Pawfect
｜ UX AWARDS NEW TALENT WINNER ｜
ROLE and RESPONSABILITIES
I identified a systemic gap in pet adoption platforms: matching mechanisms prioritize availability over compatibility, contributing to adoption returns. I led the product concept, research, and usability validation for Pawfect, from early ideation to real-user testing and public-facing storytelling.
My main contributions included:
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Product ideation and problem framing
Defined the product vision around reducing pet adoption mismatch and return rates. -
Secondary research and competitive analysis
Conducted literature review and market analysis to identify gaps in existing adoption platforms. -
Usability testing (20 participants)
Product storytelling and communication: produced the product introduction video and designed marketing & social media visuals to communicate the concept clearly to the public and judges.
I worked closely with teammates on interaction and visual execution, while primarily driving the user research, insight generation, and experience logic behind key product decisions.
Problem Framing
Pet adoption platforms primarily function as searchable directories.
They optimize for visibility — not long-term compatibility.
This results in:
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Emotional mismatch
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Post-adoption returns
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Animal stress and shelter burden
The core issue is not discovery. Adopters do not lack options.
They lack structured compatibility guidance.
Research & Market Analysis
Through secondary research and competitive analysis, I identified:
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Most platforms emphasize pet profiles, not adopter profiles.
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Matching systems, if present, lack transparency.
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Emotional reassurance is not systematically embedded.
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Shelter workflows and adopter expectations operate under different mental models.
This informed a dual-role system architecture:
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Adopter-side experience
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Shelter-side operational interface
USABILITY TESTING and VALIDATION
To validate the product flow and role-based usability, I conducted usability testing with 20 participants:
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15 general users testing the adopter-side journey
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5 former shelter volunteers testing the shelter management interface
This dual-perspective testing revealed different mental models, priorities, and friction points between adopters and shelters, which informed subsequent interface and flow iterations.
Key Insight — Mental Model Asymmetry
Adopters prioritized:
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Emotional reassurance
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Clarity
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Reduced uncertainty
Shelter-side users prioritized:
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Safety documentation
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Screening efficiency
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Information completeness
Critical Iteration: AI Trust & Explainability
The early prototype displayed:
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Compatibility percentage score (e.g., 82% match)
However, during usability testing, participants consistently expressed:
“Why is it 82%?”
“What is this based on?”
“How does the system know this?”
The absence of explanation created skepticism. The score alone increased anxiety rather than confidence.
Insight
Trust in AI is not built by precision. It is built by transparency.
Iteration
We introduced an AI Explainability Layer: each compatibility score was accompanied by:
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Behavioral trait alignment breakdown
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Lifestyle compatibility factors
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Risk indicators
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Plain-language reasoning summary
This significantly shifted user reactions from:
“I don’t know where is this results came” to “That makes sense”
JURY FEEDBACK FROM UX AWARDS
“Pawfect addresses pet abandonment by improving adoption matching and reducing returns.”
→ Problem framing and social impact
“Offering strong user value with emotionally resonant and well-crafted experience.”
→ Experience design and emotional UX
“AI logic shows promising potential.”
→ Forward-looking product vision
“Could evolve into a high-impact product with further refinement.”
→ Scalability and system thinking
UX Awards New Talent — Official Recognition
Project Walkthrough — Our Design Process
BACKGROUND
Pet adoption is an emotionally driven yet high-stakes decision-making process
While many online platforms connect adopters with animals, they often lack intelligent tools to guide suitable matches. Adopters are left to rely on fragmented information, while shelters face inefficiencies in managing applications and assessing adopter suitability.
Need to create a intelligent experience to build trust and leads to long-term ownership
With growing interest in responsible, long-term pet ownership, there is a clear need for solutions that support smarter, more confident adoption decisions — for both people and animals.
USER-CENTRIC RESEARCH
DEFINING USERS
Adopter
Inefficiency Pets Exploration
Adopters seek rich, detailed information to make informed decisions, but most platforms provide only simple descriptions and low-quality visuals, making it hard to truly understand each pet.
Shelter
Pet-Adopter Mismatch
Current platforms make it difficult for users to anticipate a pet’s behaviour and personality based on limited profiles and surface-level first impressions. This often leads to misaligned expectations and unsuccessful adoptions.
DESIGN and PROTOTYPE
Adopter
🐾 Create Account
By completing a guided, step-by-step sign-up with lifestyle details, adopters receive smarter recommendations while staying confident and engaged in the process.
🐾 AI-Powered Pet Profiles
AI analyses both pet and adopter data to explain why the match works—helping adopters look beyond appearance, build emotional connection, and make confident, well-informed decisions.
Shelter
