We extract mattress specifications, furniture dimensions, pricing tiers, discount structures, and verified reviews from Wakefit.co. 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 Product Details objects from wakefit.co. All fields typed and schema-versioned.
"product_id": "WF-MAT-ORTHO-72-72-6", "title": "Orthopedic Memory Foam Mattress", "category": "Mattress", "dimensions": "72x72x6 inches", "material_primary": "Memory Foam", "warranty_years": 10, "trial_period": 100
| # | product_id | title | category | sub_category | dimensions | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from wakefit.co. All fields typed and schema-versioned.
"product_id": "WF-MAT-ORTHO-72-72-6", "base_price": 18499.0, "discounted_price": 12949.0, "discount_percentage": 30, "in_stock": true, "emi_available": true, "emi_starting_price": 618.0
| # | product_id | variant_id | base_price | discounted_price | discount_percentage | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Options objects from wakefit.co. All fields typed and schema-versioned.
"parent_product_id": "WF-MAT-ORTHO", "variant_id": "WF-MAT-ORTHO-72-72-6", "size": "King", "thickness": "6 inch", "firmness_scale": "Medium Firm", "sku": "WMMO72726", "is_default": true
| # | parent_product_id | variant_id | size | colour | thickness | firmness_scale |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Customer Reviews objects from wakefit.co. All fields typed and schema-versioned.
"review_id": "REV-892314", "product_id": "WF-MAT-ORTHO-72-72-6", "rating": 4.5, "review_title": "Excellent back support", "author_name": "Rahul M.", "verified_buyer": true, "helpful_votes": 12
| # | review_id | product_id | rating | review_title | review_text | author_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Delivery Data objects from wakefit.co. All fields typed and schema-versioned.
"pincode": "560034", "product_id": "WF-MAT-ORTHO-72-72-6", "is_deliverable": true, "estimated_delivery_date": "2026-05-15", "delivery_cost": 0.0, "assembly_required": false, "return_eligible": true
| # | pincode | product_id | is_deliverable | estimated_delivery_date | delivery_cost | assembly_required |
|---|---|---|---|---|---|---|
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Our Wakefit scraper handles dynamic variant selectors, pincode-based delivery estimates, and nested review pagination. Built with full JavaScript rendering to capture accurate pricing and stock states.
Extract dimensions, firmness scales, foam layer compositions, and fabric materials for every mattress variant.
Capture wood type, upholstery fabric, product dimensions, and weight limits for sofas, beds, and chairs.
Track base price, discounted price, and active coupon codes. Monitor flash sales and festive discount structures.
Simulate pincode entry to extract accurate delivery dates, shipping costs, and assembly availability across regions.
Paginate through customer feedback to extract full review text, star ratings, helpful votes, and verified buyer tags.
Map parent products to child variants across all permutations of size, thickness, and colour.
Extract terms for 100-day sleep trials, warranty durations, and specific return policy conditions.
Determine if a product requires DIY assembly or professional carpenter service, including associated costs.
Detect out-of-stock variants and track inventory status changes across daily or hourly pipeline runs.
Capture high-resolution product image URLs, lifestyle shots, and assembly instruction videos.
Brief in. Clean data out.
Provide target categories like mattresses, beds, sofas, or accessories. We design the extraction schema together.
We configure Scrapy and Playwright crawlers to handle Wakefit's dynamic variant selectors and pincode logic.
Verify pricing math, dimension parsing, variant completeness, and review extraction before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern D2C storefronts rely heavily on client-side rendering. Here is how we ensure accurate, complete data extraction from Wakefit.
Wakefit reloads price and stock via JavaScript when sizes or thickness options change. We use Playwright to simulate clicks and hydrate all possible variant permutations, ensuring no data is missed.
Delivery estimates and assembly options require submitting a valid pincode. We simulate this interaction across major Indian tier-1 and tier-2 pincodes to build a complete logistics map.
Extracting deep historical reviews requires handling asynchronous load-more buttons and hidden API endpoints. We bypass the UI and query the underlying review endpoints directly for maximum throughput.
During festive sales, Wakefit alters DOM structures to display flash discounts and countdown timers. Our fallback selectors ensure continuous price tracking despite layout changes.
We rotate Indian residential proxies to prevent rate-limiting during high-frequency price monitoring, maintaining realistic request patterns.
D2C sleep startups track Wakefit's discount depth, base pricing, and festive sale strategies to adjust their own positioning.
Analyse review sentiment on firmness, durability, and heat retention to inform new mattress designs and material choices.
Track Wakefit's delivery timelines and assembly availability across different pincodes to benchmark logistics performance.
Quantify the distribution of sizes, materials, and price points in the Indian D2C furniture market.
Process thousands of verified reviews to identify common complaints or highly praised features across product lines.
Monitor the frequency, duration, and depth of Wakefit's seasonal sale events to predict future discounting cycles.
"Wakefit has redefined the Indian sleep and furniture market. Tracking their product taxonomy, pricing tiers, and delivery logistics provides a blueprint of D2C success."
Extracting data from modern D2C storefronts requires handling complex JavaScript state for product variants, pincode-specific delivery logic, and dynamic pricing overlays. DataFlirt manages this infrastructure so you can focus on market analysis rather than maintaining fragile web scrapers. We deliver clean, normalised data on your schedule.
Everything supported by our wakefit.co 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 orchestration and deduplication. Playwright manages JavaScript rendering to interact with dynamic variant selectors and pincode forms.
We maintain pools of residential ISP proxies across Indian regions to ensure reliable access and prevent IP blacklisting during high-frequency crawls.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About wakefit.co scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Wakefit is generally permissible. We target only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use Playwright to simulate user clicks on size, thickness, and colour options, ensuring the DOM updates before we extract the specific price and stock status for that exact permutation.
Yes. We can configure the pipeline to submit specific pincodes and extract the localized delivery estimates, shipping costs, and assembly availability.
We support daily catalogue refreshes. During major sale events, we can configure hourly pipelines for specific high-priority SKUs to track flash discounts.
Yes. We extract the full review text, star rating, author name, verified buyer status, and helpful vote counts across all paginated review pages.
Our selectors use multiple fallback chains. If a festive sale alters the DOM structure, our monitoring detects the schema drift, and we update the selectors to maintain pipeline stability.
Yes. We provide a sample run of up to 100 SKUs as part of the scoping process so you can validate the schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous price monitoring across all furniture categories. Tell us what you need.
