We extract product listings, pricing signals, brand catalogues, and size-level inventory from Sivvi. 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 Metadata objects from sivvi.com. All fields typed and schema-versioned.
"product_id": "N41234567A", "sku": "MAN-DRS-091", "title": "Floral Print Midi Dress", "brand": "Mango", "category": "Women > Clothing > Dresses", "materials": "100% Viscose", "image_urls": "['https://z.nooncdn.com/products/tr:n-t_400/v1612345678/N41234567A_1.jpg']"
| # | product_id | sku | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from sivvi.com. All fields typed and schema-versioned.
"sku": "MAN-DRS-091", "price": 149.0, "list_price": 299.0, "currency": "AED", "discount_pct": 50, "promo_badge": "MEGA SALE", "express_delivery": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | price | list_price | currency | discount_pct | discount_abs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizes objects from sivvi.com. All fields typed and schema-versioned.
"sku": "MAN-DRS-091", "colour": "Red", "size_system": "UK", "available_sizes": "['8', '10', '12']", "out_of_stock_sizes": "['6', '14', '16']", "low_stock_warning": true, "in_stock": true
| # | sku | colour | size_system | available_sizes | out_of_stock_sizes | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from sivvi.com. All fields typed and schema-versioned.
"product_id": "N41234567A", "average_rating": 4.2, "total_reviews": 128, "five_star_pct": 65, "one_star_pct": 5, "top_review_snippets": "['Fits perfectly, great material.']"
| # | product_id | average_rating | total_reviews | five_star_pct | four_star_pct | three_star_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Category objects from sivvi.com. All fields typed and schema-versioned.
"keyword": "midi dresses", "country": "UAE", "position": 3, "product_id": "N41234567A", "brand": "Mango", "price": 149.0, "sponsored": false, "scraped_at": "2026-05-12T09:15:22Z"
| # | keyword | country | position | product_id | title | brand |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our pipeline extracts every layer of Sivvi's catalogue across UAE and KSA markets, tracking price volatility, size availability, and brand assortments with high-frequency crawls.
Title, brand, description, materials, and care instructions scraped at the SKU level. We map parent products to all colour and size variations.
Capture current price, original list price, discount percentages, and promotional badges across AED and SAR currencies.
Track exact size availability, out-of-stock sizes, and low-stock indicators per colour variant. Essential for demand forecasting.
Extract data specific to UAE (sivvi.com/uae-en) and KSA (sivvi.com/ksa-en) storefronts, normalising regional pricing differences.
Capture all product image URLs, including model shots and detail views, useful for visual AI training and catalogue matching.
Monitor express delivery eligibility and estimated shipping times for logistics and competitive benchmarking.
Track brand visibility and organic position for high-volume fashion keywords across specific categories.
Our change-detection engine compares current crawls with previous states, emitting only updated prices or stock changes to reduce your ingestion load.
Configure hourly or daily pipelines to catch flash sales, weekend promotions, and sudden stock depletion events.
Brief in. Clean data out.
Provide target brands, categories, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for sivvi.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sivvi relies heavily on client-side rendering and API-driven product hydration. We bypass frontend complexity to deliver structured data directly.
Sivvi populates product grids and detail pages via internal APIs. Rather than parsing complex DOM structures, our Playwright implementation intercepts network traffic to extract clean JSON payloads directly from their backend responses.
Pricing and stock vary significantly between UAE and KSA. We route requests through residential proxies located in the target country to ensure accurate regional pricing and avoid geo-blocks.
Fashion items have multi-dimensional variants (colour, size, fit). Our extraction logic normalises these relationships, ensuring every specific size and colour combination is tracked as a distinct, queryable record.
High-volume crawling can trigger WAF blocks. We implement adaptive throttling and IP rotation, spreading requests across large proxy pools to maintain steady extraction rates without triggering defensive measures.
E-commerce platforms update their frontends frequently. By relying on API interception and multi-layered CSS fallbacks, our pipelines withstand routine site updates without dropping data.
Retailers track Sivvi's discount strategies, flash sales, and baseline pricing to optimise their own promotional calendars.
Fashion labels monitor how their products are merchandised, priced, and discounted to ensure compliance with regional MAP agreements.
Merchandisers analyse brand depth, category coverage, and new product introductions to identify gaps in their own catalogues.
Supply chain teams track out-of-stock rates across specific sizes and colours to model consumer demand and optimise procurement.
Machine learning teams extract structured metadata alongside high-resolution image URLs to train fashion classification and recommendation models.
New brands use historical pricing and category saturation data to formulate entry strategies for the Middle Eastern e-commerce market.
"Sivvi holds critical signals on Middle Eastern fashion trends and pricing dynamics. Without an automated pipeline, this data remains locked behind client-side rendering."
Extracting reliable data from modern e-commerce platforms requires handling complex JavaScript hydration, geo-specific routing, and variant normalisation. DataFlirt manages this infrastructure entirely. You define the categories and brands; we deliver clean, warehouse-ready data on your schedule.
Everything supported by our sivvi.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, deduplication, and retry logic. Playwright handles JavaScript rendering, network interception, and interaction flows.
We maintain pools of residential ISP proxies specifically for Middle Eastern regions. Rotation happens per-request to ensure accurate regional data.
Pipelines run on AWS Lambda and Kubernetes. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About sivvi.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure pipelines to target specific regional storefronts using localised residential proxies, ensuring you receive the correct currency (AED/SAR), pricing, and region-specific stock availability.
Our pipelines normalise complex variant structures. A single parent product (e.g., a dress) is expanded so that every colour and size combination (e.g., Red-Size 10) is recorded with its specific price and stock status.
Yes. We capture exact size availability and explicitly flag sizes or entire products that are out of stock. This is critical for demand forecasting and assortment planning.
Pipelines can be configured to run daily, multiple times a day, or even hourly for specific high-priority brand or category subsets to monitor flash sales.
We extract the high-resolution image URLs provided by Sivvi. We do not download and host the image files directly, but supply the URLs in the dataset for your ingestion systems.
Our smallest packages start at a defined category or brand list (typically 5,000-20,000 SKUs) with weekly delivery. We price based on total SKU volume and crawl frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a weekly catalogue sync or hourly price monitoring across top fashion brands, we build and operate the infrastructure. Tell us your requirements.
