO outcome |
|---|---|---|
| Supplier text imported | Identical to 40 other sites | No unique content to rank |
| Template with variables | "Buy [product] in [colour] at [price]" | Detected as thin and duplicated |
| Attribute-grounded generation | Different structure and emphasis per product | Genuinely unique pages |
| Generation plus real review data | Includes what customers actually say | Strongest — original information |
What goes into each description
The generator works from everything you know about the product, which is usually more than the current description reflects.
- Structured attributes. Material, dimensions, colour, compatibility — the specification, written into prose rather than left in a table nobody reads.
- Image-derived detail. Where attributes are missing, a vision pass fills gaps from the product photographs.
- Review themes. What buyers repeatedly praise or complain about, which is information no competitor's description contains.
- Category context. What a buyer in this category is deciding between and what they need to know to choose.
- Search intent. The terms people actually use for this product, worked in naturally rather than stuffed.
Handling messy supplier feeds
Real supplier data is inconsistent: half the fields blank, dimensions in three different formats, colours described as "Blue", "BLUE" and "Nvy Blue", product names carrying category words and stock codes.
The pipeline normalises before it generates — mapping colour variants to your standard palette, converting units, splitting compound fields, and extracting attributes from unstructured description text where the structured field is empty.
This normalisation work is usually a third of the project and it improves everything downstream, not just descriptions. Filters, search and marketplace feeds all benefit from a catalogue where colour means one thing.
Quality control at catalogue scale
You cannot read 8,000 descriptions, so the checking has to be systematic.
We measure similarity across the generated set and flag any cluster that is too alike, which catches the failure mode where the generator settles into a pattern for a whole category. We check that every factual claim maps to a source field. We run a sample of two hundred through full human review before any bulk publication.
The similarity measurement is the one clients have not usually thought of and the one that matters most, because a catalogue of 8,000 descriptions that are 80% alike is the same scaled-content problem in a different shape.
Delivery and ongoing use
Four to eight weeks from ₹1,80,000, covering data normalisation, the generation pipeline, quality checks, commerce platform integration and the review interface.
Per-SKU cost is under ₹2 including generation and checking, so even a very large catalogue is a modest processing bill against the setup.
The pipeline stays connected to your product feed, so new SKUs get described on arrival. That is what stops the problem returning, which it otherwise does within about eighteen months as the catalogue turns over.
FAQ
Product Description Generator — your questions
How long should a product description be?
Shorter than most retailers assume. Eighty to two hundred words of specific, useful text outperforms six hundred words of adjectives on both conversion and search. The exception is considered purchases — appliances, electronics, furniture — where buyers genuinely read, and there four hundred words that answer real questions works well. We set length per category rather than applying one rule, based on what actually ranks for those queries.
Can it write in multiple languages?
Yes, and for marketplaces and regional audiences it is often worth doing. Hindi, Marathi, Tamil, Telugu, Bengali and Gujarati all work well. The caution is that a machine-translated description reads worse than one generated natively in that language from the same attributes, so we generate rather than translate. Reviewing quality in each language needs someone who reads it, which limits how many languages a small team can genuinely maintain.
Will marketplaces accept the generated descriptions?
Yes, subject to their formatting rules, which we configure per marketplace. Amazon, Flipkart and Myntra each have character limits, prohibited-phrase lists and bullet-point conventions. The generator produces a compliant version per channel from the same underlying content rather than one description squeezed into every format. This also lets your own site carry a longer, better version than the marketplace listings.
What if we already have good descriptions for some products?
Keep them. We run the pipeline against products lacking descriptions or carrying supplier text, and leave anything you have written alone unless you ask otherwise. Where you have a hundred well-written descriptions, they are useful as voice examples — the generator learns your style from them, which produces better output than any style brief we could write.
How do we know it improved anything?
Measure organic traffic to product pages and product-page conversion rate, comparing rewritten SKUs against a held-back control group rather than before-and-after across the whole catalogue. Seasonality otherwise takes the credit. Give it three months, since indexing and ranking changes are not immediate. We set the control group up during the project so the measurement is available rather than reconstructed later.
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Next step
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