We extract Freestyle inventory, brand catalogues, pricing signals, and style metadata from Stitch Fix. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Freestyle Inventory objects from stitchfix.com. All fields typed and schema-versioned.
"product_id": "SF-994821", "brand": "Madewell", "product_name": "High-Rise Skinny Jeans", "category": "Women > Denim", "price": 128.0, "colours_available": "['Lunar Wash', 'Black']", "sizes_available": "['24', '25', '26', '27', '28', '29', '30']", "fabric_composition": "98% Cotton, 2% Elastane"
| # | product_id | brand | product_name | category | price | colours_available |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Offers objects from stitchfix.com. All fields typed and schema-versioned.
"product_id": "SF-994821", "current_price": 98.0, "original_price": 128.0, "discount_pct": 23, "currency": "USD", "stock_status": "in_stock", "sale_badge": true, "scraped_at": "2026-08-14T10:22:15Z"
| # | product_id | current_price | original_price | discount_pct | currency | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Style Metadata objects from stitchfix.com. All fields typed and schema-versioned.
"product_id": "SF-994821", "occasion": "['Casual', 'Weekend']", "fit_type": "Fitted", "aesthetic": "Classic", "recommended_pairings": "['SF-11234', 'SF-88392']", "style_notes": "A versatile staple for everyday wear.", "season": "All Season", "trend_tags": "['Denim', 'High-Rise', 'Essentials']"
| # | product_id | occasion | fit_type | aesthetic | recommended_pairings | style_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Brand Catalogue objects from stitchfix.com. All fields typed and schema-versioned.
"brand_name": "Madewell", "brand_slug": "madewell", "total_products": 342, "categories_covered": "['Denim', 'Tops', 'Outerwear', 'Accessories']", "price_min": 24.0, "price_max": 298.0, "average_price": 85.5, "brand_description": "Effortless styles and premium denim."
| # | brand_name | brand_slug | total_products | categories_covered | price_min | price_max |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Outfit Curation objects from stitchfix.com. All fields typed and schema-versioned.
"outfit_id": "OUTFIT-4421", "theme": "Autumn Layering", "creator": "Stitch Fix Styling Team", "components": "['SF-994821', 'SF-22910', 'SF-44021']", "total_price": 284.0, "occasion": "Work to Weekend", "engagement_score": 88
| # | outfit_id | theme | creator | components | total_price | image_url |
|---|---|---|---|---|---|---|
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Our Stitch Fix scraper navigates dynamic category grids, Freestyle inventory, and style metadata - with JavaScript rendering, anti-bot circumvention, and proxy rotation built in.
Extract product names, brands, descriptions, fabric details, and care instructions across the public Freestyle catalogue.
Capture current prices, original prices, and discount percentages to monitor pricing strategies across brands.
Map the exact brand footprint on Stitch Fix, tracking SKU counts and category penetration per label.
Extract aesthetic tags, fit types, occasion recommendations, and styling notes attached to each garment.
Track size availability and out-of-stock indicators at the SKU level to understand inventory depth.
Extract clean URLs for product imagery, flat lays, and on-model shots for visual analysis models.
Scrape curated outfits and recommended pairings to understand cross-merchandising strategies.
Monitor new arrivals and category expansions to spot seasonal trends and merchandising shifts.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences.
Brief in. Clean data out.
Provide target categories, brands, or Freestyle URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for stitchfix.com.
Schema validation, null-rate checks, and data type assertions before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apparel sites rely on heavy front-end frameworks and aggressive rate limiting. Here is how we maintain stable extraction.
We route requests through US-based residential ISP proxies to avoid datacenter IP bans and rate limits imposed by Stitch Fix's security edge.
Stitch Fix relies heavily on client-side rendering for product grids and dynamic filtering. We run headless Playwright browsers to ensure all DOM elements hydrate fully before extraction.
Front-end frameworks generate dynamic CSS classes. Our extraction logic relies on stable data attributes, JSON payloads in the DOM, and structural fallbacks to prevent pipeline breakage.
We maintain state on previously scraped SKUs. Subsequent runs only emit records when price, stock status, or metadata changes, reducing your downstream processing costs.
Every run emits structured logs to our observability stack. We monitor for extraction anomalies, missing fields, and layout shifts to ensure continuous data fidelity.
Apparel retailers monitor Stitch Fix pricing, discount depths, and promotional cadence to adjust their own strategies.
Merchandising teams analyse fabric compositions, colour distribution, and silhouette trends across the Freestyle catalogue.
Wholesale brands track their own SKU representation, pricing compliance, and stock availability on the Stitch Fix platform.
Machine learning teams use styling metadata, occasion tags, and outfit pairings to train their own recommendation algorithms.
Product developers identify underserved categories or price points within the curated assortment to inform new product lines.
Financial analysts track total active SKU counts, brand partnerships, and inventory turnover signals to evaluate company health.
"Stitch Fix holds a unique intersection of brand inventory and algorithmic styling metadata - but extracting it requires navigating heavy client-side rendering and strict bot mitigation."
Most teams underestimate the investment required: reliable apparel scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our stitchfix.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and interaction flows required for modern front-end frameworks.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to navigate bot detection.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About stitchfix.com scraping, legality, and pipeline operations.
Ask us directly →No. Personalised Fix previews are gated behind user authentication, a completed style profile, and a styling fee. DataFlirt extracts only publicly available data, such as the Freestyle catalogue and brand directories.
We typically run full catalogue sweeps on a daily or weekly cadence. We can configure specific category or brand pipelines to run at higher frequencies if required for pricing intelligence.
Yes. We extract the high-resolution image URLs for flat lays, detail shots, and on-model photography. We deliver the URLs in the payload, and can optionally download and sync the actual image files to your S3 bucket.
We use Playwright to execute full browser sessions, intercept API calls, and trigger infinite scroll or pagination elements to ensure the complete product grid is captured.
Yes. We capture size-level availability data. If a specific size or colourway goes out of stock, it is reflected in the extraction payload.
We build managed pipelines for enterprise use cases. Minimum engagements typically start at tracking the full Freestyle catalogue on a weekly cadence. Contact us for a precise quote based on your volume requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed - we scope, build, and operate the pipeline. Tell us what you need.
