BringFresh – a German-based grocery delivery app – was in developmental limbo: slow, clunky, and incapable of converting visits to orders. Users were frustrated. The dashboard was broken. Manual operations did not offer any scope for scaling. That’s when Techling stepped in.
With a full-stack transformation, Techling envisioned an entirely new architecture for Bring Fresh, providing a lightning-quick frontend, a strong backend, and an efficient operational flow. The result? The app skyrocketed from 0 to hundreds of active users and was established as a trusted platform for grocery delivery in no time.
When Bring Fresh joined hands with Techling, the app was not in a modern development phase -it was outright demonstrating severe issues.
Users had to wait for long to access the app, hence giving up on the platform.
One of the main problems was not putting products into a cart correctly, and thus blocking purchasing.
Processing of orders was slow and cumbersome without automation. It was also full of errors.
The app was disordered, navigation was messed up, and the design was of the outdated type. The result? Zero orders and an insufficient platform to facilitate growth.
To address the core challenges of Bring Fresh, Techling injected a concentrated, full-spectrum makeover – back end engineering to front end user-friendliness. Let’s take a closer look at how each of the issues got fixed and real-life examples that demonstrate the difference between the two cases:
Stodgy and lumbering UI with slow responsiveness. Techling reworked the whole frontend with React Native to give faster loading and better cross-platform performance.
Example
The switch between product categories used to take up to 4–5 seconds before the redesign, which was frustrating. Transitions now take less than 1 second post-implementation, providing users with a smooth shopping experience on both the iOS and Android platforms
Unreliable backend leading to delays and mishandling of the data. We recreated the backend from the ground up with the use of Python. The system was modularized with efficient API endpoints, optimized queries to the database to provide real-time responses.
Example
It took almost 12 seconds to process the original “Place Order” function because of redundant loops and non-indexed database fields. With the Python improvements and indexing it took under 2 seconds.
Logic errors in the dashboard and session management caused issues with adding products to the cart correctly. We integrated a state-aware cart system with up to date updates featuring server-side session persistence to prevent loss of cart data.
Example
Previously, if a user added “Apples” and then visited another category, the cart would be reset. Now, due to continuous state management and backend synchronization, the cart keeps the items reliably during the session – even after app restart.
In the case of manual order handling and inventory systems, there were human errors and inefficiencies. Techling implemented automation through background workers and cron jobs, for inventory synchronization, order status update, and delivery administration.
Example
If there was a need to update the inventory, this was done manually on a once daily basis. If 10 users ordered the last unit of a product, each got confirmations=cancellations. Real-time automation makes the system automatically update inventory as soon as a product is ordered – eliminating overselling.
Slow user drop-offs were coming from slow app load times and sluggish interactions. We minified frontend assets, we optimized image loading with lazy loading, and introduced server-side caching on the backend.
Example
The home screen used to take over 6 seconds to open because of uncompressed images and excessive API calls. Now, it takes 1.3 seconds to load, both on fast and slow networks due to lazy loaded assets and pre-fetched product data.
Low scalability and reliability level in the hosting environment. The app was released on AWS using eligible EC2 instances and controlled RDS databases. CloudWatch and load balancers were merged for monitoring and uptime management.
Example
Promotional campaign saw a surge in traffic by 10x in 48 hours. Using AWS auto scaling and load balancing, the app ran without experience with downtime.
A professional mobile interface, smoothing the transition across devices with React Native.
App speed improved drastically, reducing load times and making the overall experience better.
No more browsing, adding, and purchasing products with glitches but with trust and repeat visits.
Inventory and order processes have moved to the background leaving the Bring Fresh team with more time to work on growth.
The backend written in Python allows spikes in traffic and does not require any effort to process hundreds of orders.
With a rock-solid app handled, Bring Fresh transformed from inactivity to an addictive, money-collecting machine.
| Component | Details |
|---|---|
| Frontend | React Native (cross-platform mobile UI) |
| Backend | Python (fast, scalable APIs) |
| Database | Optimized queries for performance |
| Automation | Custom tools for order & inventory flow |
| Cloud Deployment | AWS (scalable and secure infrastructure) |
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