It is 10pm and you are pasting yet another supplier text into your shop, the same paragraph that sits word for word on forty other shops. Of your 12,000 products, half have a copied description like this, the other half a single line or nothing at all, and the attributes are only half filled in. And now you also want to move into Germany and Belgium.
You will never manage this by hand. "Just let the AI write it" sounds like the way out, but on this messy data AI writes equally messy copy. The real question is not how fast you produce text, but what the AI is working on. This piece is about that difference.
Product copy is your silent conversion killer
Poor product data costs you customers, and you barely notice it happening. The Akeneo B2C survey from 2025 (1,800 consumers, 8 countries) shows that 40% of buyers have returned an online product because of incorrect product information, and that 53% abandoned a purchase when the product data did not add up. In that same study, dissatisfaction with the completeness of product data more than doubled: from 13% in 2023 to 30% in 2025.
That is not a copy problem, it is a data quality problem that happens to surface in the copy. An empty size table, a missing material, a description that three other products also carry: it makes hesitating easy and buying hard.
Why "just letting the AI write it" solves nothing
The temptation is strong: hook a text generator up to your export and let it loose. But a language model working on half a record either invents the gaps or regurgitates the supplier text you wanted to get rid of in the first place. The bottleneck is not writing speed. It is the source.
At most shops the same data is scattered across a supplier feed, an old PIM export and the shop itself. One calls the field DIM_MM 620x820x450, the other "dimensions 62 x 82 x 45 cm". Colour appears as purple, lila and violet all mixed together. There are duplicate records of the same article. Fail to clean that up first, and the AI writes lovely sentences on top of a foundation that is out of true.
- AI writes on top of a half, duplicate record
- Gaps get invented or copied
- Colour appears as purple, lila and violet all mixed together
- Every source with its own field name and unit
- AI only writes after a clean golden record
- Missing attributes get filled in, not invented
- Values normalised to your structure
- One record per product, origin visible per field
That is why we reverse the order. SyncRefine first merges your sources into a clean record in your structure, the golden record, and only then enriches. You can read more about that approach in how you keep control per field and see the origin.
From copied supplier text to unique, findable AI product descriptions
Reusing an identical manufacturer text is not forbidden. Google has no "duplicate content penalty" for copying supplier copy, as John Mueller of Google has confirmed for years (Search Engine Journal). But identical descriptions do make it unclear to Google which page should rank and where the link authority should go. You are competing with forty shops over the exact same paragraph. You usually lose that to the party with the biggest domain.
Once the record is clean, AI writes a complete, unique description based on the real attributes: not rewritten supplier copy, but text that tells the facts of this product. That is immediately a better basis for unique product descriptions for SEO, because every page has something of its own to say.
Scale and control do not rule each other out. Quality comes from clean data plus human review, not from "just letting the AI write it".
Making it complete: filling in empty attributes and categories with AI
Descriptions are only half of it. The filters, the search and the comparison tables run on attributes, and those are often the worst filled in. From a clean source, AI can derive missing attributes, normalise values and place products in the right category. And it can look at the photos: from a product image it recognises colour, material or shape and queues that up as an attribute for review. How those four enrichment operations work exactly is written up separately.
That is how a half article becomes a complete article: title, short and long description, attributes, category and tags. Everything about this enrichment step is on AI enrichment: writing descriptions, filling in attributes and translating into 42 languages.
{"sku": "GM-4821","title": "Bornholm oak dining table, 160 cm","dimensions": "L 160 · W 90 · H 76 cm","colour": "Naturel eiken","material": "Massief eiken","category": "Dining tables","description": "Robust dining table in solid oak ...",// translated into 42 languages in context, you approve"translated": ["de", "fr", "en", "es", "..."]}
Click to switch: the same article, bare and enriched
Translating product copy with AI into 42 languages
Then the step into Germany and Belgium. The numbers here are stubborn. According to the CSA Research study "Can't Read, Won't Buy" (3rd edition, 8,709 consumers across 29 countries, 2020), 76% of online shoppers prefer to buy a product with information in their own language, and 40% never buy from websites in another language. Fail to localise the buying experience and you risk losing 40% or more of your total addressable market.
Once your golden record is clean, translating product copy with AI is no longer a separate project but a button. SyncRefine translates descriptions and attributes in context into 42 languages, keeping your trade jargon and your category structure intact. You are not translating loose sentences, but a complete, correct record.
Findability is copy and technology together
Good copy only helps on a page that loads fast. Core Web Vitals have been a confirmed part of Google's page-experience signals since June 2021 (Google Search Central). A "good" Largest Contentful Paint is under 2.5 seconds at the 75th percentile, INP under 200 ms and CLS under 0.1 (web.dev). And 53% of mobile visitors leave a page that takes longer than 3 seconds to load (widely cited Google/SOASTA study).
Heavy product photos are a creeping culprit there. A 4.2 MB HEIC photo on every product page adds up fast. That is why SyncRefine optimises images along the way: converting to WebP, correct dimensions, duplicate files removed. Copy and technology belong to the same job.
You keep control: origin per field, locking and approval
The danger of enrichment at scale is that you no longer know where a value came from. That is why SyncRefine shows the origin per field: did the dimension come from the supplier feed, from your old PIM or from the AI? You can lock fields you have corrected yourself, and nothing goes live before you approve it.
That way AI does not become a black box but an assistant that does the heavy lifting while you keep the final edit. Push the approved result back to your WooCommerce or Magento shop, and it sits online, synced in real time.
From one-off snippets to a repeatable process
The real difference is not that you write 12,000 texts once. It is that the next deliveries automatically follow the same route: merge, enrich, translate, approve, sync. That is how you produce product copy and translations at scale, synced in real time instead of starting over every time.
And you do not start from zero. SyncRefine reads your existing shop structure and builds on top of it. Tonight's messy export is the source from which the clean golden record will emerge.
- The bottleneck with product copy is the messy data underneath, not writing speed.
- AI only enriches after a clean golden record: then descriptions are complete, unique and findable.
- Translating into 42 languages is no longer a separate project, but a button on your clean record.
- You see the origin per field, lock and approve. AI learns your structure, not your writing style.
