Krympress reports:
Rebranding the logo is a step that requires a strategic approach, but is often limited in time and budget. With the advent of AI instruments, the companies are increasingly trying the generators of logos in the framework of the redesign. In this case, we tell how one small technological company updated the visual style using the neural network, what goals it set before the redesign, how the process built, and how everything ended.
How began to redesign: goals and context
The company was engaged in digital solutions in the field of education. The old logo was developed at the early stage of the existence of the brand and no longer corresponded to the current tasks. Over the years, the number of customers has grown, the product has expanded, the structure of communications has changed.
It was decided to update the visual image, preserving recognition, but at the same time adapt the logo to digital formats, minimize extra details and make the corporate sign more universal. The key goal is to improve the perception of the brand online without a sharp visual gap.
CHICK CHIED: Why was I chosen
The team examined different paths — from a studio redesign to internal study. But in the end, they chose a neural network generator for several reasons: it was necessary to get the first ideas quickly, test several directions and understand how to develop the current form of the logo. It is especially convenient to use GPT without restrictions They turbo to immediately get dozens of non -standard decisions without restrictions on the number of requests and editors.
The budget also played a role: in the context of limited resources, AI-platform allowed to avoid long briffs, approvals and design iterations. Generation made it possible to see dozens of options already on the day of the launch of the project.
How the process passed: stages and tools
The process began with the preparation of a brief description of the brand: a niche, values, an audience, key associations. In the generator, the name of the company was set, the directions in style and mood were chosen — from minimalism to technology. The algorithm proposed more than 40 options for the logo.
Of these, the team selected 5 of the most relevant, held an internal vote and chose 2 finalists. These options were modified manually — the font was adapted, proportions were adjusted, and the balance was improved. Ready -made logos tested on the site, presentations and social networks.
The main stages of redesign using AI
-
Collection of information about the current brand and its goals
-
Generation Settings: choice of style, palette, parameters
-
Selection and analysis of visual options
-
Final refinement and adaptation of the logo to the media format
Even without a designer in the team, it was possible to build a process that gave a result in just a few days.
Result: What was the updated logo
Unlike the previous version, the new logo has become more concise and more modern. The color palette remained close to the original, but was cleared of additional shades. The font has become more readable and technological.
The symbol was simplified to a sign that is easily scaled for the application icon. The logo became universal: it looked equally good in the web interface, on the business card and in the avatar of social networks. The team noted that the updated style is felt more confident, and users perceived the changes positively.
Conclusions: what worked and what required improvement
AI turned out to be an excellent tool for a quick start and generation of ideas. Its strength is variability, fast recoil and the ability to evaluate different visual decisions without investments in prototypes. The visual update turned out to be in a short time and gave a tangible result.
However, automatic generation did not solve everything. Some elements demanded manual revision, and the selection of options turned out to be time -consuming: a large number of similar solutions complicated the final choice.
It was also important to test the logos in real situations — the only way it became clear how they work in practice.
Is it possible to repeat this approach in other projects
The experience was universal for small and medium projects. AI copes well with tasks where the creation of a complex visual system is not required, but a clear and clean logo is needed. This is especially true for startups, online services and companies at the update stage.
For the successful use of the neural network, three conditions are important in redesign: a clear understanding of tasks, competent filtering of the results and the willingness to make manual editing. The ideal script is to combine the generation with the subsequent visual audit and tests in the audience. In such conditions, AI does not become a replacement for the designer, but an accelerator of the process.
Questions and answers
What result was obtained faster — manually or with AI?
The generation using the neural network made it possible to get the first set of logos for an hour. The full cycle with refinement took several days, while a similar process manually would take several weeks.
How unique did the logo turn out to be?
Initially, the options were close to the template, but after manual revision, the final sign became visually original and did not repeat typical solutions.
Were there difficulties in working with AI?
Yes. The main difficulty is the selection of dozens of similar options. An additional internal testing was required to choose the strongest.
Is this approach suitable for a large brand?
To generate concepts — quite. But the final result for serious companies should include a strategic audit, adaptation for brandbooks and a full design study.
Crimea news | Krympress: Latest news and main events
Comments are closed.