We extract product catalogues, nutritional panels, flavour variants, and subscription pricing from Kaged. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Product Listings objects from kaged.com. All fields typed and schema-versioned.
"sku": "KGD-PRE-001", "title": "Pre-Kaged Elite", "category": "Pre-Workout", "price": 59.99, "currency": "USD", "in_stock": true, "rating": 4.8, "review_count": 2145
| # | sku | title | category | sub_category | price | compare_at_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Supplement Facts objects from kaged.com. All fields typed and schema-versioned.
"sku": "KGD-PRE-001", "serving_size": "1 Scoop (35g)", "servings_per_container": 20, "calories": 25, "caffeine_mg": 388, "l_citrulline_g": 10, "beta_alanine_g": 3.2, "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | serving_size | servings_per_container | calories | protein_g | carbohydrates_g |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Subscriptions objects from kaged.com. All fields typed and schema-versioned.
"sku": "KGD-PRE-001", "one_time_price": 59.99, "subscription_price": 53.99, "subscription_discount_pct": 10, "delivery_frequencies": "['2 weeks', '4 weeks', '6 weeks']", "loyalty_points_earned": 60, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | one_time_price | subscription_price | subscription_discount_pct | delivery_frequencies | bundle_discount |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from kaged.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "sku": "KGD-PRE-001", "reviewer_name": "John D.", "verified_buyer": true, "star_rating": 5, "review_title": "Best pump ever", "review_date": "2026-04-18", "flavour_reviewed": "Fruit Punch"
| # | review_id | sku | reviewer_name | verified_buyer | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Bundles & Stacks objects from kaged.com. All fields typed and schema-versioned.
"bundle_sku": "BND-MUSCLE-01", "bundle_title": "Muscle Building Stack", "component_skus": "['KGD-PRE-001', 'KGD-PRO-002', 'KGD-CRE-001']", "total_value": 145.97, "bundle_price": 119.99, "discount_pct": 17, "in_stock": true
| # | bundle_sku | bundle_title | component_skus | total_value | bundle_price | discount_abs |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Kaged scraper handles Shopify variant structures, dynamic subscription pricing, and complex nutritional tables with JavaScript rendering built in.
Extract active ingredients, dosages, serving sizes, and proprietary blends directly from Supplement Facts tables and images.
Map flavours, sizes, and packaging options to their corresponding SKUs, capturing stock status for each specific variant.
Capture one-time purchase prices alongside Subscribe & Save rates, delivery frequencies, and loyalty point values.
Paginate through product reviews to extract star ratings, text bodies, verified buyer badges, and specific flavours reviewed.
Deconstruct product stacks into component SKUs, calculating true discount percentages and total bundle value.
Track inventory availability across all variants to monitor supply chain health and out-of-stock frequencies.
Navigate pre-workout, protein, hydration, and vitamin categories to maintain a complete catalogue map.
Capture high-resolution product imagery, label scans, and promotional graphics associated with each SKU.
Run daily or weekly pipelines to track pricing adjustments, new product launches, and review volume changes.
Brief in. Clean data out.
Provide target categories, specific SKUs, or request a full catalogue sweep. We define the schema together.
We configure crawlers to handle Shopify endpoints, dynamic rendering, and nutritional table parsing.
Schema validation, null-rate checks, and ingredient dosage verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Supplement sites present unique structural challenges. Here is how we maintain data integrity across nutritional panels and dynamic variants.
Kaged uses complex variant structures for flavours and sizes. We execute JavaScript to trigger variant state changes, ensuring we capture accurate pricing and stock status for every combination.
Supplement Facts panels often use non-standard HTML tables or images. We use custom parsers to convert these structures into clean JSON, mapping active ingredients and dosages accurately.
Subscribe & Save pricing requires DOM interaction to reveal discount tiers and delivery frequencies. Our Playwright instances simulate user selection to extract all pricing layers.
Reviews are loaded dynamically via third-party widgets. We intercept underlying API calls to bypass frontend pagination limits and extract the complete historical review corpus.
We maintain state across pipeline runs, emitting only changed records. This allows you to track minor formulation tweaks, price adjustments, or new flavour additions without processing the entire catalogue.
Supplement brands analyse Kaged ingredient profiles and dosages to benchmark their own pre-workout and protein formulations.
Competitors monitor Kaged one-time and subscription pricing models to optimise their own discount tiers and bundle offers.
Product teams mine review text to identify flavour preferences, mixability issues, and perceived efficacy of specific ingredients.
Analysts track flavour availability and out-of-stock frequencies to identify high-demand profiles and supply chain constraints.
Brands compare Kaged direct-to-consumer pricing against third-party retailers to enforce Minimum Advertised Price policies.
Supply chain teams correlate review velocity and stock depth indicators to estimate sales volume for specific supplement categories.
"Nutritional profiles and dynamic subscription pricing are locked in complex DOM structures. We convert them into queryable datasets."
Most teams underestimate the complexity of scraping supplement catalogues. Reliable extraction requires parsing non-standard nutritional tables, mapping dynamic flavour variants, and monitoring stock levels across multiple SKUs. DataFlirt absorbs that complexity so your engineers can focus on analysis.
Everything supported by our kaged.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 executes JavaScript to trigger variant state changes and reveal subscription pricing tiers.
We route requests through US residential IPs to bypass Cloudflare protections and ensure consistent access to catalogue endpoints.
Pipelines run on AWS 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 kaged.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing, nutritional data, and reviews is generally permissible. DataFlirt targets only public, non-authenticated endpoints. We do not extract personal data or circumvent authentication walls.
We use Playwright to simulate user interactions, selecting different flavours and sizes to capture the corresponding SKU, price, and stock status for every variant.
Yes. While Kaged provides HTML tables for most products, we deploy OCR pipelines for legacy products where nutritional data is only available as an image asset.
We support daily or hourly runs depending on your requirements. Hourly pipelines are typically restricted to a subset of high-priority SKUs.
Yes. We capture both the one-time purchase price and the subscription price, along with the available delivery frequency options.
Yes. Our parsers deconstruct product stacks into their component SKUs, allowing you to calculate the exact discount applied to the bundle.
Our packages start with a full catalogue extraction delivered weekly. Contact us with your specific frequency requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across all variants, we scope, build, and operate the pipeline.
