// PRODUCT DATA
Product Data Enrichment and Automated Data Quality
Incomplete raw data is a time-to-market killer. You have product data, but it is incomplete, inconsistent, or arriving in ten different formats from ten different suppliers. Xanevo refines that data with AI: from unstructured sources like PDFs, images, or supplier feeds, we extract clean attributes and fill your PIM fields automatically.
98 % attribute completeness. 2 days instead of 4 weeks. 10x more SKUs per employee
// DEVELOPING PRODUCTION-READY AI SOLUTIONS FOR
// DEFINITION
How does AI-driven enrichment work?
The AI analyzes unstructured sources like product descriptions, PDFs, or supplier feeds, identifies technical attributes inside them like color, material, dimensions, or weight, and writes them into the correct PIM fields automatically. From a sentence like "This durable sneaker made of red leather weighs only 300 g" the system extracts four clean attributes: category sneaker, color red, material leather, weight 300 g.
Data quality determines discoverability, conversion, and return rates. Customers filter shops by attributes. If material is missing, the product never shows up in the material filter, even if the information is technically available. Product data enrichment is not busy work, it is conversion optimization with a direct revenue lever.
The enrichment approach is source-agnostic and target-agnostic at once. We enrich PIM data, process supplier feeds in any format, parse unstructured PDFs, and handle data from crawling sources. The output can be optimized for marketplaces (Amazon A+, Idealo, price comparison sites), your own shop, or print catalogs in parallel. From varied raw data, a single golden record emerges.
The old way vs. the new way
The old way (manual data work)
Data is entered, copied, and verified. By people.
- Supplier data arrives in Excel, PDF, CSV: someone normalizes it manually.
- Product descriptions are retyped word by word, or copy-pasted.
- Classification follows gut feeling, without consistent taxonomy.
- Missing fields stay empty and reduce discoverability in the shop.
- Scaling means more people. Linear, not efficient.
The new way (AI-driven enrichment)
From unstructured to structured data. Automatically.
- Supplier data in any format is normalized automatically.
- AI extracts attributes from text, PDFs, and even product images.
- Classification follows defined taxonomies like ETIM, ECLASS, or client-specific schemes.
- Completeness rates above 98 % instead of 40 % with manual work.
- Scaling means more volume without more headcount.
Our tools for perfect data
Data quality is not a single-method question. Depending on starting point and target system, you need different tools. These eight modules cover the typical enrichment scenarios and can be combined modularly.
Data Ingest
Automatically capture and normalize supplier data in any format (Excel, CSV, PDF, JSON, EDI) before it reaches the PIM.
Ontology and Taxonomy
Automatically classify products into standardized schemes: ETIM, ECLASS, GS1, or client-specific taxonomies.
OCR and Document Processing
Extract clean product information from unstructured PDFs, catalogs, and datasheets.
Attribute Extraction
Extract individual attributes from running text, images, and technical specs, then structure them into PIM fields.
Content Generation
Automatically generate product descriptions, SEO copy, and category texts in your brand voice with no scaling limit.
Data Governance
Quality rules, validation, and approval workflows so enrichment stays clean in production.
Multi-Channel Export
The same data prepared differently for shop, marketplaces, and print catalogs, with no duplicate storage.
Which starting point fits your situation?
Data quality projects do not all start the same way. Depending on where you stand today, a different entry point makes sense. Three engagement models, clearly defined by starting position and outcome.
Tier 1
Data Quality Audit
We do not know yet what is possible.
For you if:
You sense that data quality is a bottleneck but you do not yet have a clear picture of the lever or the next steps.
What you get:
A structured analysis of your data landscape, identification of the biggest levers, and a recommendation for the right entry point. The outcome is a concrete roadmap, not a generic report.
Effort:
2 weeks, defined scope
Outcome:
Decision basis for the right next project
Tier 2
Enrichment Project
We know the lever and want to execute.
For you if:
You know which data you need where, and you want to reach the first measurable outcome. Classic use case: enriching one product catalog from the ground up.
What you get:
Defined project with a clear scope: specific product groups, specific attributes, specific target systems. Build-out of the enrichment pipeline together with your teams.
Effort:
8 to 12 weeks, defined outcome
Outcome:
Productive solution, measurable data quality, documented process
Tier 3
Managed Enrichment
We need this as a continuous service.
For you if:
Enrichment is not a one-time project but an ongoing need: new assortments, new suppliers, new channels keep coming in.
What you get:
Continuous AI pipeline that we set up and operate. You feed in new data sources, we make sure clean enriched data lands in your PIM.
Effort:
Ongoing service, monthly retainer model
Outcome:
Data quality as an operating system, not a project
Frequently Asked Questions
Both. Computer Vision lets us recognize attributes like color, material, shape, or cut directly from product images. This is especially valuable when existing text lacks detail or when images are the primary data source, such as in fashion or home textiles.
The common standards (ETIM, ECLASS, GS1) are natively supported. Beyond that, we can map to client-specific taxonomies and to marketplace requirements (Amazon A+, Idealo). Which standard is right for you depends on your sales channels and target markets.
If the AI cannot extract an attribute with high confidence, the field is not guessed. Instead the item is flagged as "missing value" and routed to the human-in-the-loop process. Your team reviews only the critical 10 to 20 % of cases instead of maintaining 100 %. This prevents data garbage and preserves trust in the AI output.
// READY?
Turn raw data into competitive advantage.
Data quality is measurable, and that means it is improvable. Let us check where your biggest enrichment levers are and which entry point fits your situation.