Product Data Optimization & Content Enrichment
Search engines, marketplaces and answer engines all read the same thing: your attributes. Incomplete data is the single most common reason good products stay invisible.
The problem
Your catalogue is competing with its own missing fields
Attribute coverage below about 70% breaks three things at once: filters return the wrong sets, feeds get disapproved, and AI answer engines skip the product because they cannot verify a claim about it.
It is unglamorous work, which is exactly why nobody has done it. It is also the work with the shortest path between effort and revenue on most accounts we audit.
What you actually get
Every engagement ships these. No line items you cannot point at.
Taxonomy rebuild
A category and attribute model that matches how buyers search, not how your ERP happens to be organised.
AI-assisted enrichment
Attributes extracted from spec sheets, images and supplier docs, then human-reviewed before they touch production.
Coverage scoring
Per-category completeness scoring so you can see exactly which parts of the catalogue are still costing you.
Copy at scale
Titles and descriptions generated against a house spec and claim-mapped to source data, reviewed by an editor.
Typical outcomes
0%
Attribute coverage achieved on median project
+0%
Of AI-citation gains traced to data work alone
−0%
Fewer feed disapprovals post-enrichment
How we run it, week by week
Coverage audit
Field-level completeness and consistency scored across the whole catalogue, by category and by revenue.
Taxonomy design
Attribute model agreed with merchandising, including the fields you can realistically maintain.
Enrichment run
Extraction, generation and human review in batches, highest-revenue categories first.
Intake rules
New-product requirements documented so the catalogue stops degrading the moment we leave.
Stack we work in
Platform-agnostic. We work in whatever you already own and tell you honestly when it is the constraint.
Questions buyers ask us
Not in this pipeline. Every generated attribute is mapped back to a source document, and anything without a source is flagged for a human rather than published.