Automated product description generation

Automated product description generation is setting up a process where the AI, from structured data (name, specs, category, key properties), creates product descriptions in the needed style and length, in bulk for a large catalog. It suits when there are many items and writing each by hand is expensive and slow. Honestly about review, this is key: the AI creates a DRAFT, not a ready-to-publish final. The main risk is hallucinations: the model can confidently attribute a spec, material or effect a product does not have. For products this is a direct risk — complaints, returns, and in some categories (cosmetics, supplements, electronics, children's) also legal. So we build a process with review: the AI generates from factual data and does not make things up beyond it, while a human reviews before publishing (especially facts and claims). Honestly about responsibility: you as the seller are responsible for the accuracy of descriptions and for advertising/legal claims — the AI does not remove this responsibility. We honestly design it so the model relies on your data rather than fantasizes. Honestly about uniqueness/SEO: AI descriptions must be meaningful and varied, not stamped identical templates — otherwise the search benefit is small; we set up variability but do not promise a 'top guarantee'. Honestly about the effect: huge time savings on drafts for large catalogs, but not 'press a button and forget' — quality control is needed. Honestly about cost: generation is paid per token but usually cheap per product. Honestly about access: structured product data is needed. An important boundary: this is product descriptions; product image generation — 876; description translation — 871. Picture this: instead of 'thousands of cards without descriptions or with blanks' — drafts across the whole catalog in hours, left to review. The base price starts from 40,000 ₽ (depends on catalog volume and integration).

• Thousands of products without descriptions or with blank cards.
• Writing descriptions by hand for a large catalog is expensive and slow.
• Descriptions are uniform and help neither the client nor search.
• You need it fast, but fear the AI will write untruths about the product.

• Setting up description generation from product specs
• A set style, tone and length of descriptions
• Variability (not identical templates)
• Reliance on your data, without making up specs
• A human-review process before publishing
• Honest boundaries (hallucinations; claim responsibility is yours)
• Bulk processing of a large catalog
• Handover of the process and review with you

• Description drafts across the whole catalog in hours, not weeks
• A unified style and reasonable variability
• Huge copywriting time savings
• Fact control (a human reviews; responsibility is the seller's)

• We prepare structured product data; set the style
• We set up generation relying on data with variability
• We set up review, honestly set fact boundaries with you

• Can the AI attribute to a product something it does not have?

  Yes, that is the main risk (hallucinations), and we honestly handle it. The model can confidently make up a spec, material or effect. For products this is a risk of complaints and returns, and in some categories — legal. So generation relies on your factual data and does not go beyond it, while a human reviews descriptions before publishing. We honestly advise against publishing product descriptions fully on autopilot.

• Who is responsible for the accuracy of descriptions?

  You as the seller — honestly. The AI does not remove responsibility for the accuracy of specs and for advertising/legal claims (composition, effect, guarantees). So we build a process with human review, especially in sensitive categories. The AI speeds up drafts, but you confirm the final accuracy — that is both safer and more honest.

• Will generated descriptions give a search boost?

  They help if made meaningfully and variably, but promising a 'top guarantee' is dishonest. Identical template descriptions are of little use to search, so we set up variability and reliance on real properties. But positions depend on many factors (content, competition, all SEO). We raise card quality rather than guarantee ranking.