10 · AI automation · SEO · GEO · E-commerce
10,000 items — new copy, new images.
A reseller with around 10,000 items from dozens of brands barely ranked in search engines — because nearly all product copy and images had been taken over directly from the manufacturers.
Client project · client anonymized (confidentiality) · project from 2026
- AI text generation
- AI image generation
- Nano Banana Pro
- SEO
- GEO
- Custom development
- PHP middleware
- MariaDB
- 10,000
- items with completely new copy and images
- 0%
- duplicate content once the pipelines finished
- own check using a language model
- 2 pipelines
- text + image — generated automatically, reviewed before going live
01 · Problem
The client runs an online store as a reseller for around 50 well-known brands and carries approx. 10,000 items. As is common among resellers, product copy, short descriptions and images were taken over directly from the manufacturers — identical to thousands of other stores carrying the same brands. From an SEO perspective, a major problem: duplicate content on nearly every product page, hardly any relevance of its own for search engines, weak rankings despite a decent product range. On top of that, the store was not geared at all to the requirements of AI-powered search systems such as ChatGPT, Perplexity or Google AI Overviews — it was barely visible in AI search.
02 · Solution
vona built two fully automatic AI pipelines that rebuild the store's entire catalog independently of each other — and around the clock.
Pipeline 1 — copy: A PHP middleware reads each item from the store database, passes the product name, category and original manufacturer text to the AI and uses them to generate fully original, SEO-optimized product copy, short descriptions, meta descriptions and page titles — based on defined prompts that already incorporate tone of voice, keyword strategy and GEO requirements. All copy is written straight back into the store database.
Pipeline 2 — images: A second pipeline uses Nano Banana Pro to generate new, original product images based on the original product images — visually consistent, high-quality and no longer identical to the manufacturer assets. For every newly generated image, the pipeline automatically creates SEO-optimized alt text and assigns both to the respective item.
Review before publishing: copy and images do not go live unchecked. Every draft is reviewed before publishing – on the same principle we use to check the entries in our AI tools directory before they appear.
03 · Result
Both pipelines generate the new copy and images for the entire catalog of approx. 10,000 products automatically; each item is published after review. Once both runs were complete, our check using a language model found no item left with manufacturer-supplied copy or images. For the first time, the store has a content identity of its own — every product page is unique, relevant to search engines and geared to AI-powered search systems. Newly listed items pass through both pipelines and the same review — so they get their own copy and images too.
04 · Workflow
- Store database: approx. 10,000 items from around 50 brands
- PIPELINE 1 · COPY: Middleware reads (name, category, manufacturer text), AI rewrites (prompts with tone, keywords and GEO), Original copy (product text, description, meta description, title)
- PIPELINE 2 · IMAGES: Original images (from the manufacturers), Nano Banana Pro (new, original images), Image + alt text (SEO-optimized, assigned to the item)
- Unique items: no manufacturer copy, no manufacturer images
- NEW ITEMS PASS THROUGH BOTH PIPELINES AUTOMATICALLY
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