logoPandorLabs
Contemporary & direct-to-consumer

Madewell Product Data

Prices, variants, availability, and images from madewell.com — normalised, validated, and delivered into your stack. No collector to build, and none to maintain.

source specification

Source
madewell.com
Segment
Contemporary & direct-to-consumer
Origin
United States — denim-led, J.Crew Group
Categories
Denim, Womenswear, Menswear, Shoes, Bags
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily

about the source

What Madewell sells, and how its catalogue behaves

Madewell is a denim-first American label, sister to J.Crew, whose catalogue is organised around fit names and washes more than around seasonal collections. Jeans persist under stable fit identifiers for years while washes rotate constantly, which produces a very particular data shape.

why teams track it

Why Madewell data is worth having

Denim is one of the few apparel categories where fit is a durable, comparable attribute across brands, and Madewell is the US contemporary benchmark for it. Merchandisers use the feed to track which fits get expanded or retired and how wash rotation drives markdown behaviour.

Visit madewell.com

collection method

How we collect Madewell

Madewell is collected through listing and product page collection, with fit and wash attributes pulled out of the denim product detail. Every source gets a dedicated collector rather than a generic crawler, because the field detail that makes this data useful only survives if the extraction is built for the site it runs against.

Structured at the source

Records are read from madewell.com's own structured responses wherever they exist, rather than reconstructed from page markup. That keeps the feed stable across visual redesigns.

Validated every run

Each field is checked against expected types and historical ranges. A collector producing anomalies is quarantined and repaired upstream instead of emitting bad prices into your pipeline.

Normalised to one schema

Every brand in the catalogue lands on the same schema, so adding a source is a configuration change on your side rather than another integration to write.

15min
Fastest refresh
99.9%
Uptime SLA
3
Delivery formats
49+
Fashion sources

what you get back

Fields in the Madewell feed

A normalised core that is identical across every source, plus the attributes that are specific to this one.

Standard across every source

  • Product name, brand, and source URL
  • Current price, original price, and currency
  • Category and subcategory as the source classifies them
  • Colour, size, and variant availability
  • Product images, deduplicated across variants
  • Description, composition, and care text
  • Collection timestamp on every record

Specific to Madewell

  • Fit and wash attributes extracted from denim product detail
  • Stable fit identifiers persisting across seasons
  • Inseam and size run coverage where published
  • List and promotional price captured separately

SOC 2 Type II

Audited controls across security, availability, and confidentiality. Report available under NDA.

GDPR & CCPA

Public catalogue data only. No personal data collected, and a DPA is available on request.

99.9% Uptime SLA

Contractual availability with monitored collectors and a public status page.

Data residency

Choose EU or US processing and storage regions to match your obligations.

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

Yes, and that separation is the point of this source. Fit is durable and comparable across brands while wash rotates seasonally, so keeping them in distinct fields lets you track fit lifecycle and wash turnover independently instead of treating every new wash as a new product.
Yes. Both run on the same schema, so we can deliver a combined J.Crew Group feed with a brand field or two separate feeds, whichever fits your pipeline. The combined form is the usual choice when the goal is group-level promotional analysis.
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 madewell.com catalogue, so you can check the data against your own benchmarks before committing to anything.

Still have questions?

Talk to an engineer

Ready to Get Started?

Talk to us about your sources and volume. We'll return a sample dataset from your target sites before you commit to anything.

SOC 2 Type II
GDPR & CCPA compliant
99.9% uptime SLA

© 2026 PandorLabs, Inc. All rights reserved.