Role
Founding Product Designer
Owned product direction, core experience, visual identity, and launch design.
Designing the app that turns networking into simply asking for help.
My contribution
I helped define the product, lead its request-first pivot, and build the brand and design system from concept through launch.
Timeline
Aug 2025–Present
From early concept and pivot through beta and App Store launch.
Team
Early-stage startup team
Partnered across product, engineering, growth, and community.
Skills
- 0→1 product strategy
- AI interaction flows
- Brand & design systems
- Growth & launch design
Define the product, then make people believe in it.
Paira began with a broad ambition to make networking feel more human. My job was to turn that ambition into a product model, an understandable experience, and a brand people could trust.
People do not need more connections. They need the right person for a real need.
Performative profilesCold outreachUnclear intentWeak follow-through
Product framing, request and matching flows, the visual identity, the design system, and launch-facing creative.
A request-led product shipped after growing to 100+ waitlist users and 50+ beta testers.
We changed the unit of the product from a person to a request.
Profile discovery still made users perform an identity before they could ask for help. A request gave each interaction a reason, a moment, and a clear way to respond.
Profile-led discovery
Browse people first, then invent a reason to connect.
Request-led matching
State the need first, then meet someone relevant.
The pivot made intent visible. It lowered the social cost of asking and gave AI a more useful signal than a profile alone.
The experience moves from a need to a useful introduction.
The screens below are structured placeholders for the final product assets. They preserve the storytelling and layout without presenting invented UI.
01
Translate a request into a relevant person.
The system captures the need, context, and desired outcome before suggesting an introduction with a clear reason why.
App screens coming
02
Make asking and helping equally visible roles.
A request stays legible from posting to conversation, so both sides know what is needed and what happens next.
- Post
- Browse
- Respond
- Select
- Chat
App screens coming
03
Build reciprocity into the product model.
People can move between asking and helping. Contribution becomes visible without turning connection into a popularity contest.
App screens coming
I designed Paira as the opposite of a corporate networking room.
The system uses high-contrast color, soft forms, and direct language to make an unfamiliar AI product feel energetic but approachable.
The two-part mark holds both sides of the exchange: asking and helping.
Electric clarity, softened by playful form.
Color carries action and recognition while neutral surfaces keep dense social content readable.
The identity had to work beyond the app.
I extended Paira across launch campaigns, beta education, community events, and the public website while keeping the voice recognizably ours.
A useful introduction only matters if people reach it and return.
I connected the core product to onboarding, referral, and premium pathways, then refined those flows with startup goals and user feedback.
Onboarding
Set expectations and collect just enough context to create a useful first match.
Screens coming
Referral
Turn a successful interaction into a credible invitation, not a generic growth prompt.
Screens coming
Premium
Frame paid value around better access and control without weakening trust.
Screens coming
100+waitlist users
50+beta testers
36schools represented
30companies represented
Paira moved from a broad networking idea to a request-led product in the App Store.
The live system has supported 500+ matches per day across a community spanning 36 schools and 30 companies.
At 0→1 stage, strategy and craft cannot be separated. The pivot only became credible once the product model, interface, language, and brand all told the same story.
I would compare the quality of introductions across request types and use those patterns to tune how much AI explanation each match needs.
See Paira in context.
The official site carries the current public positioning. Load it here only when needed, or open it in a separate tab.