Decision 01
Lower the cost of starting a club
- Problem
- Supply drives demand.
- Evidence
- Common community pattern.
- Decision
- A short create flow.
- Trade-off
- Fewer options up front.
- Validation
- Count clubs created.
Hobby Club
Hobby Club, an employee-engagement product in the suite, designed under a shared UX strategy across product lines.
- Industry
- Employee engagement
- Timeline
- Dec 2022 to Present
- Technologies
- Figma, User research, Prototyping, AI-assisted analysis, Perplexity, Google Stitch, Claude Design, Figma AI
Project Overview
Design direction for Hobby Club as part of an enterprise product portfolio.
Research, prototyping, usability testing, and design QA through release.
My role
- UX strategy
- Team leadership
- Design QA
Key Features
- Engagement experience for employees
- Sprint-aligned delivery
Challenges & Solutions
Challenge: An engagement product alongside HRMS, wellness, and rewards
Solution: Shared design direction so each product stayed distinct and coherent.
Takeaways
One of multiple product lines led by the UX team.
At a glance
My role
UX strategy
Timeline
Dec 2022 to Present
Employee engagement
Key decision
A short create flow.
Evidence on this page
1 built, 0 provisional, 0 illustrative, 0 planned
Each outcome below is labeled.
// The problem
What was getting in people's way
How to read this page: the outcomes marked Built are results I can stand behind. Personas, decisions and the problem framing are my design reasoning for this product and are not interview findings. Measured numbers are added only where I hold them.
Hobby Club gives employees a way to connect over shared interests. It sits beside HRMS, Wellness and Rewards, and needed to stay distinct while remaining coherent.
Connection across teams
Colleagues in different teams rarely meet over common interests.
Starting a club is effort
If creating a group is hard, few groups exist.
Stay distinct but coherent
The product needed its own personality inside a shared suite.
// Strategy
From business problem to measurable outcome
KPIs are what the product is built to move. They are targets until a measured result is shown.
An easy way to find, join and run interest groups at work.
| Business problem | Product capability | KPI | Intended outcome |
|---|---|---|---|
| Few groups exist | Simple group creation | Groups created | More communities |
| Hard to discover | Browse and suggestions | Join rate | More participation |
| Suite coherence | Shared patterns | Reuse | Familiar feel |
Interest groups on personal chat appstoClubs inside the company platform
Hard to find groupstoBrowse and join
Organisers do it all by handtoTools for events and members
// Users
Who the product is for
Provisional personas, derived from the workflow and the roles involved. They are not interview findings.
Employee
Find people with the same interest
- Discovery
- Easy join
- Events
Club organiser
Run a group with little effort
- Create a club
- Post events
- Member list
HR or culture lead
Build engagement
- Participation view
- Programmes
// Capabilities
What the app can do
A capability map of what the product is designed to do. It describes design scope, not a feature audit.
Browse clubs
Find groups by interest.
Create a club
A short flow to start a group.
Join and follow
One tap to join.
Events
Post and RSVP to meetups.
Member list
See who is in the club.
Discussion
Share updates and photos inside the club.
Suggestions
Clubs recommended by interest.
Engagement view
Participation overview for HR or culture teams.
// Scenarios
Finding people
A new joiner browses clubs, joins a photography group and sees the next meetup.
Starting something
An employee creates a book club in two minutes and invites colleagues.
Culture lead checks in
HR sees which clubs are active and supports the quiet ones.
// Process
How I ran the design lifecycle
Stage 01 of 5
Discover
Research on how colleagues form interest groups today.
// Design decisions
Problem, evidence, decision, trade-off, validation
Each decision is written the same way so the reasoning can be checked.
Decision 02
Discovery before search
- Problem
- People do not know what to look for.
- Evidence
- Design rationale.
- Decision
- Browse by interest, with suggestions.
- Trade-off
- More curation work.
- Validation
- Track join rate.
Decision 03
Own personality, shared system
- Problem
- Engagement products can be playful.
- Evidence
- Suite strategy.
- Decision
- Shared components, distinct content.
- Trade-off
- Careful limits on variation.
- Validation
- Design QA.
// Evidence from the company site
What Advantage Club says publicly
Source: advantageclub.in, checked 8 Oct 2026. Company claims, paraphrased. Not my measured results.
What Advantage Club says publicly about communities. These are the company's claims, not results I measured.
The Communities page groups Hobby Clubs, Funzone and Classifieds as ways for employees to connect beyond work.
Source ↗
// AI in the process
How AI shapes the design process
This describes the method my team uses. It is not a measured result.
AI at Advantage Club is part of how my team decides what to design next. We use it to read the data, turn it into a usability and engagement report, and write clear design instructions. Designers then use generative tools to produce reference pages and refine them by hand in Figma.
For Hobby Club the report looks at which clubs and events get joined, how discovery works in practice, and who stays active after the first event.
Step 1 · AI analysis
Analyze the data
Usage and engagement exports are analyzed to find drop-offs, unused features and unusual patterns.
Step 2 · Perplexity
Check and research
Data sanity checks and secondary research. Numbers and claims are verified before they go into a report.
Step 3 · AI-assisted
Write the report
A usability and engagement report that names the problem screens, the affected users and the likely cause.
Step 4 · Design brief
Turn it into design instructions
Each finding becomes a specific instruction for the new or revised page: what to change, for whom, and what to measure.
Step 5 · Google Stitch, Claude Design, Figma AI
Generate references
The instructions are fed to generative design tools to produce several reference layouts quickly.
Step 6 · Figma
Design and review
Designers choose, rebuild and refine in Figma with the design system, then run usability review before release.
Tools
- Perplexity · Data sanity checks and research
- Google Stitch · Quick UI reference screens from written instructions
- Claude Design · Reference layouts and design exploration
- Figma AI · Variations and cleanup inside the design files
- Figma · Final design, design system and handoff
Guardrails
- AI output is a reference. A designer makes every final decision.
- Data is checked before it drives a design instruction.
- Generated screens are rebuilt with the design system, never shipped as they come.
- Results are stated only when they are measured.
// Outcomes
What exists, and how sure I am
Built
Product in the suite
One of multiple product lines led by the UX team.
// Leadership
My contribution, what is next, what I learned
// What I did
- UX strategy
- Team leadership
- Design QA
// What is next
- Participation tracking per club
- Event reminders
- Cross office clubs
// What I learned
Engagement products depend on supply. Making it simple to create the first club mattered more than any browsing feature.
More Project
All Project
AI Recruitment Command Center
UX and design system for Skywalk Consultant's AI recruitment platform. n8n workflows screen and route candidates, and a command center lets recruiters see, check and override every AI decision.
AIn8nRecruitment
MAYA, my personal AI system
Seven n8n builds on one hub: a portfolio chat assistant, an interview-prep web app, a daily design digest, inbox triage, a morning briefing, two chat agents and a Claude to Gemini image pipeline. Results land in Discord, Telegram and Gmail, and most of them run every day.
AIn8nAutomation
Hiring a design leader for an AI product?
I'm open to design leadership roles. Send me a note and I'll reply within two working days.