The threshold is yours
You decide what auto-applies. Starting conservative and relaxing it as accuracy is measured is the whole method.
A confident mess is worse than a gap.
Catalogue AI is easy to demo and hard to trust. Every mapping carries a confidence score against a threshold you set — above it applies automatically, below it goes to a merchandiser rather than being forced into the nearest category.
Across suppliers, formats and languages.
Confidence
Scored by default
Multilingual
Product data
Catalogue-scale
Throughput
The picture
Mapped with a threshold.
Every mapping carries a score you set — above it applies automatically, below it goes to a merchandiser rather than being forced into the nearest category.
Where catalogue AI goes wrong
The long tail is the whole problem.
Select an area for how it is handled at catalogue scale rather than on a clean sample.
Detailed view
Accuracy on the common case is table stakes.
Every vendor maps a clean product title. What decides a catalogue is the tail — ambiguous, abbreviated and multi-language titles that get mapped confidently and wrongly at a scale nobody reviews.
- Confidence scored per mapping
- Threshold you set decides what auto-applies
- Ambiguous items routed, not force-fitted
- Abbreviations and supplier shorthand handled
- Multi-language titles read natively
- Long-tail accuracy measured separately
Detailed view
Yesterday's correct mapping is today's wrong one.
Taxonomies change with season, range and merchandising strategy. Mappings are versioned so a taxonomy change is a reviewable event rather than a silent rewrite of catalogue history.
- Mappings versioned against taxonomy revisions
- Re-mapping proposed, not applied silently
- Impact of a taxonomy change previewed
- History preserved for reporting continuity
- Bulk review tooling for merchandisers
- Rollback of a bad taxonomy change
Detailed view
Supplier data does not arrive in one language.
Product data is read in the language it arrives in rather than translated first, because a translation step is where attribute detail — sizes, materials, pack quantities — quietly disappears.
- Native reading across supplier languages
- Attributes preserved rather than translated away
- Mixed-language fields handled
- Units and pack quantities normalised
- Locale-specific naming retained
- No per-language pipeline to maintain
Detailed view
Search that understands the product.
Attributes buried in unstructured descriptions are what shoppers actually search on. Retrieval over the full product record beats keyword matching against a title field.
- Retrieval over descriptions, not just titles
- Attribute extraction feeding facets
- Synonyms learned from real queries
- Zero-result queries surfaced as a signal
- Relevance measurable, not anecdotal
- Personalisation without hand-built rules
Detailed view
Accuracy on the common case is table stakes.
Every vendor maps a clean product title. What decides a catalogue is the tail — ambiguous, abbreviated and multi-language titles that get mapped confidently and wrongly at a scale nobody reviews.
- Confidence scored per mapping
- Threshold you set decides what auto-applies
- Ambiguous items routed, not force-fitted
- Abbreviations and supplier shorthand handled
- Multi-language titles read natively
- Long-tail accuracy measured separately
This is for you
This is for you if
- Supplier feeds arrive in formats no two of which agree
- Taxonomy drift means yesterday's mapping is wrong today
- Product data spans languages your team does not all read
- Search returns the catalogue rather than the product
If none of these are true we are probably not the right call yet, and we will say so. The suppliers whose titles nobody can parse and whose categories never line up. Describe that on a call and we can tell you quickly whether this is worth your time — no data, no feed, no NDA needed to have the conversation.
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