RETAIL & E-COMMERCE Delivered

Catalogue-Wide Brand Compliance Cleanup

Rights holders required an online retailer to remove every reference to specific brands across roughly ten thousand products — in titles, descriptions, model fields and metadata, including transliterations. We built tooling that finds every occurrence, reports it for review and applies approved replacements safely. The full catalogue pass takes fifteen minutes.

10k
products processed
15 min
full catalogue pass

The problem we were asked to solve

Rights holders required an online retailer to remove every reference to specific brands from its product catalogue. This sounds like a search-and-replace and is not. Brand names appear in product titles, descriptions, model fields, compatibility notes and SEO metadata; they appear in transliterated and abbreviated forms; and some occurrences are legitimate compatibility statements that must survive while others must go. Across roughly ten thousand products, doing this by hand would have taken weeks of editor time and still missed occurrences — and a missed occurrence carries legal exposure, not just an untidy listing. A rerun had to be cheap, because compliance requests of this kind arrive more than once.

What we built

01

Catalogue export analysis

The full OpenCart catalogue is exported to CSV and scanned across every text-bearing field — titles, descriptions, models, attributes — rather than only the obvious ones, which is where hand-run cleanups typically leave residue.

02

Pattern-based detection

Detection covers spelling variants, transliterations and abbreviated forms, with matches reported for review before anything is written back. The reviewer sees the full inventory of hits instead of discovering them one at a time.

03

Safe bulk replacement

Approved replacements are applied back to the OpenCart catalogue as a controlled batch, preserving surrounding text and formatting so that descriptions remain readable after the brand reference is removed.

04

Repeatable tooling

The cleanup ships as reusable tooling rather than a one-off script run, so the next compliance request is a configuration change and another fifteen-minute pass.

Python CSV processing PHP OpenCart
Cleanup tool after a dry run: blocked-brand rules with spelling and transliteration variants, fields scanned, and a table of matches showing the proposed replacement and the decision for each occurrence
The cleanup tool after a dry run — every match listed before anything is written back. Reproduced with anonymised data.

What changed for the client

A cleanup estimated in weeks of manual editing completed in a fifteen-minute pass over roughly ten thousand products, with a reviewable list of every match rather than a silent bulk edit. The retailer met the rights holders' requirement with an auditable record of what changed, and retains the tooling to satisfy the next request at effectively zero cost. The engagement is complete.

  • A cleanup estimated in weeks of editor time completes in a fifteen-minute pass over roughly ten thousand products.
  • Detection covers spelling variants, transliterations and abbreviations across every text-bearing field, which is where hand-run cleanups typically leave residue and legal exposure.
  • Matches are reported for review before anything is written back, so the retailer has an auditable record of exactly what changed.
  • The tooling is reusable, so the next rights-holder request is a configuration change rather than another project.

Want similar results?

Tell us what the process looks like today and we will tell you what can be automated — and what should not be.

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