Azati

The Azati team helped the customer create a user friendly application design that serves to replace the offline market for selling and buying auto parts in a more convenient and fast way.

OBJECTIVE
Spare parts are usually searched by specific criteria. And it is not possible to accurately combine the search structure of such products with the classic catalog of multi-category marketplaces on one site. This is one of the key technical issues at the moment.

The customer wanted to create an app instead of an offline market in Dubai and save people from grueling trips to physical stores.

Azati’s task was to implement business logic, form an application map and create a convenient, modern and intuitive interface for this online custom marketplace.

SOLUTION
The business logic of the application and the UX part were developed with minimal UI.

The design was developed for two types of users:

1 – BUYER: places an ad for the purchase of the spare part he needs (for this, he sets up a filter, on the basis of which the text of the purchase offer is generated automatically)

1 – SELLER: adjusts the filter according to what he sells and receives offers from buyers according to his settings

Then the seller responds to the ad from the buyer: sets his own price, adds photos of the product and sends an offer to the buyer.

The buyer sees the response and rejects or accepts it. If he accepts, it means that he is placing an order.

The seller sees that the buyer placed the order, confirms it and sends the product by mail or courier (depending on the option the buyer has chosen).

RESULTS
Ultimately, our solution was a complete design for a mobile application instead of an offline market in Dubai for buying and selling auto parts on the fly.

The Azati team have designed a full application structure and logic:

• Homepage;
• Sign-up/Sign-in pages for buyers and sellers;
• Filters settings for buyers to receive offers;
• Filter settings for sellers to send offers;
• Active orders and orders history pages;
• Checkout page.

Our Data Scientists have successfully implemented a prototype system into an already functioning calorie counting application, that can instantly estimate the calorie content of complex dishes by images analysis. Such a solution can be useful in such domains as agriculture, catering, sports or even for everyday life.

OBJECTIVE
In recent years, it has become possible to use deep learning to recognize objects in images with high accuracy. We realized Azati could apply new technologies to the problem of food quality estimation to simplify the process and provide the user with the fastest and most efficient result.

SOLUTION
While solving challenges we developed a small script written in Python. The prototype takes an image as the input and returns a set of frames where each component is circled with a square in which calories are indicated, and the total result of the whole dish is displayed. Check out the screenshots below to see how the results look.

RESULTS
We made it possible to process images of compound dishes and calculate their calorie content using machine learning and computer vision. The model recognizes each product quite accurately and distinguishes one component from another. For more complex object classification, the model requires additional data and extra training.