Prepare labelled sets
Pin a labelled attribute set per category; decontaminate against the evaluation split.
Industry blueprint
Catalog enrichment
Marketplaces and retail. Extract and normalise product attributes across millions of SKUs, at a cost you approved first.
Catalogues hold tens of millions of SKUs with missing or inconsistent attributes, and no single source (title, description, image) is complete. Production teams distill a foundation model into a compact one, then run it as high-throughput batch with precision and recall measured on labelled sets. That is three shipped Cairn pipelines in a row, with the partner's schemas and prompts.
Illustrative pipeline
Distill a compact extractor
Distill the foundation model into a small one; accept only if precision and recall hold.
Run the catalogue
Batch inference over every SKU, priced before the first request, with the actual spend recorded.
Review and publish
Category owners review low-confidence attributes; the enriched dataset is versioned.
What the record keeps
The labelled sets, the distilled extractor and its scores, the approved estimate and actual spend, and the enriched dataset version.
Partner brings
- Product data and images
- Attribute schemas and prompts per category
- Connector to the catalogue or PIM
Cairn brings
- Distillation with a quality gate
- Batch inference with the cost gate
- Held-out precision and recall
- A versioned, recorded attribute dataset
Business value to test
Attribute coverage, extraction precision and recall, and cost per thousand SKUs.
Keep the decision explicit
A new extractor replaces the old one only after a measured comparison and an approval.
Close the feedback loop
Reviewer corrections become labels; new categories trigger a re-evaluation.
Reference design, not a shipped domain pack. The underlying pipelines (compress/distill, eval, serving/batch-inference, dataset_readiness) ship; the partner brings schemas, prompts and catalogue connectors.
See how failure can trigger a reviewed improvement cycle →Bring a catalogue with gaps.
Talk about your attribute schemas, SKU count and what a wrong attribute costs.