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App Scraping Done Right

Overview

Not every brand wins by innovating. Some win by executing better — faster, leaner, smarter. Our client, one of the fastest growing e-commerce marketplaces globally, built their brand on this very principle. 

 

Think Samsung vs. Apple. BYD vs. Tesla. They don’t reinvent the wheel, they build a faster chariot with it.

 

After establishing dominance in e-commerce, they set their sights on a new frontier: food delivery.

 

If they were going to outmaneuver UberEats, DoorDash, and Deliveroo, they first had to see the game through their eyes. 

 

They needed granular data from rival food delivery apps — menus, pricing, customer reviews, and more. But scraping mobile apps is notably different from traditional web scraping. 

 

This is the story of how we helped reverse-engineer mobile app responses to extract real-time data using an on-demand Web Data API. 

 

By monitoring a direct competitor — the biggest food delivery app in the region — our client gained deep insight into pricing strategies, product listings, and market sentiment. 

 

What followed was a smarter go-to-market plan, optimized merchant onboarding, and a decisive edge. 

 

Overview-App-Scraping-Done-Right
Key points
  • APIs offer structure but rarely scope. The mobile food delivery app held rich, dynamic data (menus, pricing, delivery times, promotions) not available via public APIs. By reverse-engineering the app, the client accessed otherwise hidden data, enabling real-time benchmarking and more responsive pricing. 
  • Custom headers, SSL pinning, and token authentication initially blocked all scraping attempts. Once we manually deconstructed the mobile app’s session flow and mimicked human behavior through token injection and header replication, stable access was achieved — unlocking the data pipeline. 
  • Using scraped mobile data, the client launched their food delivery offering with surgical precision — onboarding high-demand restaurants rapidly, targeting the right customer segments, and bypassing early market trial-and-error phases. 
  • The biggest challenge of all was the fact that the mobile app frequently changed structure, breaking naïve scrapers. But thanks to a dedicated Slack line, fast-response collaboration, and on-demand data delivery, the system adapted with <5% disruption — ensuring operational reliability and business continuity. 

Challenges

Scraping data from mobile apps presents a fundamentally different challenge than scraping the web. Unlike websites, where browser DevTools make network traffic inspection straightforward, mobile apps operate in closed environments — with no built-in tools to view or analyze requests easily. 

To access underlying data, we had to configure a proxy environment using tools like Charles Proxy or Fiddler, install trusted root certificates on real or emulated devices, and carefully monitor encrypted traffic. Even then, visibility was far from guaranteed. 

The most persistent barrier was SSL pinning — a security mechanism that blocks interception by rejecting any certificate not hardcoded within the app. The source app implemented SSL pinning alongside additional defenses: encrypted tokens, obfuscated endpoints, and custom headers, all designed to resist reverse engineering. 

Other mobile-specific constraints further complicated extraction: 

  • Geolocation-based content filtering 
  • Short-lived authentication tokens
  • Aggressive request rate limiting 
  • Blocking based on behavioral fingerprinting 

Each of these challenges demanded a deep technical response — from reverse-engineering encrypted request logic to simulating verified user behavior — in order to build a stable, compliant, and resilient data pipeline.   

Our data project, if we hadn’t automated through GREPSR would take weeks to complete each month. Working through GREPSR is as easy as it gets. The data comes to us neatly packaged and downloadable. We save hours and hours of work hours each month and can provide up-to-date information regularly for customers. We’ve enlisted their service for years.

G2 User in Retail Small-Business(50 or fewer emp.)

80 %

reduction in product and promotion matching—from 10 days to under 48 hours

95 %

data accuracy rate despite weekly app changes

85 %

of top-performing restaurants in each target zone were identified within the first 7 days of launch

Solutions

When your entire business model is built on out-executing competitors, you can’t afford weak links in your operations — especially in data acquisition. For our client, knowing what their rivals were doing was a requirement for survival. 

Naturally, that left very little room for error. 

They needed clean, structured, high-frequency data from a mobile app designed to keep outsiders out. What they didn’t need was to get bogged down in proxy configurations, token refresh loops, or chasing every UI tweak that broke a scraper. 

As always, this is where we came in. Grepsr took full ownership of the web data pipeline — including all the messy, brittle, behind-the-scenes work. Using Android and iOS emulators, we intercepted the mobile app’s traffic through MITM proxies, paired with custom root certificates to decrypt HTTPS data. 

We reverse-engineered the app’s request architecture, uncovered hidden API endpoints, and handled short-lived session tokens that required real-time refresh logic to stay authenticated. 

The app’s SSL pinning, behavioral fingerprinting, and geolocation-based restrictions added another layer of complexity — all of which we neutralized with adaptive techniques that mirrored verified user behavior. 

Most importantly, perhaps one factor above everything else continues to push this project through the finish line — communication.

We worked in tight sync with the client via a dedicated Slack channel, rapidly responding to changes in app’s structure and keeping their internal teams focused on insights while we handled the infrastructure. 

Whether it meant hopping on a call at 4 a.m. or patching a scraper late at night, our engineering team made sure the data kept flowing.

Operational efficiency was their end game. We understood that — completely.  

Even as the app changed multiple times a month, we maintained 95%+ data accuracy and held system drift under 5%, ensuring uninterrupted business performance.

In short, we did the legwork, so they could stay focused on what they do best: beating their competitors at their own game. 

When execution is everything, your partner shouldn’t be a variable. 

With Grepsr, it isn’t. 

 

Solutions

Similar challenges faced across the industry:

Lack of technical know-how to automate routine data extractions

Businesses need fresh data to gather the best insights. To that end, one or two data extractions a day does not suffice. They need a system that can easily schedule crawl runs at specific intervals, as well as on demand.

Lack of resources - time, money and manpower - for data sourcing at scale

Data extraction is extremely tedious and highly error-prone. Most businesses lack the infrastructure to perform high volumes of data sourcing, and at a quality that yields the best results.

Overcoming data source restrictions

Most websites place limits on how many requests can be made in a set time period, and regularly block bots from accessing their content.

PROCESS

Getting started with Grepsr

Start with Grepsr in a few easy steps. Leave the data sourcing heavy lifting to us, so you can focus on innovation and growth.

1

Initial project consultation

First, we'll discuss the specifics of your web data needs and the KPIs you would like to have in order to ensure successful project execution.

2

Instrument web crawlers

We'll then set up automated extractions specific to your use-case, and send you a sample dataset before moving on to a full-scale crawl.

3

Begin data collection

Once you've approved the sample data, we will start scaling and performing the full run, and deliver the data in the agreed timeframe.

4

Hassle-free maintenance

Our team will ensure that all subsequent runs are running well, and that your data is delivered as scheduled with the least disruption.

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