The project boiled down to creating a proof of concept version (POC) of the Python application in order to demonstrate its feasibility and verify a practical potential of the concept.

In brief, the main user flow looks as follows: user inputs name of the business, selects industry, chooses a few pre-designed styles from templates, picks up colour schemes. These several steps were supposed to lead to the automated generation of a logo with a watermark.

A fundamental job that determined the success of the project was designing the model of logo generator's engine.

This came down to the following flow:

• at the beginning of the process, a batch of logos with random parameters was created,
• from this batch, those which broke some base design principle were filtered out i.e. those without sufficient contrast between colours,
• finally, a Machine Learning classifier, trained on examples hand-labeled as good or bad, filtered out those logos that didn’t reach a certain quality.

The model used as machine learning classifier was a neural network created in TensorFlow. This framework exposes to programmer low-level API that allows having better control over the architecture of the neural network.