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Independent & artisan

K.Jacques Product Data

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

source specification

Source
kjacques.fr
Segment
Independent & artisan
Origin
France — Saint-Tropez, since 1933
Categories
Sandals, Leather footwear
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily

about the source

What K.Jacques sells, and how its catalogue behaves

K.Jacques has made leather sandals in Saint-Tropez since 1933, and its core styles have barely changed in decades. The catalogue is built on a small set of enduring silhouettes offered across many leathers and colours, which produces a wide variant grid over a narrow style count.

why teams track it

Why K.Jacques data is worth having

K.Jacques is the reference for artisanal French leather sandals and one of the few catalogues where the same style can be priced continuously across half a century. Its variant-heavy structure also makes it a useful test of whether a data model handles variant depth properly.

Visit kjacques.fr

collection method

How we collect K.Jacques

K.Jacques is collected through collection pages combined with variant data, so the leather and size options behind each style are captured. 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 kjacques.fr'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 K.Jacques 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 K.Jacques

  • Deep variant grid across leathers and colours per style
  • Leather type as an explicit attribute
  • Stable style identifiers persisting across many years
  • Size run coverage per variant

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.

Variants are captured individually rather than collapsed into the parent style. K.Jacques offers a small number of silhouettes in a very large number of leather and colour combinations, so a style-level count would understate the assortment by an order of magnitude.
Yes, and it is one of the more interesting properties of this source. The core silhouettes persist under stable identifiers year after year, so a scheduled collection produces a genuine long-run price series on an unchanged product.
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 kjacques.fr 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

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