Verified on August 27, 2026.
AI can speed up the creation of product listings for Ozon and Wildberries if it generates text only from verified product data and passes the rules for a specific category. Without that, automation scales mistakes: it invents material, contents, or effects, mixes up variants, and sends prohibited wording into hundreds of SKUs.
A reliable pipeline is built around a catalog or PIM: facts → normalization → marketplace template → generation → automated checks → spot human review → test upload → moderation feedback.
In short: do not give the model only photos and a request to “make a high-converting product listing.” Provide structured fields, approved benefits, prohibited claims, the category, and the current marketplace rules. Any fact that is not in the input data must be blocked or flagged for clarification.
Contents
- Why a single prompt is not enough
- What data is needed as input
- Ozon and Wildberries: what differs
- Product listing generation pipeline
- How to generate a title
- How to write a description
- Specifications and variants
- Photos, infographics, and rich content
- Automated checks
- Bulk upload and rollback
- How to measure results
- Pilot plan
- FAQ
- How AI Dawn automates product listings
- Bottom line
Why a single prompt is not enough
A product listing is not just marketing copy. It is tied to the category, filters, inventory tracking, labeling, variants, certificates, and returns. A polished description will not fix the wrong color or material in the specifications.
Main risks of unconstrained generation:
- invented features and promises;
- mixing data from similar SKUs;
- inconsistency between the title, photos, and specifications;
- keyword repetition and prohibited symbols;
- incorrect contents or size;
- use of another brand;
- mass-uploading an error before it is discovered.
That is why the LLM should not be given the freedom to “know the product,” but rather a limited task of transforming approved facts into the marketplace format.
What data is needed as input
Create a source record for each SKU:
| Group | Example fields |
|---|---|
| Identifiers | SKU, item number, barcode, brand |
| Classification | marketplace category, product type, HS code if needed |
| Physical properties | material, composition, color, size, weight, dimensions |
| Contents | items and quantity |
| Usage | purpose, compatibility, care, limitations |
| Evidence | certificates, instructions, product passport, packaging photos |
| Approved claims | verified benefits and conditions of use |
| Restrictions | unverified effects, comparisons, other brands |
Each field gets a type and a source. Unknown does not become a plausible-sounding value. If a required attribute is missing, the listing goes into a clarification queue.
Ozon and Wildberries: what differs
Rules depend on the category and are updated, so they need to be stored as versioned configurations, not hard-coded into a single prompt.
In the current Wildberries instructions the title, description, and specifications are separated. The marketplace sets restrictions for the title and description, while the specifications are tied to search and filters. The description should not repeat everything that belongs in dedicated fields.
Ozon also uses category fields, templates, and separate media sections. For example, the video cover guide describes uploading through the product card and XLS, media reuse, and moderation. This shows an important architectural boundary: text, specifications, and media assets go through different rules and pipelines.
| Layer | General principle | What to store separately |
|---|---|---|
| Title | exact product definition | marketplace/category template and limits |
| Description | benefits and use cases without invention | prohibited elements and length |
| Specifications | structured facts | dictionaries and required fields |
| Variants | aligned SKU differences | rules for combining listings |
| Media | product matches the image | dimensions, order, rights, moderation |
Product Card Generation Pipeline
- Import data from ERP, PIM, 1C, or a spreadsheet.
- Map the internal category to the marketplace schema.
- Normalize units, colors, materials, and dictionaries.
- Check required fields and conflicts.
- Generate an evidence-bound prompt for each section.
- Get strictly structured JSON.
- Validate length, characters, claims, and consistency.
- Show the diff to the operator for risky or sampled SKUs.
- Upload a small batch through a supported interface.
- Collect moderation errors without automatically changing the original facts.
Each field must have source, generated_value, validation_status and a rule version. That makes it possible to see where the error came from and reprocess only the affected product cards.
How to Generate a Product Title
The title is built from the product type and several distinguishing attributes in the order allowed by the marketplace. It should answer the question “what is it,” not include the entire search cluster.
Safe template:
[product type] + [key feature] + [model/variant, if needed]
After generation, check length, repetition, brand, special characters, photo match, and category fit. As of the time of review, Wildberries lists a maximum title length of 60 characters and specifies undesirable elements; before bulk upload, recheck the current guidelines.
