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Smart Suggestion system

Smart Suggestion system

Product Design

Product Design

Interaction Logic

Interaction Logic

Gen AI

Gen AI

Vibe Coding

Vibe Coding

IxDF — AI for Deisgners 2025

IxDF — AI for Deisgners 2025

CONTENTS

Introduction

This is a speculative, AI-native product concept, focused on ethical use of bio-data, trust-building UX, and human–AI collaboration in a sensitive domain. The project explores how contextual intelligence and biometric signals can be used to reduce decision paralysis and help users begin their sessions with reduced effort without removing their sense of agency.

Introduction

This is a speculative, AI-native product concept, focused on ethical use of bio-data, trust-building UX, and human–AI collaboration in a sensitive domain. The project explores how contextual intelligence and biometric signals can be used to reduce decision paralysis and help users begin their sessions with reduced effort without removing their sense of agency.

role

Product Designer

Vibe coding

AI Tools

Perplexity + Claude

UX Pilot

Filgma + Make

timeline

3 weeks

The Objective

Reduction in decision paralysis through NeuroTone's smart suggestion engine. Helping users to start journeys faster based on biometric and contextual data.

The Problem Space

The health-tech app market is growing rapidly, led by apps like Headspace, Whoop & Endel but faces user pain points: lack of personalization, overwhelming content catalogue.

Opportunities explored for solutions (in collaboration with Claude & Perplexity)
1. Adaptive and emotionally intelligent content. 💚
2. Personalization layer to fine tune meditation 🧐
3. Strong and transparent privacy protections 💚

The Process

  1. Discovery & Research Workflow

Problem Statement - The lack of personalization, overwhelming content catalogue leading to friction in habit building and decision paralysis.

Focus - Content personalization and integration of wearable tech for analysis and progress tracking.

HMW enable users to benefit from biometric-driven suggestions and actionable practice insights?

HMW enable users to benefit from biometric-driven suggestions and actionable practice insights?

HMW use EEG data to deliver personalized and actionable meditation insights for users?

HMW use EEG data to deliver personalized and actionable meditation insights for users?

  1. Persona Crafting

Partnering with LLMs, I refined the target audience. Followed by uncovering their key goals, pain points, and translated these insights into a persona.

  1. The Suggestion Engine

Through collaboration with LLMs and a data scientist, the product evolved into a context-aware predictive system that considers:

My goal was to design while striking a balance between engineering and user experience. Therefore, before jumping into building the interface and interactions it was critical to layout the technical aspect of the suggestion system.


Understanding how the product would actually work gave me clarity on how to design around its intelligence layer (AI/ML aspect), data requirements and technical constraints.

Before jumping into building the interface and interactions it was critical to layout the technical aspect of the suggestion system. Understanding how the product would actually work gave me clarity on how to design around its —- and technical constrains. My goal was to designing while striking a balance between engineering and user experience.

  1. Design & Prototyping

Phase 1 : Through a lightweight product requirement document, I began to explored the concept and generated the first version of the user journey for the suggestion flow.

To quickly validate the concept, I ran informal tests with peers and fellow designers, walking them through the flow and taking feedback on what they expected at each step.

For rapid validation of the concept, I ran informal tests with peers and fellow designers, asking them to walk through the flow and talk about what they expected at each step.

Phase 2 : After adjusting and validating the user flow I outlined the key screens: context awareness, smart suggestion, post session analysis. Utilised tools like Google Stitch for UI iteration and Figma Make to transform screen design into interactive prototypes.

  1. Biometric Data Interpretation

Although the system could in theory generate suggestions quickly, biometric analysis and wearable APIs introduce unavoidable latency. Leading to a soft check-in flow that runs in parallel to data collection and analysis.

Phase 2 : From a lightweight PRD, I collaborated with some LLMs to rapidly explore concepts and generate the first version of a user journey.

  1. Smart Suggestion and Tracking

The smart suggestion screen provides the users with sound therapy options that are the best match for the user based on contextual data analysis.


The "why this journey?" module was designed to serve as a trust building layer and advocate for AI transparency.

  1. Dashboard & Post session analysis

After each session, the product reflects back patterns rather than judgments: - How the body responded - How context may have influenced the experience - How future suggestions may adapt

Insights & Reflections

Collaborating with the LLM for UX research and Idea generation was valuable because it brought diverse perspectives and speeded up decision-making. It was powerful to jumpstart an idea and lay out some level of foundation for rapid prototyping.

Next Steps after fully building the prototype

Run usability sessions with 5–8 regular meditation practitioners to observe:

  • How they move through the flow

  • how clearly they understand the biometric feedback.

Metrics to study with 5–8 participants

  • Task success rate

  • Error and hesitation rate

  • Time on key tasks

Next Case - Cinera Website