How AI Can Enrich Complex Technical Product Catalogs
Technical catalogs contain more than names and descriptions. They can depend on identifiers, dimensions, materials, compatibility, product families and category-specific attribute sets.
For ENRIVAQ, enrichment means completing the usable card: validated technical attributes are combined with SEO keyword targets, unique factual product content, SEO metadata and target-language output rather than being delivered as attributes alone.
The challenge is therefore both semantic and operational: identify the correct item, collect the right evidence, structure it consistently and prevent unsupported values from entering the catalog.
{{ c.title }}
{{ c.text }} {{ c.tag }}A prompt is not a product-data process
A generic LLM prompt is not a full product-data process. It may not know which source should be trusted, which product variant is correct, which attributes belong to the category or whether a plausible technical value is supported.
That is why the site positions ENRIVAQ around research + structured extraction + validation + content, not description generation alone.
Research, extraction, normalization, classification, validation, content
Each stage produces input for the next one. Nothing becomes catalog data until validation accepts it.