How to Write a Description
The description answers three questions: who the product is for, what problem it solves, and how to use it. It should not simply repeat the specifications table.
The LLM receives:
- the audience and use cases from the approved reference set;
- approved facts;
- terms that should be used naturally;
- a prohibition on new numbers, properties, and medical/legal promises;
- the marketplace format and character limit.
Then a separate validator compares all text entities against the source fields. The phrase “waterproof” is blocked if the only available value is “moisture-resistant.”
Specifications and Variants
Structured specifications matter more than literary style. They are filled from reference data and rules, while the LLM is used to map ambiguous names under a confidence threshold.
For variants, create a difference matrix. Shared fields are inherited from the family, while variable fields come from the SKU. You cannot carry over the color, volume, or contents of a neighboring variant just because the description is similar.
Useful checks:
- units of measure and valid ranges;
- color in the photo versus the attribute;
- total number of items in the package;
- mutually exclusive properties;
- duplicate SKUs and barcodes;
- required category documents.
Photos, Infographics, and Rich Content
AI can remove backgrounds, create compositions, and adapt sizes, but the product in the image must preserve its real properties. Control color, shape, set contents, labeling, and text on the packaging.
For each asset, store the original file, rights, editing operations, marketplace variants, and moderation result. A generative background must not add objects that a buyer would perceive as part of the set.
Automated Checks
Divide the rules into four classes:
- schema: types, required fields, dictionaries, length;
- consistency: title ↔ specifications ↔ photo ↔ variant;
- claims: prohibited claims or claims that require evidence;
- quality: duplicates, readability, spelling, natural keyword usage.
Hard rules block upload. Soft rules send the product card to review. An LLM judge can be used as an additional signal, but not as the only fact check.
Bulk Upload and Rollback
Do not start with the full catalog. Upload in batches and keep a snapshot of the previous version. For each batch, record:
- the SKU list and the ruleset version;
- generated JSON and the diff;
- who approved it;
- the API/import response and errors;
- the ability to restore previous values.
Start with a sandbox or drafts if the marketplace provides them. After a successful small batch, increase volume while keeping selective manual review and a stop condition if errors rise.
How to Measure Results
Useful metrics:
- share of SKUs with all required attributes completed;
- first successful moderation;
- import errors by type;
- time and cost per accepted product card;
- share of manual edits;
- returns and complaints due to description mismatch;
- visibility in filters and search queries;
- conversion only in a controlled comparison.
You cannot promise sales growth from description generation alone. Price, reviews, shipping, assortment, advertising, and competition all influence results at the same time.
Pilot Plan
Choose one category and a limited group of SKUs. Record the current completion rate, errors, time, sources, and acceptance criteria. Create a category schema, banned claims, and reference product cards. Generate drafts, check all hard fields and a manual sample, then upload a small batch. Only after reviewing moderation outcomes and returns should you apply the rules to the next category.
FAQ
Can AI fully create a product listing from a photo?
It can suggest a draft, but a photo does not confirm ingredients, size, compatibility, documents, or included items. Those fields must come from the catalog or be verified by a person.
Can one text work for both Ozon and Wildberries?
The core facts can be shared, but the title, description, characteristics, and media need to be built according to each marketplace’s and category’s specific rules.
How do you avoid fabricated attributes?
Allow generation only from a structured list of facts, require JSON, compare the response entities against the source, and block any new claims.
Do you need to add every keyword to the description?
No. Use search phrases naturally and follow the marketplace’s current rules. Characteristics and category are often more important than repeating words in the text.
How do you safely update thousands of listings?
Version the ruleset, run diffs, launch batches, keep the previous version, and stop the flow if moderation errors increase.
How AI Sunrise automates product listings
AI Sunrise starts with one category: it captures current metrics, data sources, marketplace constraints, and the acceptance criteria. Then the team can:
- connect ERP/PIM/1C to the Ozon and Wildberries schemas;
- set up generation of titles, descriptions, and media variants based on approved facts;
- add checks for attributes, claims, variants, rights, and quality;
- run a pilot upload, monitor errors, train the team, and hand off the procedures.
Conclusion
AI is useful in marketplace listings as a transformer of verified data, not as a source of product facts. The most important part of the project is a single source listing, category rules, and automated blocks.
Start with one category and a small batch. When the system shows the origin of each field, passes moderation, and can roll back, it can be scaled across the catalog.