Manufacturers mine gold. They know their products down to the molecular level. A helmet manufacturer like Schuberth has 60+ distinct data fields for a single product: shell material composition, noise levels at specific speeds, ventilation airflow rates, visor mechanism tolerances, communication system compatibility. Laboratory-grade precision.
But to sell online, all that richness gets compressed into one generic description and copy-pasted to every channel. Google truncates it. Amazon rejects it. TikTok users scroll past it. Kaufland says "Nein." The manufacturer mines gold and smashes it into a generic brick.
The Tab Explosion
It's Tuesday night. You're on the couch. You've decided to buy a motorcycle helmet. The credit card is in your hand. You open the product page and it says: "Premium materials. High quality finish. Engineered for performance."
You pause. Is it quiet? Will your glasses fit? How does the weight compare to alternatives? The page says nothing specific. So you open a new tab. Reddit. Another tab. YouTube — a 10-minute review for one detail. "Is 1,640 grams heavy for a helmet?" "ECE 22.06 vs 22.05 difference." Fifteen tabs later, your browser is lagging, you're exhausted, and you're doing the job the retailer was supposed to do.
This is the Tab Explosion. Every e-commerce buyer recognizes it. It happens because the product page gives you numbers without context, claims without proof, and a description written for nobody in particular. The number "1,640 grams" means nothing to a non-expert. "1,640g — lighter than 72% of modular helmets" means everything.
One Size Fits None
A retailer has 5,000 SKUs. Each product has a title, a description, maybe some specs. This single piece of content is expected to work on the webshop (SEO meta titles, Schema.org markup), Google Shopping (de-hyped titles, category mapping), Amazon (200-char titles, 250-byte keywords), Facebook (scroll-stopping hooks), TikTok (entertainment-first scripts), and Kaufland (German-only, strict taxonomy).
Each platform has different character limits, different compliance rules, different algorithm priorities. The retailer does none of this. They copy-paste the same description everywhere. 80% of the data value is destroyed at the output stage.
The Cost
The economic waste is staggering. Globally, e-commerce companies spend an estimated $200 billion annually on product content — copywriting, photography, data entry, compliance checking, translation. Most of this spend produces content that fails on every platform it touches.
Manual copywriting costs $15-50 per product. For 10,000 products across 5 channels, that's $750K-$2.5M. And the content starts decaying the moment it's published — never updated, never validated, slowly rotting as products evolve and platforms change their rules.
The Root Cause
All of this points to the same root cause: product data is treated as a document, not as intelligence. A document is written once and copied everywhere. Intelligence is structured, verified, contextual, and adapts to its audience.
The shift from documents to intelligence requires verification (every fact confirmed by multiple sources), context (every spec positioned against the category), honesty (explicit about limitations), adaptation (different content for different channels), and structure (machine-readable data for AI agents).
This is what Central does. The 7-phase enrichment pipeline takes a basic CSV and transforms it into verified, confidence-scored product intelligence — 50-129 enriched fields per product, 67.6% multi-source validated — then the Channel Router delivers it perfectly formatted to every channel. Same gold, different shapes. No more bricks.