PRODUCT DATA ENRICHMENT

Fill the gaps in
your own catalog.

"Half our product records are missing specs, images or descriptions."

// THE SHORT ANSWER

Product data enrichment fills the gaps in your own catalog using web-sourced data — missing specifications, images, descriptions, attributes and categorization pulled from public sources, matched to your SKUs, and delivered in your schema. iWeb Data Scraping turns incomplete, inconsistent product records into complete, standardized ones, so your e-commerce listings convert better, your search works, and your data is ready for AI applications that depend on structured attributes.

Get free sample in 48h How it works
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Gap-fill — specs, images, attributes, copy
  • Your schema — normalized to your structure
  • AI-ready — structured attributes for ML/search

Incomplete product data quietly costs money everywhere: listings with missing specs don't convert, products without proper attributes don't surface in filtered search, and AI features can't reason over records that lack structure. Most catalogs are riddled with these gaps — inherited from suppliers, accumulated over years — and filling them manually is endless. Enrichment automates it from public web data.

We match your incomplete records against public product data across the web, extract the missing fields — specs, images, descriptions, attributes, categorization — normalize them to your schema, and return a complete, consistent catalog. It's the inverse of our other services: instead of monitoring competitors, we improve your data, using the same extraction and AI parsing pipelines that make it accurate.

THE POINT

The gap isn’t knowing this matters — it’s seeing it in time to act. That’s what the feed is for.

Gap-fillspecs, images, attributes, copy
Your schemanormalized to your structure
AI-readystructured attributes for ML/search
WHAT YOU GET

Complete records,
from partial ones.

Missing-field completion

Specs, dimensions, materials, descriptions and attributes filled from public product data.

Image sourcing

Additional and higher-quality product images matched to your SKUs where yours are missing or poor.

Attribute standardization

Inconsistent attributes normalized to your taxonomy — 'red', 'crimson', 'RED' become one value.

Categorization & tagging

Products classified into your category tree and tagged with searchable attributes.

Deduplication & matching

Duplicate and variant records identified and resolved; enrichment matched to the right SKU.

Schema-native delivery

Enriched data returned in your exact schema — ready to load, not to reformat.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

Enrichment output — gaps filled, normalized to your schema.
your_sku field_filled before after source_match confidence
P-1001 specifications (empty) Material: cotton; GSM: 180 UPC match 0.98
P-1001 image_url (empty) cdn/.../p1001_main.jpg UPC match 0.98
P-1002 color Red red (normalized) internal 1.00
P-1003 category (empty) Apparel > Men > Tees title match 0.91
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
HOW IT WORKS

From question to answer.

STEP 1

Send your catalog & schema

We take your product records, your schema and the fields you need completed.

STEP 2

Match & extract

Records matched against public product data; missing fields extracted and validated.

STEP 3

Return it complete

Enriched, normalized data delivered in your schema, ready to load into your systems.

WHY BUY VS BUILD

The details in the feed.

INPUT Your partial catalog + target schema + fields to complete
FILLED Specs, images, descriptions, attributes, categorization
QUALITY Matched to correct SKU, validated, deduplicated
OUTPUT Your exact schema — CSV, JSON, feed or warehouse-ready
SCALE From thousands to millions of records
RELATED:Managed Web Scraping →AI-Powered Scraping →AI-ready data guide →
FAQ

Before the first call.

Product data enrichment fills the gaps in your own product catalog using web-sourced data — completing missing specifications, images, descriptions, attributes and categorization, matched to your SKUs and delivered in your schema. It transforms incomplete, inconsistent records into complete, standardized ones ready for e-commerce, search and AI applications.

We match your records against public product data using identifiers (UPC, EAN, MPN, ISBN), plus titles, brand, attributes and images where identifiers are missing. Each enrichment is validated before it's attached, so a filled field goes to the right SKU — matching accuracy is the whole game in enrichment, and we treat it as such.

Yes — enriched data is returned in your exact schema and taxonomy, normalized to your attribute values, so it loads directly into your PIM, e-commerce platform or warehouse without reformatting. We adapt to your structure rather than imposing ours; the deliverable is ready to use, not another integration project.

AI features — search, recommendations, agents, chatbots — reason over structured attributes. Records missing specs or with inconsistent attributes are invisible or misinterpreted by these systems, just as they underperform in filtered search and convert worse for shoppers. Enrichment makes your catalog machine-usable, which increasingly determines whether AI-driven surfaces can work with your products at all.

Get a free sample dataset in 48 hours.