AI catalog enrichment
AI catalog enrichment is the use of machine learning and language models to complete an entire product catalog at once — extracting attribute values from supplier documents, classifying SKUs into a taxonomy, normalising units and vocabulary across suppliers, and generating structured content — rather than enriching records one at a time by hand. Its defining requirement is consistency at volume: being correct across hundreds of thousands of records and being able to show where each value came from.
The pipeline
Catalog-scale enrichment is a sequence, not a single model call:
- Schema definition — decide the attribute set each category actually requires, with types, units and allowed values. Everything downstream is measured against this.
- Source acquisition — gather supplier spec sheets, manufacturer pages, PDFs, images and existing records for each SKU.
- Extraction — pull candidate values from those sources, including from scanned documents and tables.
- Normalisation — reconcile that
1/2",0.5 inand12.7 mmare one value, and that four suppliers' colour vocabularies collapse into one facet. - Classification — place each SKU in the taxonomy, and map to external taxonomies such as UNSPSC or a marketplace's tree.
- Validation and confidence — check against type rules and category norms, flag outliers, and record a confidence and a source per value.
- Write-back — push accepted values into the PIM or commerce platform, which is where projects most often stall.
The interesting engineering is in steps 4 and 6. Extraction is close to a solved problem; being consistent across suppliers and being able to justify a value are not.
Provenance is the difference between enrichment and guessing
The single most useful question to ask any vendor in this category is: for a value you produced, where did it come from?
A system that can answer with a document, a page and a snippet is doing extraction. A system that cannot is doing generation, and generation over missing data is invention with a confidence score attached.
This matters commercially rather than philosophically. Sooner or later a supplier will dispute a specification, a customer will return a part, or a compliance team will ask how a certification flag was set. Values that cannot be sourced become values somebody has to re-verify by hand, which is the work the project was supposed to remove.
Why it is a practice rather than a project
Catalogs do not hold still. Suppliers revise specifications, new SKUs arrive weekly, certifications lapse, taxonomies get restructured, and channels change their required fields.
A one-time backfill therefore produces a completeness number that peaks on the day it lands and decays from that point. Teams who treat enrichment as a standing capability — a defined standard, a pipeline that runs continuously, and a fill-rate metric reviewed like any other operational number — hold the gains. Teams who treat it as a project run the same project again in two years, usually with a different vendor and the same result.
Frequently asked questions
How accurate is AI catalog enrichment?
Accuracy depends on grounding rather than on model choice. Values extracted from manufacturer documents are reliable and independently checkable; values generated without a source are not. Evaluate by pulling a random sample of produced values and verifying each against the source the system cites.
How long does it take to enrich a large catalog?
The processing is rarely the constraint. Schema definition, source acquisition for long-tail SKUs, and write-back integration determine the timeline. A first production category commonly lands in weeks; a full catalog is a rolling programme sequenced by revenue rather than a single delivery date.
Can we do this with a general-purpose LLM ourselves?
Prototyping with one is worthwhile and cheap, and it teaches you what good output looks like in your categories. What does not survive the jump to production is cross-supplier consistency, unit normalisation, provenance, taxonomy discipline, handling of scanned and malformed sources, and a reliable write-back path into the system of record.
What should be enriched first?
The categories where incompleteness costs the most, which is usually a small share of SKUs. Rank categories by revenue and by null-result search rate, take the intersection, and start there. Starting with the easiest category produces a fast result that does not change any business number.