TailTrack

An AI-powered health tracking system that helps new dog owners understand their dog’s wellbeing through real-time insights, alerts, and guided care.

Timeline

Fall 2025

Tools

Figma, Illustrator, ChatGPT

Revision Team

Faith Innes, Justine Santos, Dorsa Roohbakhsh & Minju Jung

Original Team

Adrienne Luong, Anton Kouznetsov, Jasmine Dacanay

& Long Le

The Challenge

The original TailTrack concept aimed to help new dog owners monitor their pet’s health, but early feedback revealed significant usability gaps. Users struggled to interpret health indicators, found the interface overwhelming, and often were not sure what the data meant or how to respond. Ambiguous terminology, unclear visual hierarchy, and limited guidance made the experience feel difficult.


Our challenge was to refine TailTrack’s interface, language, and overall user experience to make pet health insights simple, intuitive, and emotionally reassuring. The goal was not only to improve visual clarity but to reshape how the app communicates so it truly supports pet owners in understanding and caring for their pets with confidence.

Homescreen

Unclear scale

Scale out of 100%

Shows stats from different type periods

Ears on graph as indicator for the

dog’s well-being

No distinction to separate types of alerts

Different colours to show alert severity

Progress

Unclear scaling

Generic / repetitive graphing system

Use of icons to make stats more visual

Clear percentage scaling system

Gamification & badge system to motivate user

Variety of graphing systems used for

visual clarity

High information density

Coaching

Very small text with little visual contrast between the content and background

Training progress tracker

Added visual to illustrate what actions the AI is suggesting to the user

FAQ section to support the user’s experience

Generalized AI suggestion

Learning

The learning section of TailTrack was introduced as a new feature in the revision of this application. This feature was added to benefit the user in the coaching aspect of the app, using AI to suggest users the best training plans relevant for their pet based on their pets activity, nutrition and stress levels.

AI summary of the user’s pet’s condition with training plan suggestions

AI tells the user how the training plan will specifically benefit their pet

Overview of what the plan entails

Tag system to clearly show aspects of the training plan

Varied categories of training plan suggestions

Interface Redesign

Homescreen

Unclear scale

Scale out of 100%

Shows stats from different type periods

Ears on graph as indicator for the

dog’s well-being

No distinction to separate types of alerts

Different colours to show alert severity

Progress

Unclear scaling

Generic / repetitive graphing system

Use of icons to make stats more visual

Clear percentage scaling system

Gamification & badge system to motivate user

Variety of graphing systems used for

visual clarity

High information density

Coaching

Very small text with little visual contrast between the content and background

Training progress tracker

Added visual to illustrate what actions the AI is suggesting to the user

FAQ section to support the user’s experience

Generalized AI suggestion

Learning

The learning section of TailTrack was introduced as a new feature in the revision of this application. This feature was added to benefit the user in the coaching aspect of the app, using AI to suggest users the best training plans relevant for their pet based on their pets activity, nutrition and stress levels.

AI summary of the user’s pet’s condition with training plan suggestions

AI tells the user how the training plan will specifically benefit their pet

Overview of what the plan entails

Tag system to clearly show aspects of the training plan

Varied categories of training plan suggestions

Interface Redesign

Homescreen

Unclear scale

Scale out of 100%

Shows stats from different type periods

Ears on graph as indicator for the

dog’s well-being

No distinction to separate types of alerts

Different colours to show alert severity

Progress

Unclear scaling

Generic / repetitive graphing system

Use of icons to make stats more visual

Clear percentage scaling system

Gamification & badge system to motivate user

Variety of graphing systems used for

visual clarity

High information density

Coaching

Very small text with little visual contrast between the content and background

Training progress tracker

Added visual to illustrate what actions the AI is suggesting to the user

FAQ section to support the user’s experience

Generalized AI suggestion

Learning

The learning section of TailTrack was introduced as a new feature in the revision of this application. This feature was added to benefit the user in the coaching aspect of the app, using AI to suggest users the best training plans relevant for their pet based on their pets activity, nutrition and stress levels.

AI summary of the user’s pet’s condition with training plan suggestions

AI tells the user how the training plan will specifically benefit their pet

Overview of what the plan entails

Tag system to clearly show aspects of the training plan

Varied categories of training plan suggestions

Prototype

TailTrack

An AI-powered health tracking system that helps new dog owners understand their dog’s wellbeing through real-time insights, alerts, and guided care.

