Overview
Doppl.ai is a mobile application that creates a highly accurate digital version of yourself using AI while storing your personal data on the blockchain.
Business goal
Raise investment by communicating the product vision with high-fidelity designs and building a proof of concept (POC).
Team
UI/UX Designer
Senior/Lead Designer
Product Manager
Chief Product Officer
Business Analyst
Front-end Developers
AI Engineers
My Role
UI/UX Designer, responsible for:
Visual design
User research
Usability testing
Desirability testing
Time frame
4 months
Why Doppl?
Ever wished you could be in multiple places at once?
Doppl lets you create a digital version of yourself, called a "Doppl," to handle tasks, gain insights, and even have unique experiences with celebrities.
This digital version of you can help you save time by handling repetitive tasks and be in multiple places at once.
Additionally, it provides insights into personal growth and allows you to have unique experiences with celebrities.
Doppl is fully under your control and can act on your behalf in various situations, giving you more time for yourself and your loved ones.
Discovery & Product Vision
The drawing board
During our initial research, we explored various AI products as references and identified potential use cases and beneficial features for our app.
We also discussed designing the app to provide value and ensure global adoption. Our findings highlighted significant concerns about AI's impact on security, privacy, and the global market, while also indicating interest in AI for healthcare and daily tasks. These insights were validated in subsequent phases.
User flows & low-fidelity designs
In the initial stages, we defined the core flows that would help us to communicate the product potential. We ran several brainstorming sessions where we mapped and prioritized which flows we should focus on each week.
In these sessions, we listed some potential users (non-validated personas), jobs to be done, features related to these goals, and a hierarchy between those features to understand the big areas of the app.
Onboarding and Chat flows were the priorities,
so I started the wireframe designs to define the information architecture and work with the content.
Branding & Moodwords
Since we had a lean team, I helped with the brand design as we defined the typography and colors.
By conducting branding sessions, we were able to identify the main keywords and concepts we wanted to translate with the Doppl brand. After that, a competitor analysis focusing on the visual aspects showed some opportunities to stand out on the visual side regarding colors and typography.
With those insights, we decided to follow with a warm and vibrant color palette alongside a bold and strong typeface.
With the basic branding guidelines, we defined the visual style of the app connected to the brand. At this moment, we also designed and developed a landing page to start building our waiting list.
High fidelity UI
During this phase, we enhanced the concept screens of the main flows that were previously developed in the low-fidelity wireframing stage, adhering to the brand and visual guidelines.
While our main focus was on high-fidelity designs, the entire team also worked on defining business requirements, mapping user flows, and designing concept screens for new features.
Proof of Concept (POC)
As the project moved forward, it was time to develop a POC to show to some potential investors. Working with the engineering team, we designed a simplified web version of the product to be developed faster, using existing solutions and public APIs. We adapted the designs to match the solutions we were using.
Product Validation
Finally, we had an opportunistic period to validate our key hypothesis.
We ran a usability test using a tool called Maze to validate the Onboarding and Chat flows.
The test showed some improvements we needed to make regarding some Chat interactions and buttons, and we were able to measure the perception of the users by scanning their faces and recording their voices during the onboarding.
Product Validation
Surveymonkey
After the Maze testing, we put out surveys to both our waiting list (leads acquired from the landing page) and a recruited sample using SurveyMonkey, which had 102 and 421 responses respectively.
Both surveys had the same questions and structure, approaching the users regarding their interest in creating their Doppls, use cases for the Doppls, security, privacy, visual and voice accuracy level, and interest in sharing their Doppls with others.
With the responses, we delivered Executive Summaries and detailed reports with a lot of insights for each discussion topic, demographic data aligned to use cases, and guidelines on how to promote the product to acquire more users.
Outcomes & Learnings
Takeaways from this project
Through this project, I realized the vital role of research and validation in each product development phase.
Additionally, I learned that effective teamwork, clear communication, defined objectives, and smart prioritization streamlined the design processes and led to high-quality outcomes.
Doppl in the press
Doppl has been highlighted in articles and blog posts by leading media outlets globally. To discover more about Doppl, explore the following links.
Runor Ekpagha.
