How we released app 4th version in the
About the project
CamFind app uses visual search technology. By hovering any item with a smartphone's camera it can recognise it and find a variety of suggestions within different marketplaces. The app has a smart UX design that helps to perform its main function in two steps: user takes a picture of any item then CamFind analyzes it and shows search results.

The owner of CamFind project – CloudSight – contacted us in 2019 with two main requests:
  • UI/UX improvement;
  • New features implementation: integration with marketplaces' API and changing monetization conditions.
Our team updated the app interface based on the provided by CloudSight new mockups as part of the redesign work. As a result, CamFind gained up-to-date style and improved UI/UX design.
Going into details:
  • Search bar has became more convenient;
  • New elements and dynamic animations were added;
  • Social elements we added: now users can share the images they recognized, add friends, like and comment on the activity of other users.
In addition we came up with a new way of monetization where marketplaces pay commision.
AI is the base for the CamFind app. CloudSight is a provider of neural network with an ability to analyze and recognize objects in pictures. It transfers the collected data from picture into text format.
The service for neural network models was created on TensorFlow and connected to Ruby on Rails backend. The neural network was trained in a hybrid format: machine learning and humans worked together to teach it quickly recognize items. The unique technology allows for real time responses while also learning and improving over time.
There are challenges we faced and successfully solved:

  1. Under strict deadline conditions, we updated the layout of the entire application;
  2. Team made responsive Lottie-animations for correct display on a wide range of devices;
  3. We developed a custom keyboard for iOS. Our developers studied the guides and best practices & picked up a solution;
  4. Our QA-engineers used Pixel perfect UI - UI that allows us to achieve full compliance with the initial design.
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