One Platform to Research, Enrich and Validate Product Data
ENRIVAQ completes the product card, not only the attribute table.
ENRIVAQ connects product research, structured technical enrichment, validation, SEO keyword discovery, unique factual content, SEO metadata and multilingual final output in one workflow for complex technical catalogs.
The result is a publication-ready product-card package that can move back into the systems your business already uses.
SKU · MPN · title · attributesmanufacturer · technical · distributorattribute · value · unitkeywords · title · description · meta · languagevalidated · review · missingPIM · ecommerce · file workflowREFERENCE RECORD · VERIFIED PUBLIC PRODUCT
The platform starts with the product data available to you, builds the validated technical record, discovers relevant search terminology and turns the result into complete product-card content and metadata for downstream systems.
Find information beyond the fields you already have
Incomplete data should not limit the final product record. The Research Engine uses available product identifiers and context to find relevant external information about the product.
This can help catalog teams move away from the repeated manual process of searching for every missing specification one SKU at a time.
The output of research is not treated as final truth. It becomes input for extraction and validation.
Turn product information into structured attributes
Useful product information may exist inside descriptions, specification tables or other unstructured content.
The Extraction Engine converts that information into structured fields.
The data can then be mapped into the product structure required by your catalog instead of remaining trapped inside text.
The objective is controlled enrichment, not blind automation.
Turn the validated record into the complete product card
Descriptions should be the result of reliable product data, not a substitute for it.
Once the record has been structured and validated, ENRIVAQ builds the SEO keyword set and prepares the product title, unique description, technical content, meta title, meta description and final output in the configured target language.
Validated facts first.
Complete product card next.
This data-first approach keeps content tied to the product information available in the record.
This shifts catalog teams away from researching every product manually and toward resolving the cases where human judgment actually adds value.
Apply the same process across a catalog
The value of automation appears when the same data rules can be applied consistently across many products. The platform is designed around catalog workflows rather than isolated one-off prompts.
Products can move through common stages while exceptions remain visible for review.
CATALOG WORKFLOW · REFERENCE STATE
Batch-oriented catalog processing
Product data remains a business asset
Product-data enrichment may involve internal catalog records, supplier data and technical information.
The platform therefore needs clear rules for data processing, system access and any external AI processing.
Detailed security information is published on the dedicated Security and AI & Data Policy pages.
Interface shown with representative product data.
See the platform on your own catalog
The most useful demonstration starts with a product record you already work with.