Project Screenshot

A Question & Answer platform where users can find answers to popular search queries

  • Idea founder Alex Gonzalez tasked us with creating a website that would answer popular search queries. Alex wanted to start with a US-oriented minimum viable product (MVP) and develop further when it gains popularity.


    The design needed to be clear to all users regardless of their background and age. Mainly, he emphasized the importance of a clearly identifiable search bar, since it’s the website’s most valuable feature. He also wanted clear content categorization and subcategorization and access to other sources in case there’s no answer on

  • Analysis

    Google processes over 40,000 queries every second, which is about 3.5 billion a day. Platforms like tend to do very well precisely because people like to ask questions and enjoy having them answered. And the more question and answer platforms are out there, the more answers there are, and hence the further information gets spread.


December 2016 – March 2017
UI/UX - 1 Backend - 1 Frontend - 1
Created UX and UI

At Alex’s request, we emphasized clarity and user friendliness in design. Our goal was to create a comfortable system for navigating content and add an easy-to-spot search bar.

When designing, we studied the examples of,, and other similar websites. This helped us understand what successful platforms of this type do to achieve their status. As a result, we got a pleasant looking website in neutral colors with the search bar on top, most frequently queried articles on the front page, and a color-coded category list available both by scrolling and via a drop-down menu in the top right corner.

Developed a functional backend is written in Python 3.5 and based on the Django framework. We used PostgreSQL as the database.

We customized the Django admin panel so writers and editors can manage content on the website, implementing a wide variety of comfortable features. The edit feature is powered by CKEditor.

Using Google AMP helps ensure that all content is loaded quickly, which is important for SEO and website promotion. We implemented rich snippets and structured data by following Google’s Webmaster Guidelines, which helps Google understand the content on better.

We also integrated APIs from YouTube and WikiHow to provide users with answers to queries that has no articles for.

​​​​​​​Developed the frontend

Having analyzed the market carefully, Alex knew that the desktop version of would likely attract the most traffic. However, he wanted the platform to be accessible to a wider variety of users, so we also developed a mobile version of the website.

We developed with JQuery, which we used for document traversal and manipulation along with event handling. Less was used as the CSS preprocessor, making working with styles and components a lot more comfortable. To implement the comment section, we used Disqus, which offers a wide feature set including comment analytics and management. We also used Algolia’s Autocomplete.js to implement the smart autocomplete feature.

Implemented features

We enhanced the search bar with smart suggestions to help people reach their answers quicker and formulate their questions better. Instead of having to type a whole question, users only need to choose the question they want from the list. pulls answers from third-party sources to answer users’ questions even if there isn’t a related post. This feature is especially useful for the MVP, given that the amount of content on the website is still growing and the site wants to help users find the answers they've come for.

For now, all articles are divided into eight different categories that can be further subcategorized. This makes it easier to find exactly what you’re looking for and search through articles on a topic. The website administrators can customize the number of categories and subcategories, so there can be more or less over time.

As server-side rendering is crucial for search engine optimization and therefore for the promotion of, we made sure that all important content is rendered on the server.

Using the official AlgoliaSearch library, we created an index class that manages indexes on and is used in the article view to select posts related by tags. We also used Algolia to implement the search results page, where articles are listed according to their title and description.

We implemented custom file storage that identifies images and sends them to Cloudinary. Cloudinary offers a wide variety of tools for managing and transforming images.

Technology Stack

Python icon
Django icon
Algolia icon
Disqus icon