Web Scraping Collects Pages. Enrichment Builds Product Records.
Web scraping and product data enrichment can both involve external sources, but they are not the same task. Scraping collects information from pages. Enrichment determines which product the information belongs to, turns useful facts into a target schema and validates the resulting record.
ENRIVAQ continues beyond structured extraction: after validation, the same record can become SEO keyword targets, unique product content, meta title, meta description and localized product-page output.
Scraping can be an input to enrichment. It is not the entire enrichment workflow.
Retrieved page fragments
<h1>Product title<td>47 mm<span>0.2 kg<table>Specification rowsUsable product record
identityMATCHEDouter_diameter47 mmweight0.2 kgschemaMAPPEDvalidationCHECKEDONE CAN FEED THE OTHEROne retrieves content, the other produces a record
Both categories touch external sources. What they hand back to the catalog is different.
Scraping is a data-collection step
At category level, scraping means retrieving information from web pages or other online sources. It can collect text, tables or fields for later processing.
The approved source does not define a scraping implementation for ENRIVAQ, so this comparison stays conceptual rather than claiming a specific crawler architecture.
SOURCE
WORLD
Enrichment turns source information into a usable product record
Enrichment adds product identification, source relevance, structured extraction, normalization and validation around the collected information.
Its output is intended for a product-data workflow rather than being a raw page dump.
Collected HTML does not automatically mean product knowledge
A page may contain a value, but the workflow still needs to know whether the page describes the correct product, variant or model.
That context is what separates raw collection from a reliable product record.
Source discovery →Questions still open
UNKNOWNUNKNOWNUNKNOWNUNKNOWNIdentify the product before trusting the source
Identifiers, manufacturer context and existing attributes can be used to establish whether a candidate source is relevant.
Match before extract
identifier_matchYESmanufacturer_matchYESattribute_contextCONSISTENTsource_relevanceACCEPTEDConvert source values into the target catalog language
External pages may use different attribute names, units and formats. Enrichment maps those values into the target schema instead of preserving every source convention.
Outer ØOuter diameterNAME MAP4.7 cm47 mmUNIT NORMALIZEWeight 200 g0.2 kgUNIT NORMALIZEMaterial: steelSteelVALUE STANDARDIZETARGET SCHEMA DRIVES OUTPUTConflict stays open until resolved
candidate47 mmconfidenceREVIEWpublishBLOCKEDConflicts need a decision, not another scrape
If two sources disagree, collecting more HTML does not resolve the issue automatically. The record needs validation or review.
That is why source conflicts are treated as a separate stage in the product-data workflow.
Product data validation →The output should be fields the catalog can use
The chain ends in a typed value with a unit and a validation status, not in a saved page.
attributeouter_diametervalue47unitmmstatusVALIDATEDCompare collection with product-data transformation
Scraping is one step. Enrichment is the sequence that turns that step into a record.
Collection can sit inside a broader enrichment pipeline
Source collection is the first stage. Everything after it is what makes the result usable.
Collect
Retrieve page content or source material.
Identify
Confirm which product the source belongs to.
Extract
Turn source material into typed product facts.
Normalize
Map names, units and values to the target schema.
Validate + output
Resolve uncertainty and return usable product data.
CLAIM CONTROLShow us the output your catalog actually needs
Send the target schema and one product. That defines what has to come back as a field, not as a page.