Blinkit data scraping extracts hyperlocal data from the Blinkit app — prices, MRP, availability, delivery ETAs and promotions — captured pincode by pincode across cities, up to hourly. iWeb Data Scraping delivers it QA-verified as CSV, JSON or API, so FMCG and quick-commerce teams see the price and stock each neighborhood actually sees. Because Blinkit prices per dark store, this pincode-level view is the only accurate one — a single national price is a fiction.
Blinkit is India's quick-commerce leader, and its defining behavior is hyperlocal: prices, availability and delivery fees vary by the dark store serving each pincode. For FMCG brands and competitors, this is exactly the data that decides trade promos and availability — and exactly what web scraping can't see, because it lives only inside the app, computed per location.
We capture it store-by-store and pincode-by-pincode, hourly, handling Blinkit's app defenses. It powers price intelligence and stock-out tracking, built on our mobile app extraction. Track it alongside Zepto and Instamart for full quick-commerce coverage.
If it’s on the page, we can put it in your warehouse — clean, matched, and refreshed on your schedule.
Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.
| product | pincode | price | mrp | in_stock | eta_min | captured_at |
|---|---|---|---|---|---|---|
| Amul Butter 100g | 110001 | 58 | 62 | yes | 9 | 2026-07-08 18:00 |
| Amul Butter 100g | 110024 | 58 | 62 | no | 12 | 2026-07-08 18:00 |
| Maggi 70g (pack of 4) | 110001 | 55 | 60 | yes | 9 | 2026-07-08 18:00 |
| Coca-Cola 750ml | 400001 | 42 | 45 | yes | 11 | 2026-07-08 18:00 |
[
{
"product": "Amul Butter 100g",
"pincode": "110001",
"price": "58",
"mrp": "62",
"in_stock": "yes",
"eta_min": "9",
"captured_at": "2026-07-08 18:00"
},
{
"product": "Amul Butter 100g",
"pincode": "110024",
"price": "58",
"mrp": "62",
"in_stock": "no",
"eta_min": "12",
"captured_at": "2026-07-08 18:00"
},
{
"product": "Maggi 70g (pack of 4)",
"pincode": "110001",
"price": "55",
"mrp": "60",
"in_stock": "yes",
"eta_min": "9",
"captured_at": "2026-07-08 18:00"
},
{
"product": "Coca-Cola 750ml",
"pincode": "400001",
"price": "42",
"mrp": "45",
"in_stock": "yes",
"eta_min": "11",
"captured_at": "2026-07-08 18:00"
}
]
Hourly rival pricing per city feeds trade-promo decisions — the 40-city case.
Pincode availability pauses spend where you're out — stock-out tracking.
New dark stores and pincodes as Blinkit grows — quick-commerce footprint intelligence.
Prices and MRP, availability per pincode, delivery ETAs and fees, discounts and offers, product attributes, category, images, pack sizes/variants, dark-store coverage, out-of-stock timing and new listings — all captured at pincode level across the cities you track, up to hourly.
Quick-commerce platforms are app-first and price by serving dark store, so the data is computed per location inside the app — there's often no web page showing it, and any single view misrepresents every other pincode. App-based extraction at pincode level is the only way to capture the real, hyperlocal picture.
Engagements commonly cover every serviceable pincode across multiple cities — one program tracked 40 cities hourly. Coverage scales to your needs, and sampling strategies (one pincode per dark-store zone) are available where full coverage exceeds what the question requires.
We collect only what the app publicly displays to any user at a location — the same screens a shopper sees — with rate-limited collection, PII scrubbed, and ISO-certified, NDA-first methodology. We capture public in-app data, not private or account-gated information.