Quick-commerce pricing captured three times a day

The challenge

Quick-commerce apps re-price through the day and by pincode. A single daily snapshot misses the intraday moves — the mid-morning promo, the evening stock-out, the surge pricing — that actually decide who wins the basket. Our client, competing across five platforms, needed to see prices as shoppers actually saw them at any given hour, not as a once-a-day average that described no real moment.

Compounding this: each of the five platforms structures its data differently, prices per dark store, and shows different availability by pincode. Reconciling that by hand, three times a day, was impossible.

What we built

We built pincode-level price and availability capture across Blinkit, Zepto, Swiggy Instamart, BigBasket and Flipkart, refreshed three times daily. Products were matched across all five platforms so the same SKU could be compared like-for-like, and the output was normalized into a single schema the client's pricing team could work from directly.

Because quick-commerce data lives inside the apps and varies by dark store, this ran on our mobile app scraping infrastructure with per-pincode capture, not a simple web scrape.

How it's delivered

The feed is delivered three times a day as a single normalized dataset — CSV and API — with each record timestamped and tagged by pincode, platform and dark-store zone. Product matching runs automatically, so the client sees one row per SKU per pincode per platform, ready to drop into their price intelligence workflow.

The outcome

The client moved from a once-a-day, single-platform view to an intraday, five-platform, pincode-level picture of the market. They can now see how a competitor's morning promotion plays out by evening, which pincodes are running stock-outs, and where their own pricing sits against rivals at any point in the day — the granularity quick commerce actually demands.