We extract lightweight jewellery catalogues, dynamic gold pricing, BIS hallmark metadata, and category structures from Melorra. 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 Metadata objects from melorra.com. All fields typed and schema-versioned.
"sku": "W222P083", "name": "The Golden Glint Gold Ring", "category": "Rings", "metal_colour": "Yellow Gold", "metal_purity": "18kt", "total_weight_g": 2.45, "is_new_arrival": true
| # | sku | product_id | name | category | sub_category | metal_colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Breakdown objects from melorra.com. All fields typed and schema-versioned.
"sku": "W222P083", "total_price": 14590.0, "list_price": 16211.0, "discount_pct": 10, "gold_value": 11200.0, "making_charges": 2950.0, "gst_amount": 440.0, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | total_price | list_price | discount_pct | gold_value | diamond_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Diamond & Gemstone objects from melorra.com. All fields typed and schema-versioned.
"sku": "D193P442", "has_diamonds": true, "diamond_weight_ct": 0.12, "diamond_clarity": "SI", "diamond_colour": "IJ", "total_diamonds": 14, "has_gemstones": false
| # | sku | has_diamonds | diamond_weight_ct | diamond_clarity | diamond_colour | total_diamonds |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Availability objects from melorra.com. All fields typed and schema-versioned.
"sku": "W222P083-14", "size_option": "14", "in_stock": true, "dispatch_timeline": "Ships in 24 hrs", "try_at_home_eligible": true, "bis_hallmarked": true, "lifetime_exchange_eligible": true
| # | sku | parent_id | size_option | in_stock | dispatch_timeline | try_at_home_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from melorra.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "sku": "W222P083", "rating": 4.8, "review_text": "Beautiful design for daily wear. Exactly as shown in the pictures.", "verified_buyer": true, "helpful_votes": 12, "review_date": "2026-03-14"
| # | review_id | sku | rating | review_text | reviewer_name | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Melorra's pricing is highly dynamic, driven by daily gold rates and complex making charge calculations. We extract the complete price breakdown and technical specifications reliably.
Capture metal purity, weight, diamond clarity, colour, and gemstone details for every SKU.
Extract the exact split between gold value, diamond value, making charges, and GST.
Track price fluctuations as Melorra updates its catalogue based on live market gold rates.
Map ring sizes, bangle dimensions, and chain lengths to their respective availability states.
Scrape primary images, 3D render URLs, and lifestyle shots associated with each product.
Track dispatch timelines, Try-at-Home eligibility, and PIN code specific delivery estimates.
Preserve Melorra's internal taxonomy, including seasonal collections and trend tags.
Extract customer ratings, review text, and verified buyer flags across the entire catalogue.
Only receive updates when prices, stock levels, or new designs are added to the platform.
Brief in. Clean data out.
Specify target categories, collections, or specific product types. We map the required data fields.
We deploy Scrapy crawlers with Playwright to handle Melorra's dynamic frontend and pagination.
We verify price breakdowns match total prices and ensure technical specifications are fully captured.
Clean JSON, CSV, or Parquet records delivered to your S3 bucket or data warehouse on schedule.
Extracting data from Melorra requires navigating client-side rendering and dynamic pricing engines. Here is how we maintain reliable extraction.
Melorra relies heavily on JavaScript to render product grids, variant options, and calculate final pricing. We use Playwright to execute the SPA logic, ensuring we capture the exact data displayed to users.
Jewellery pricing is complex. Our pipeline validates that the sum of gold value, diamond value, making charges, and GST matches the final displayed price, flagging any anomalies automatically.
To prevent IP blocks during full catalogue sweeps, requests are routed through a pool of residential Indian IP addresses with randomised delays matching human browsing patterns.
We utilise multiple selector fallbacks and API interception techniques to extract product metadata, ensuring pipeline stability even when frontend layouts are updated.
Instead of re-delivering the entire 18K+ product catalogue daily, our change detection system isolates products with modified prices, stock levels, or new additions.
Jewellery retailers monitor Melorra's making charges and discount strategies to optimise their own pricing models.
Analysts track the volume of new designs and material trends in the lightweight, daily-wear jewellery segment.
Computer vision teams use high-quality product images and detailed metadata to train jewellery recognition algorithms.
Fashion aggregators integrate Melorra's updated inventory and pricing into unified shopping portals.
Retail strategists correlate review volume and out-of-stock indicators to estimate product popularity.
Brands analyze the ratio of gold to diamond products and average ticket sizes across different categories.
"Melorra's pricing model shifts daily with global gold rates. Capturing this dynamic pricing alongside exact diamond specifications requires persistent, targeted extraction."
Scraping modern jewellery platforms involves handling complex single-page applications and dynamic price calculation engines. DataFlirt manages the underlying extraction infrastructure, ensuring you receive normalised data without maintaining custom scrapers or bypassing bot protection systems.
Everything supported by our melorra.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.
Playwright handles Melorra's JavaScript-heavy frontend, ensuring all dynamic pricing scripts execute before data extraction occurs.
Residential proxy pools rotate IP addresses to maintain high success rates and avoid rate limiting during full catalogue crawls.
Apache Airflow orchestrates daily or hourly extraction runs, triggering validation checks before pushing data to your warehouse.
Data delivered to where your team already works — no new tooling required.
About melorra.com scraping, legality, and pipeline operations.
Ask us directly →We typically configure pipelines to run daily, aligning with global gold rate updates. Hourly tracking is also available for specific high-priority SKUs.
Yes. We capture the total price alongside the specific values for gold, diamonds, gemstones, making charges, and GST as displayed on the product page.
Yes. We can simulate sessions with specific Indian PIN codes to extract accurate delivery estimates and Try-at-Home eligibility.
Variants are mapped to a parent product ID. Each variant record includes its specific size, availability status, and any price variations.
Absolutely. We normalise technical specifications, convert string weights to numeric values, and deliver clean JSON or Parquet files ready for your warehouse.
We begin tracking price history from the day your pipeline is activated. We do not provide historical data prior to pipeline deployment.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually tracking gold price adjustments and making charges. DataFlirt delivers clean, structured Melorra catalogue data directly to your infrastructure.
