Generative AI The AI writes the draft. Your team approves it.

Product copy, translations, category texts and draft replies for support, created from your product data and manuals. With templates, a defined tone and an approval step before anything goes live.

Discuss a use case

Where it pays off

Lots of text, little time. And always the same structure.

  • Product copy from attributes

    Description, bullet points and meta data from dimensions, material and application, following your template.

  • Translations

    Into every shop language, with a glossary so technical terms are the same everywhere.

  • Category and guide texts

    Introductions for categories and buying guides that refer to the actual assortment.

  • Draft replies in support

    Based on manuals and internal rules. Your team checks and sends.

So nothing is made up

If a value is missing from the data sheet, the AI does not invent one.

  • Only from your data

    The basis is PIM, catalog and documents. If information is missing, the text is flagged instead of embellished.

  • Templates and tone

    Structure, length and wording are defined. So are banned terms and mandatory details.

  • Approval before publishing

    Nothing goes into the shop unchecked. Edits by your editors flow back into the template.

  • Model to suit the task

    OpenAI, Gemini or a self-hosted model, depending on privacy, cost and quality.

Typing product copy until your hands cramp? Let it be typed for you.

The AI drafts from your product data, your editors check and approve. That way you get through the whole catalog, not just the first hundred items.

Discuss a use case
Vintage mint-green typewriter

How we work

One product group first. Then the rest.

  1. Check the data

    Are attributes and documents good enough for good copy, or is something missing already?

  2. Build the template

    Define structure, tone and mandatory details together with your editors.

  3. Test run

    Generate one product group, have it reviewed and measure how much needs fixing.

  4. Roll out

    More assortments and languages, with cost and quality control.

Honestly

Bad product data just gets bad faster.

Worth it if

  • you have thousands of items with a similar structure,
  • copy is needed in several languages,
  • the attributes in your PIM or shop are maintained.

Probably not if

  • your product data has gaps. Then fix the data first,
  • every text is a one-off that someone writes by hand anyway.

Your catalog has more items than your editors have hours.

Discuss a use case