La DoubleJ Product Data

Yes. Tableware and homeware sit in the same catalogue as apparel and are collected under the same schema with a category field. For this brand that matters — the same print running across a dress and a plate is the commercial idea, and splitting the two would break the analysis.

Print identity is preserved as an attribute rather than collapsed into a colour field. In a catalogue where the same silhouette ships in a dozen archival prints at the same price, print is the only thing that distinguishes the variants.

Into the systems you already run, rather than through an API you have to integrate against. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks can push price and availability changes as they are detected. A solutions engineer fixes the schema, cadence, and destination with you during onboarding, so the first delivery already matches your pipeline.

Maintaining the collector is our job, not yours. Every field is validated against expected types and historical ranges on each run, and a collector that starts producing anomalies is quarantined rather than allowed to emit bad records. We repair it upstream, and the schema we deliver to you does not move — which is the entire reason to buy this rather than run a scraper in-house.

We collect only publicly visible catalogue data — the prices, descriptions, images, and availability any shopper sees without logging in. We do not bypass authentication, we do not collect personal data, and we honour rate limits so the source is never disrupted. Our infrastructure is SOC 2 Type II certified, our processing is GDPR and CCPA compliant, and we sign DPAs as part of procurement.

Pricing is scoped per engagement, driven by catalogue size, refresh cadence, the number of locales you need, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample pulled from the live ladoublej.com catalogue, so you can check the data against your own benchmarks before committing to anything.