Timeline

Fall 2025

Tools

Figma, Illustrator, ChatGPT

Revision Team

Faith Innes, Justine Santos, Dorsa Roohbakhsh & Minju Jung

Original Team

Adrienne Luong, Anton Kouznetsov, Jasmine Dacanay

& Long Le

The Challenge

The Challenge

The original TailTrack concept aimed to help new dog owners monitor their pet’s health, but early feedback revealed significant usability gaps. Users struggled to interpret health indicators, found the interface overwhelming, and often were not sure what the data meant or how to respond. Ambiguous terminology, unclear visual hierarchy, and limited guidance made the experience feel difficult.


Our challenge was to refine TailTrack’s interface, language, and overall user experience to make pet health insights simple, intuitive, and emotionally reassuring. The goal was not only to improve visual clarity but to reshape how the app communicates so it truly supports pet owners in understanding and caring for their pets with confidence.

The original TailTrack concept aimed to help new dog owners monitor their pet’s health, but early feedback revealed significant usability gaps. Users struggled to interpret health indicators, found the interface overwhelming, and often were not sure what the data meant or how to respond. Ambiguous terminology, unclear visual hierarchy, and limited guidance made the experience feel difficult.


Our challenge was to refine TailTrack’s interface, language, and overall user experience to make pet health insights simple, intuitive, and emotionally reassuring. The goal was not only to improve visual clarity but to reshape how the app communicates so it truly supports pet owners in understanding and caring for their pets with confidence.

New Design System

Colour Scheme

Typography

H1

H1

Caption

H2

Body

H2

H3

Body 1

#FFE689

#F89E0B

#FF633B

#49CBFF

#CFFC1A

#1F1F22

#F96361

#000000

#FEE2E2

#E4E1DE

Body 2

Takeaways

Takeaways

Working within an established design framework pushed me to identify what aspects of the design were worth preserving and where deeper changes were needed to create a clearer, more user-centered experience. Introducing AI insights and automated alerts also emphasized the importance of transparency and trust, especially when users rely on the product for guidance about their pet’s wellbeing. Reworking the user flow showed me the value of grounding decisions in real user behavior rather than assumptions, simplifying key journeys to reduce friction and support natural routines. Collaborating closely with my team strengthened the outcome, as combining research, design, and strategy perspectives helped create a solution that was both practical and emotionally supportive. Ultimately, this project reinforced that meaningful improvements come from empathetic listening, thoughtful iteration, and a willingness to challenge existing patterns to better serve users.

Interface Redesign

Unclear scale

Scale out of 100%

Shows stats from different type periods

Ears on graph as indicator for the

dog’s well-being

No distinction to separate types of alerts

Different colours to show alert severity

Homescreen

The initial homepage featured a circular diagram to display a pet’s activity and nutrition levels, along with any urgent wellbeing alerts. In the revised version, the diagram is more visually branded and uses a clearer scale, while alerts now employ a distinct color system to indicate the severity of each type.

Unclear scaling

Generic / repetitive graphing system

Use of icons to make stats more visual

Clear percentage scaling system

Gamification & badge system to motivate user

Variety of graphing systems used for

visual clarity

High information density

Progress

The original progress feature relied heavily on line graphs to show a pet’s stress levels throughout the day, along with alerts for peak moments and their causes. In the revised version, we introduced more varied data visualizations and added a badge system to gamify the experience, giving users clearer insights and greater motivation to stay engaged with their training plans.

Very small text with little visual contrast between the content and background

Training progress tracker

Added visual to illustrate what actions the AI is suggesting to the user

FAQ section to support the user’s experience

Generalized AI suggestion

Coaching

The original coaching feature offered generalized training plans supported by an AI chatbot that guided users through each step. In the revised version, the AI recommendations are more specialized and visually driven, and the chatbot now includes a voice-to-chat option that makes the training experience more accessible, engaging, and customizable for each user.

The original coaching feature offered generalized training plans supported by an AI chatbot that guided users through each step. In the revised version, the AI recommendations are more specialized and visually driven, and the chatbot now includes a voice-to-chat option that makes the training experience more accessible and customizable for each user.

Learning

The learning section of TailTrack was introduced as a new feature in the revision of this application. This feature was added to benefit the user in the coaching aspect of the app, using AI to suggest users the best training plans relevant for their pet based on their pets activity, nutrition and stress levels.

AI summary of the user’s pet’s condition with training plan suggestions

AI tells the user how the training plan will specifically benefit their pet

Overview of what the plan entails

Tag system to clearly show aspects of the training plan

Varied categories of training plan suggestions