Product descriptions that are not manufacturer boilerplate

Most catalogues run on supplier copy that hundreds of other stores also use. It reads like a spec sheet, converts poorly, and gives search engines nothing unique to index.

Product description work stalls for a predictable reason: writing one good description takes twenty minutes, and a catalogue has two thousand products. So the supplier feed gets imported as-is. That is a conversion problem and a duplicate-content problem at the same time. The Builder and Visibility agents write per product, using the attributes and reviews that already exist in your store, to a structure that answers buying questions in order.

Written from what you already have

The raw material for a good description is already in your store — variant attributes, specifications, customer reviews, returns reasons, and support questions. The agent draws on those rather than inventing benefits, which is what keeps the output accurate and specific.

  • Real product attributes and variant differences, stated plainly

  • Objections and questions taken from your actual reviews and support tickets

  • Use-case framing for the buyer segment that actually purchases the item

  • Fit, sizing, materials, and compatibility details that reduce returns

Structured to be read and to be retrieved

Shoppers scan; models extract. Both are served by the same structure — a clear opening statement of what the product is and who it is for, scannable specifics, then supporting detail. Marketing prose that delays the answer hurts conversion and retrievability simultaneously.

Unique at scale, without drift

Every description is written per product, but to a consistent pattern, so the catalogue stays coherent. New products inherit the pattern automatically. Where the source data is too thin to write honestly, the agent flags the product instead of padding it.

How it works

01

Ingest the catalogue

The agent reads attributes, variants, existing copy, and reviews for each product.

02

Write to a pattern

Descriptions are generated per product against real data, in a consistent structure across the catalogue.

03

Review by sample

Approve a representative sample, adjust the pattern, then apply it across the catalogue with new products inheriting it.

What you get

  • Unique descriptions across the catalogue instead of shared supplier copy

  • Objections answered on the page, which reduces both hesitation and returns

  • Consistent structure that both shoppers and AI engines can parse

  • Thin-data products flagged rather than padded with invented claims

Frequently asked questions

Will it invent product features?

It should not, and that is a hard constraint. The agent writes from your attribute data and reviews; where the source data is insufficient it flags the product for a human rather than generating plausible-sounding specifications.

How long should a product description be?

Long enough to answer the buying questions for that category and no longer. A cable needs three lines; a mattress needs several hundred words. Fixed word-count targets are the wrong instinct.

Does unique copy actually help rankings?

It removes a specific harm — duplicate copy shared with every other retailer selling the same item gives a search engine no reason to prefer your page. It is necessary rather than sufficient.

Can it write in multiple languages?

Yes, and it writes per locale rather than machine-translating one source, because buying objections and terminology differ by market.

Put the team to work on your store

Connect your storefront, analytics, and email stack, set a goal, and let the agents run the work end to end. Start on the free plan with your own model key.