We extract product listings, dimension charts, material specifications, pricing, and stock status from Nilkamal and @home. Delivered as clean JSON, CSV, or Parquet.
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 nilkamal.com. All fields typed and schema-versioned.
"sku": "FNTB1029", "title": "Nilkamal Arthur Dining Table", "category": "Furniture", "price": 14500.0, "mrp": 18000.0, "in_stock": true, "material": "Solid Wood"
| # | sku | title | category | sub_category | collection | price |
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Complete list of extractable fields for Specs & Dimensions objects from nilkamal.com. All fields typed and schema-versioned.
"sku": "FNTB1029", "width_cm": 150, "height_cm": 75, "depth_cm": 90, "primary_material": "Rubber Wood", "assembly_required": true, "finish": "Walnut"
| # | sku | width_cm | height_cm | depth_cm | weight_kg | primary_material |
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Complete list of extractable fields for Pricing & Stock objects from nilkamal.com. All fields typed and schema-versioned.
"sku": "FNTB1029", "current_price": 14500.0, "original_mrp": 18000.0, "discount_percentage": 19, "stock_status": "In Stock", "delivery_days": 5, "installation_included": true
| # | sku | current_price | original_mrp | discount_percentage | tax_inclusive | stock_status |
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Complete list of extractable fields for Mattresses & Sleep objects from nilkamal.com. All fields typed and schema-versioned.
"sku": "MATT892", "mattress_type": "Memory Foam", "firmness": "Medium Firm", "thickness_inches": 6, "size_category": "King", "warranty_years": 10, "trial_nights": 100
| # | sku | title | mattress_type | firmness | thickness_inches | size_category |
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Complete list of extractable fields for @home Decor objects from nilkamal.com. All fields typed and schema-versioned.
"sku": "ATH304", "title": "Ceramic Table Vase", "decor_category": "Vases", "price": 899.0, "mrp": 1299.0, "material": "Ceramic", "color": "Teal"
| # | sku | title | decor_category | brand | price | mrp |
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Our pipeline captures the complete product taxonomy across Nilkamal, @home, and SleepX. We handle dynamic pincode checks, material specifications, and stock updates.
Extract primary material, finish, dimensions, weight, and assembly requirements for every seating, storage, and table unit.
Capture firmness ratings, thickness, foam density, coil count, and warranty periods for SleepX and Nilkamal mattresses.
Track current selling price, MRP, discount percentages, and seasonal sale pricing across the entire catalogue.
Automate pincode inputs to extract location-specific delivery timelines, shipping costs, and installation availability.
Link parent products to all available colour variants and upholstery options, extracting specific images for each.
Scrape the distinct @home by Nilkamal catalogue, including home accents, kitchenware, lighting, and textiles.
Extract bulk office seating, desking systems, and storage cabinets with B2B specification metrics.
Capture high-resolution product images, lifestyle shots, and assembly manual PDFs directly from the CDN.
Monitor out-of-stock indicators and inventory levels to map supply chain availability over time.
Brief in. Clean data out.
Provide category URLs, specific product lines, or target pincodes. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for nilkamal.com.
Schema validation, null-rate checks, and dimension format normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting furniture data requires handling dynamic pincode validation, complex variant structures, and CDN image mapping.
Delivery timelines and stock availability on Nilkamal depend on the user's location. We inject specific pincodes via Playwright to extract accurate regional data.
Furniture items often have multiple upholstery or finish variants. We map these relationships so you receive a structured grouping rather than disjointed SKUs.
Measurements appear in inches, centimetres, and millimetres across different categories. Our pipeline normalises all dimension data into a consistent unit structure.
We extract direct links to high-resolution product images and assembly manuals from Nilkamal's CDN, ensuring you get the best quality media assets.
We maintain a hash index of last-seen values. Subsequent runs only push diffs for price changes and stock availability, reducing downstream processing load.
Furniture retailers monitor Nilkamal's pricing, discounts, and seasonal sale events to adjust their own pricing strategies.
Category managers analyse Nilkamal's product mix across living, bedroom, and office segments to identify market gaps.
Analysts extract primary materials, finishes, and upholstery types to track shifting consumer preferences in home decor.
Procurement teams track out-of-stock rates across specific pincodes to understand regional supply chain bottlenecks.
Corporate buyers aggregate office furniture specifications and pricing to build internal procurement catalogues.
Sleep brands track SleepX pricing, firmness ratings, and warranty terms to benchmark their own D2C mattress offerings.
"Nilkamal's digital catalogue contains critical intelligence on Indian furniture pricing, material trends, and regional stock availability. This data is accessible only through structured extraction."
Extracting home and furniture data requires more than simple HTML parsing. You need to handle dynamic location contexts, map complex product variants, and normalise inconsistent dimension formats. DataFlirt manages this infrastructure so your analysts can focus on pricing strategy and market research, not crawler maintenance.
Everything supported by our nilkamal.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 deduplication. Playwright manages location injection and dynamic content rendering.
We use Indian residential ISP proxies to ensure accurate regional pricing and avoid rate-limiting during large catalogue sweeps.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About nilkamal.com scraping, legality, and pipeline operations.
Ask us directly →We extract data across all categories including home furniture, office furniture, SleepX mattresses, and the @home decor collection.
Yes. We configure our crawlers to inject specific Indian pincodes, allowing us to extract accurate regional stock availability and estimated delivery days.
Furniture specifications can be unstructured. Our pipeline extracts raw dimensions and materials, normalising them into standard units and structured JSON fields.
We extract the direct CDN URLs for all high-resolution product images, lifestyle shots, and downloadable assembly PDFs associated with each SKU.
For price monitoring, we typically run daily or weekly pipelines across the catalogue, delivering delta files that highlight price drops or discount changes.
Extracting publicly available product, pricing, and specification data is generally permissible. DataFlirt does not extract authenticated user data or bypass login walls.
Yes. We track out-of-stock indicators and historical inventory states to help you model supply chain availability over time.
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 the furniture market, we scope, build, and operate the pipeline.
