Design My Day uses artificial intelligence and machine learning to deliver personalized life‑guidance that helps users boost self‑efficacy and make intentional choices. The platform analyzes a person’s current state and future goals, then offers tailored suggestions and crowdsourced ideas to break negative thought patterns and inspire actionable steps. By combining data‑driven insights with community input, it aims to turn everyday decisions into purposeful progress.
Funding
Funding not disclosed
Founders
Product
Problem
Individuals experiencing depression, helplessness, or discouragement often struggle to identify concrete, actionable steps that can improve their mood and sense of control, leading to a cycle of inactivity and reduced self‑efficacy.
Solution
Design My Day leverages artificial intelligence and machine learning to generate personalized daily activity recommendations aimed at boosting self‑efficacy. Users receive tailored suggestions that align with their personal goals and desired future self, while also accessing a curated pool of crowdsourced ideas for additional inspiration. The platform presents these actions in an easy‑to‑follow format, encouraging users to take small, intentional steps each day to counteract negative thoughts. By tracking completed activities, the system refines its recommendations over time, creating a feedback loop that reinforces positive behavior and supports mental well‑being.
Target Audience
Primary users are individuals seeking to improve their mental health and self‑efficacy through structured daily actions, including those dealing with mild to moderate depression or feelings of helplessness.
Features
- AI-driven recommendation engine that matches daily activities to individual goals and emotional state
- Crowdsourced idea library offering diverse, user‑generated suggestions for overcoming low mood
- Simple daily planner interface that organizes recommended actions into manageable tasks
- Progress tracking that records completed activities and informs future recommendations
- Adaptive learning model that improves suggestion relevance based on user feedback and behavior