We extract hyper-local inventory, pricing signals, delivery SLAs, and stock availability from Blinkit across specific geo-coordinates. 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 Catalogue objects from blinkit.com. All fields typed and schema-versioned.
"product_id": "PRD-849201", "name": "Amul Taaza Homogenised Toned Milk", "brand": "Amul", "mrp": 72.0, "selling_price": 72.0, "weight_volume": "1 l", "category": "Dairy & Breakfast", "sub_category": "Milk"
| # | product_id | name | brand | category | sub_category | weight_volume |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dark Store Inventory objects from blinkit.com. All fields typed and schema-versioned.
"store_id": "DS-DEL-44", "pincode": "110017", "product_id": "PRD-849201", "in_stock": true, "delivery_time_mins": 9, "surge_active": false, "handling_fee": 4.0, "scraped_at": "2026-05-12T09:14:00Z"
| # | store_id | lat | lng | pincode | product_id | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from blinkit.com. All fields typed and schema-versioned.
"product_id": "PRD-11928", "store_id": "DS-BLR-12", "mrp": 150.0, "selling_price": 120.0, "discount_pct": 20, "bank_offer_text": "10% off on HDFC Cards", "promo_code_eligible": true, "last_updated": "2026-05-12T09:15:00Z"
| # | product_id | store_id | mrp | selling_price | discount_abs | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from blinkit.com. All fields typed and schema-versioned.
"category_id": "CAT-992", "category_name": "Snacks & Munchies", "parent_category": "Packaged Food", "display_order": 3, "total_products": 412, "is_active": true, "store_id": "DS-BOM-08"
| # | category_id | category_name | parent_category | display_order | banner_image | total_products |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from blinkit.com. All fields typed and schema-versioned.
"keyword": "cold coffee", "lat": 28.5355, "lng": 77.241, "position": 1, "product_id": "PRD-5532", "sponsored_flag": true, "in_stock": true, "scraped_at": "2026-05-12T09:16:33Z"
| # | keyword | lat | lng | position | product_id | name |
|---|---|---|---|---|---|---|
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Our Blinkit scraper handles the complexities of 10-minute delivery apps: strict geo-fencing, dynamic dark store mapping, high-frequency inventory changes, and mobile API rate limits.
Inject exact latitude and longitude coordinates into API headers to extract inventory and pricing specific to individual dark stores.
Monitor minute-by-minute out-of-stock statuses across FMCG categories to map supply chain gaps at the pincode level.
Capture base selling price, print rate (MRP), handling fees, weather surge pricing, and active bank offers per location.
Identify active store IDs, serviceability radii, and delivery time SLAs across multiple cities and neighbourhoods.
Extract directly from Blinkit mobile endpoints using TLS fingerprinting and token generation, bypassing limited web fallbacks.
Track organic versus paid positions in search results to audit retail media spend and brand visibility.
Calculate digital shelf share for specific categories across hundreds of dark stores simultaneously.
Compare pricing, assortment, and stock depth between Delhi NCR, Mumbai, Bengaluru, and other tier-1 markets.
Run pipelines at 15-minute intervals, emitting only state changes (stock-outs, price drops) to minimise warehouse compute.
Brief in. Clean data out.
Provide lat/long pairs, pincodes, category lists, or specific brand names. We design the extraction schema together.
We configure HTTPX clients, mobile API emulation, Indian residential proxy rotation, and header generation.
Schema validation, null-rate checks, location accuracy verification, and stock anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Quick commerce apps rely on strict geo-fencing and aggressive rate limiting. Here is how we maintain stable extraction across thousands of dark stores.
Blinkit inventory does not exist globally; it is tied strictly to a dark store. We inject exact latitude and longitude coordinates into request headers and cookies, mapping specific locations to their servicing dark store to extract accurate local stock states.
The web interface is a secondary surface. We reverse engineer the Blinkit iOS and Android API endpoints, emulating mobile app TLS fingerprints, request signing, and session tokens to access the primary, highest-fidelity data source.
In a 10-minute delivery model, inventory changes rapidly. We deploy streaming architectures that poll specific dark stores at 15-minute intervals, capturing intra-day out-of-stock events and surge pricing spikes that daily crawls miss entirely.
Blinkit blocks data centre IPs instantly. We route all requests through high-reputation Indian residential ISP proxies, matching the geographic region of the requested dark store to avoid anomaly detection.
Polling 500 dark stores every 15 minutes generates massive data bloat. We maintain a state cache in Redis, emitting records only when a product's price, stock status, or delivery time changes. You ingest a clean event stream.
Consumer brands track out-of-stock rates across key neighbourhoods to optimise supply chain distribution to quick commerce warehouses.
Q-commerce aggregators compare Blinkit pricing, delivery SLAs, and assortment depth against Zepto and Swiggy Instamart.
Brands verify if their sponsored ad spend on Blinkit translates into top-of-search placements across targeted pincodes.
Analysts map stock depletion velocity during peak hours or weather events to build predictive demand models.
Category managers identify missing SKUs in specific dark stores to pitch new product listings to Blinkit buyers.
Research firms calculate digital shelf space and category penetration by brand across tier-1 Indian cities.
"Quick commerce is won or lost in the dark store. If you cannot see hyper-local inventory at the pincode level, you are flying blind in a 10-minute delivery market."
Most data teams struggle with quick commerce extraction because it requires precise geo-coordinate injection, mobile API reverse engineering, and high-frequency crawling to catch minute-by-minute stock changes. DataFlirt handles the Indian residential proxy rotation and API session management so you receive clean inventory feeds.
Everything supported by our blinkit.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.
We map Blinkit internal API endpoints using Mitmproxy, replicating header signing, token generation, and payload structures via HTTPX clients.
Requests are routed through Indian residential proxies, ensuring the IP geography matches the requested dark store coordinates to prevent blocking.
Redis caches the last known state of every SKU per dark store. Pipelines emit records only when price, stock, or delivery SLAs change.
Data delivered to where your team already works — no new tooling required.
About blinkit.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available inventory, pricing, and catalogue data from Blinkit is generally permissible. DataFlirt targets only public, non-authenticated endpoints. We do not extract personal user data, order histories, or violate GDPR/DPDP norms. Clients should review Blinkit terms of service and consult legal counsel for specific use cases.
We require a list of target latitude and longitude coordinates or pincodes. Our infrastructure injects these coordinates into the API headers, forcing Blinkit to return inventory and pricing specific to the dark store servicing that exact location.
Yes. For targeted SKU lists across specific dark stores, we configure streaming pipelines that poll endpoints at 10 to 15-minute intervals. We use Redis state caching to emit only the delta changes (e.g., when an item goes out of stock).
Yes. We maintain pipelines for Zepto, Swiggy Instamart, and BigBasket Now. We can map these sources into a unified schema, allowing you to compare pricing and availability across platforms instantly.
We use high-quality Indian residential ISP proxies, rotate TLS fingerprints, and manage session cookies to emulate legitimate mobile app behaviour. Our orchestration layer automatically backs off and rotates IPs if 429 Too Many Requests responses are detected.
Our minimum deployments typically cover a specific brand catalogue across a tier-1 city (e.g., 500 SKUs across 150 dark stores in Delhi NCR) delivered daily. For pan-India coverage or high-frequency polling, we scope compute resources accordingly.
We do not sell historical datasets. We build forward-looking pipelines. We maintain a time-series table per product and store location from the day your pipeline is commissioned.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily FMCG availability report across 500 dark stores or continuous price tracking for competitor benchmarking - we scope, build, and operate the pipeline. Tell us what you need.
