We extract dark store inventory, hyperlocal pricing signals, delivery ETAs, and search rankings from Zepto. 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 zepto.in. All fields typed and schema-versioned.
"sku_id": "PRD-928174", "name": "Amul Taaza Homogenised Toned Milk", "brand": "Amul", "category": "Dairy, Bread & Eggs", "sub_category": "Milk", "weight": "1 L", "mrp": 72.0, "selling_price": 70.0
| # | sku_id | name | brand | category | sub_category | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dark Store Inventory objects from zepto.in. All fields typed and schema-versioned.
"store_id": "DS-BLR-042", "pincode": "560034", "sku_id": "PRD-928174", "in_stock": true, "stock_quantity": 45, "max_order_qty": 5, "availability_status": "AVAILABLE"
| # | store_id | pincode | lat_long | sku_id | in_stock | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from zepto.in. All fields typed and schema-versioned.
"sku_id": "PRD-928174", "store_id": "DS-BLR-042", "base_price": 70.0, "zepto_pass_price": 68.0, "discount_amount": 2.0, "promo_code_eligible": false, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku_id | store_id | base_price | zepto_pass_price | discount_amount | promo_code_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Logistics objects from zepto.in. All fields typed and schema-versioned.
"store_id": "DS-BLR-042", "pincode": "560034", "base_eta_mins": 12, "weather_surge": false, "high_demand_surge": true, "delivery_fee": 15.0, "handling_fee": 4.0, "operational_status": "OPEN"
| # | store_id | pincode | base_eta_mins | weather_surge | high_demand_surge | delivery_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Discovery objects from zepto.in. All fields typed and schema-versioned.
"keyword": "milk", "location_id": "DS-BLR-042", "rank_position": 1, "sku_id": "PRD-928174", "sponsored_flag": false, "trending_flag": true, "category_path": "Dairy, Bread & Eggs > Milk"
| # | keyword | location_id | rank_position | sku_id | sponsored_flag | banner_promotions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Zepto scraper handles the complexities of quick commerce: precise geolocation spoofing, mobile API interception, and high-frequency inventory polling.
Pinpoint extraction across hundreds of micro-fulfillment centres using exact latitude and longitude grids.
Capture location-specific MRP, selling price, and exclusive Zepto Pass discounts per dark store.
Track stock-outs, low-stock warnings, and availability at the SKU level across the network.
Monitor dynamic delivery times, weather surges, and high-demand fees with sub-minute precision.
Extract the full nested category tree from fresh produce to electronics and apparel.
Audit organic versus sponsored placements for FMCG keywords across different pincodes.
Capture bank offers, bundle deals, and cart-level discounts applied to specific SKUs.
Map frequently bought together items and substitute product recommendations.
Run pipelines at 15-minute intervals for critical stock monitoring and competitor alerting.
Brief in. Clean data out.
Provide target pincodes, lat/long coordinates, or category lists. We design the extraction schema together.
We configure API interceptors, geolocation spoofing, session management, and proxy rotation for zepto.in.
Schema validation, null-rate checks, price-outlier detection, and location accuracy checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Quick commerce platforms rely on strict geolocation validation and mobile-first architectures. Here is how we extract the data reliably.
Zepto requires exact geographic coordinates to route requests to the correct dark store. We inject structured geodata into API headers and payloads to simulate requests from specific Indian pincodes.
Web traffic provides limited visibility. We intercept and structure the undocumented mobile API endpoints for higher throughput, lower latency, and richer payload extraction.
Authentication tokens expire rapidly. Our middleware handles automated token generation and rotation across residential IP pools without triggering rate limits or blocklists.
Inventory changes by the minute. We maintain hash indexes to only push state changes to your warehouse, reducing compute costs and downstream processing load.
Delivery fees and ETAs fluctuate based on weather and rider availability. We capture these transient states with sub-minute precision for accurate logistics benchmarking.
Brands track category penetration and share of shelf across competing quick-commerce platforms.
Rival platforms monitor hyper-local pricing and discount strategies to adjust their own algorithms.
Supply chain teams receive webhook alerts when core SKUs drop to zero inventory at specific dark stores.
Category managers analyze Zepto's SKU expansion in new verticals like electronics and apparel.
Logistics analysts track ETA promises and surge pricing frequencies across different city zones.
Agencies verify sponsored product placements and banner visibility for their FMCG clients.
"Quick commerce is a hyper-local data problem. A single SKU has a different price, stock state, and ETA across 300 dark stores at any given minute."
Scraping Zepto requires more than just HTTP requests. It demands precise geolocation spoofing, mobile API interception, and high-frequency polling. DataFlirt handles the geographic state management and token rotation so you can focus on the market analysis.
Everything supported by our zepto.in 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 bypass web frontends and interface directly with backend APIs using intercepted mobile app traffic patterns.
We maintain a mapped grid of India's pincodes and lat/long coordinates to systematically query every dark store radius.
Stateless AWS Lambda functions poll inventory endpoints concurrently, writing state changes to Kafka and Redis.
Data delivered to where your team already works — no new tooling required.
About zepto.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zepto is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated product, pricing, and availability data. We do not extract personal data or bypass authentication walls.
We inject precise latitude and longitude coordinates into the API requests. This forces Zepto's backend to return inventory and pricing data specific to the dark store servicing that exact location.
Yes. We configure high-frequency pipelines to poll specific SKUs at 15-minute intervals, capturing exact stock quantities and out-of-stock events as they happen.
Yes. Our schema includes the standard MRP, the regular selling price, and the discounted Zepto Pass price for every SKU.
Yes. We extract search results and flag sponsored SKUs for targeted FMCG keywords across different dark store locations.
We distribute requests across Indian residential proxy pools and rotate API session tokens automatically to maintain high throughput without triggering IP bans.
Our smallest packages start at a defined category or keyword list across a set of target cities. Contact us with your specific use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category dump or a continuous inventory monitoring feed across 300 dark stores, we build and operate the pipeline. Tell us what you need.
