Lotic.ai
Lotic is an AI-driven, privacy-first well-being ecosystem designed to help individuals gain actionable insights into their mental health. By harnessing the power of spoken word and personal storytelling, Lotic empowers both individuals and organizations to make well-informed decisions about their well-being.
Individuals seeking mental wellness support often felt overwhelmed by an abundance of generic resources with unclear credibility. Our goal was to build a privacy-first platform that leverages AI-driven insights from spoken personal stories, providing tailored guidance that resonates with unique user contexts. By focusing on trust, personalization, and meaningful data privacy, Lotic aimed to address the growing need for credible, actionable well-being resources.
As an early hire, I shaped the product as a UX/UI and visual designer, conducting user research, mapping journeys, defining architecture, and designing interfaces. Collaborating with diverse teams, I ensured user-focused, strategically aligned solutions.
To understand user needs, we began with a series of surveys. We discovered that users often felt overwhelmed by the volume of information and struggled to find content that matched their personal mental health goals. Key insights included:
Users want personalization: tailored recommendations rather than generic content.
Trust and credibility are crucial in mental wellness guidance.
Simple, empathetic language resonates better than clinical jargon.
Key insights included:
Transparent data usage and privacy practices boost user confidence.
Spoken word enables more natural self-expression, leading to richer, more meaningful insights.
AI-driven insights are more impactful when combined with personal narratives rather than static text entry.
Our research identified personas like "The Overwhelmed Professional" seeking quick guidance and "The Reflective Journaler" valuing deep exploration. Both emphasized transparency and data privacy.
The user journey:
Share their story.
Receive AI-curated insights.
Feel reassured by robust privacy measures.
Refine their well-being strategies through reflection and learning.
We brainstormed ways to present AI-driven insights from personal stories, using card sorting and low-fidelity sketches to explore:
Clear distinction between raw input (spoken words) and AI-generated recommendations.
Seamless navigation for privacy settings, story submissions, and personalized dashboards.
Visual patterns that evoke trust, simplicity, and comfort.
The refined architecture ensures privacy controls, prompts, and insights are intuitive and easy to access.
We transformed wireframes into high-fidelity prototypes that focused on creating a calm, supportive user experience. Key design highlights included:
Warm, gentle colors and friendly, readable fonts for a welcoming feel.
Clear “Record” controls with prominent privacy indicators.
Intuitive dashboards showcasing AI-generated insights with transparent explanations.
We conducted usability tests with target users interested in AI-driven mental health tools. While they valued the personalized insights, feedback highlighted the need for clearer explanations of AI terms and data transparency.
In response, we:
Enhanced labels and added brief explainers on how AI transforms input into insights.
Highlighted privacy prompts to ensure users could easily manage data-sharing preferences.
Simplified navigation labels to reduce confusion and improve clarity.
The final solution seamlessly blended spoken word inputs with AI-driven insights within a privacy-first framework. Users gained a platform that respects their personal stories, delivers tailored guidance, and ensures transparent data practices. This empowers individuals and organizations to feel informed, secure, and supported in their journey toward better well-being.
Outcomes:
User satisfaction increased by 8%, driven by personalized insights and clear privacy controls.
Engagement improved by 24% with the addition of the spoken input feature.
Positive feedback highlighted enhanced trust and ease of use, reinforcing the platform's value.
Working on Lotic emphasized the importance of building trust in AI-driven solutions, especially in sensitive domains like mental health. Involving users early on and openly addressing privacy concerns made the difference between a merely functional tool and a truly supportive ecosystem.
Next steps could include:
Adding more robust explainability features to help users understand how AI arrives at insights.
Exploring more interactive storytelling prompts or guided spoken sessions.
Conducting long-term studies to assess how Lotic’s personalized insights affect well-being outcomes over time.
Through this project, we demonstrated:
Thoughtful integration of AI-driven features into a sensitive user experience.
Empathy-driven design that highlights user data privacy and transparent insights.
The ability to iterate rapidly based on user feedback, refining both interface and messaging.
Strong collaboration skills across multidisciplinary teams to deliver a meaningful, privacy-first well-being ecosystem.