Owner-reported updates introduce inconsistency and subjective interpretation
Limited real-world visibility increases training failures and dog wash-outs.
Lack of ongoing signals prevents timely intervention and course correction
Wash-off Risk
Recall Bias
Data Continuity
"I'm finding it harder and harder to keep track of everything I need to keep track of"
"Once the dog leaves the facility, I lose visibility. I rely on owner recall, which makes it difficult to adjust training accurately"
Owner
Trainer
“I was in a situation where I cannot get a dog that fails training because I do not have enough money… I just can’t do that.”
Owner
Conceptualization
Through visualization of the service dog training process, we identified areas for improvement in efficiency and highlighted challenges faced by stakeholders. Specifically, we pinpointed two key pain points:
During community research with service dog trainers and owners, we found that fewer than 1% of Americans who could benefit from a service dog are able to access one due to high training costs.
Trainers reported that nearly 60% of dogs are washed out during training, often after significant time and financial investment. Through interviews and observation, we identified that limited trainer availability, repeated training steps, and poor visibility into training data contribute directly to these outcomes.
Interviewing Stakeholders
Concept Co-Development
Concept selection
Concept Prototyping
Evaluative research
Prototype Refinement
Solution implementation
User flows & Prototyping
Week 8 - 13
Narrative Development
Narrative refinement
Partner Pitch & Feedback
Partner pitch &
Feedback
Week 14 - 15
Tasks
Duration
Phase
Week 1 - 2
Opportunity Framing
Insight Generation
Generative Research
Ideation
Idea Development
Idea Selection
Intent Coversation
Brief Exploration
Partner management
Team Building
Project Planning
Problem Definiton
Discovery & Scoping
Service Dog Exploration
& Ideation
Week 3 - 7
Design Process
The Solution
Create an AI-assisted training, wearable and companion app, that detects and interprets the owner’s condition, translating multiple situations into a single trigger. By tracking training data over time, the system helps reduce wash-off rates.
Laika is an AI-enabled service design project that helps service dog owners and trainers simplify the complexity of daily training routines. By pairing a wearable (watch + dog collar) with a mobile app, Laika provided real-time guidance, session logging, and dashboards to support consistent, accessible training.
AI - assisted training app
Role
Product Designer
End-to-end design from research, design, to delivery
Stakeholder interviews, IA, wireframes, prototypes, and user testing
Team
Manager (Angel)
Researcher (Anupama)
Designer (Me)
Designer (Tania)
Tools
Figma
Adobe After Effects
Miro
Notion
Company
Microsoft
Parsons
5 Months
Duration
The Problem
Service dog training is constrained by limited trainer availability, dogs are required to learn multiple cues for similar actions, and owners struggle to track and interpret training data.
Setting up the Ecosystem
This setup flow establishes the ecosystem by pairing the watch and collar to work together.
Each profile surfaces role-specific insights instead of a one-size-fits-all dashboard.
Owners see health and episode trends, while dogs’ profiles emphasize response quality, energy, and sleep consistency.
Designing distributed
AI System
Laika was conceptually designed to integrate with Microsoft Azure’s AI and IoT services, enabling signal detection, pattern recognition, and longitudinal data analysis across devices.
Passive sensing of physiological and behavioral signals
Dog Wearable
Real-time prompts and confirmations during critical moments
Watch
Long-term insights, trends, and training progress
Mobile App
Start, Stop & Continue
Start
Clarifying team roles and strengths early to align expectations and ensure balanced collaboration
Practicing tool agility—switching methods or platforms when current ones limit progress
Stop
Over-indexing on iteration without converging, focus on narrowing down solution portfolios
Limiting research to direct users broaden lens to include analogous and edge-case users
Continue
Involving stakeholders early as co-creators, not just interviewees
Testing with low-fidelity prototypes pre-MVP and crafting clear user communication around it
