Enrich Product Data with AI: from raw supplier data to a sales-ready catalog
Every supplier delivers differently. AI-based enrichment fills the gaps automatically — and people review instead of typing. Here's how it works for multi-brand specialist retailers.
- ✓Every supplier delivers differently — the real problem is the combination of volume and inconsistency, not a single missing value.
- ✓AI Autofill fills missing attributes, writes descriptions, assigns categories and translates — in bulk across thousands of products.
- ✓Nothing is overwritten blindly: every AI value lands in a review queue with a robot icon, so the team reviews instead of typing.
- ✓Result: complete, channel-ready data — up to 95% more efficient data processing.
Every supplier delivers differently. One sends a 40-column spreadsheet, the next a PDF datasheet, a third a feed with half the attributes missing. For multi-brand specialist retailers, that's daily reality — and the reason product maintenance eats so much time: to sell online, a product has to be complete. Title, description, technical attributes, category, clean images, ideally in several languages. Done manually, that's minutes to hours per product. Across thousands of SKUs from dozens of brands, it becomes the bottleneck.
This is exactly where AI-based enrichment comes in: it fills the gaps automatically — and people review instead of typing.
What "enriching product data" actually involves
Enrichment is more than "writing a description." Complete, sales-ready product data has several layers:
- Attributes — material, dimensions, colour, fit, technical specs. They drive filters, search and the buying decision.
- Copy — titles and descriptions that fit the brand and are optimised for the channel.
- Categories — every product in the right place in the assortment.
- Images — cleanly cut out, channel-appropriate.
- Translations — the same quality in every target language.
Miss one layer and the product isn't truly sales-ready — it ranks worse, converts worse, or gets rejected by the marketplace.
Why manual enrichment slows retailers down
The core problem isn't a single missing value — it's the combination of volume and inconsistency. Every brand names attributes differently, delivers different completeness and a different format. Consolidating that by hand means spending most of your time on dull re-research and rewriting — work that's rarely documented and starts over with the next supplier update. That doesn't scale, and it ties up exactly the people who should be working on the assortment and the channels.
How AI-based enrichment works
In Productbay, enrichment runs as one continuous process — from raw data to a reviewed, channel-ready product.
1. Import from CSV, Excel, feed or PDF
Product data enters via CSV/Excel upload, a scheduled remote import (feed URL or FTP), or the API. Unmapped columns are preserved — the AI can use them as context later.
2. AI Autofill: fill missing attributes
AI Autofill is the core. It generates descriptions, assigns categories and fills missing attributes — based on all imported product information (including unmapped columns) and, when needed, whitelisted web sources. Productbay uses whichever LLM performs best (including Gemini, ChatGPT, Claude, Perplexity), swapping models continuously by result quality. The key point for retail: AI Autofill runs in bulk. Using filters (e.g. Brand = "Kappa"), you can select all matching products and enrich thousands at once.
3. Automatic categorization & normalization
Categorization is part of the same step: AI Autofill assigns products to the right category based on attributes, descriptions and images — no separate task.
4. Copy, translation and images
Descriptions are written in your brand voice, controlled via custom prompts and Golden Examples (sample outputs the AI learns style and structure from). Translation runs through the DeepL integration. Images can be cut out directly (clean white background) — even from lifestyle or supplier photos.
5. A review queue, not a leap of faith
Productbay never overwrites blindly. Every AI value lands in a review queue: approve or discard individually or in bulk, export to Excel first if you like. Every AI-filled attribute is marked with a robot icon. Control stays with the team; the AI does the grunt work.
AI enrichment vs. manual vs. classic PIM
| Manual (Excel) | Classic PIM without AI automation | Productbay (AI-native) | |
|---|---|---|---|
| Fill missing attributes | by hand, per product | structured storage, manual fill | AI Autofill, in bulk |
| Multiple supplier formats | copy-paste | import + mapping | import + normalization + enrichment |
| Copy & translation | write it yourself | manual / extra tools | AI copy + DeepL |
| Control | no audit trail | approvals vary by system | review queue with robot icon |
| Scaling | breaks with volume | depends on resources | thousands of products at once |
The "classic PIM" column describes the approach without AI automation, not a specific product. For a vendor-by-vendor comparison, see our PIM comparison.
The outcome: completeness, conversion, time-to-market
Complete data isn't an end in itself. It ranks better, converts better and passes marketplace checks without rejections. Above all, the time from supplier feed to a live product drops sharply — Productbay increases data-processing efficiency by up to 95%. The time that used to go into re-research goes back into the assortment.
How to start
- Import supplier data (CSV/Excel, feed/FTP or API).
- Run AI Autofill over a filtered selection.
- Review and approve results in the review queue.
- Publish to shop, marketplace and ERP.