sonkei
AI SHIPPED PRODUCT
Match with the right martial arts training partners
Martial artists traveling to new cities have no reliable, trust-layered way to find vetted training partners.
STACK
Claude (product thinking + prompt engineering + prototyping) → Lovable (React/Vite build) → Vercel (deployment) + Microsoft Clarity + Tally
YEAR
2026
DURATION
Concept to shipped in 3 weeks, solo
THE CONSTRAINT
Sonkei couldn't treat safety as a feature layer added on top of a working product. Every design decision - whether users can message immediately, whether win/loss records appear, how many onboarding screens there are - was a trust decision underneath. That reframe changed everything that followed.
3 KEY DECISIONS
Request-gating over open messaging Messaging unlocks only after both users accept a session request. Not a safety feature bolted on; a model of bidirectional consent before physical contact. It also changes behavior: a user who explicitly accepted your request shows up differently than one who just received a cold message.
Removed win/loss records from the data model Not just hidden; gone. A practitioner with a 0-0 record who is an excellent drilling partner shouldn't be filtered out. A practitioner with a 12-3 record who is dangerous to train with shouldn't be surfaced. The stats show Exp, Stance, and Belt. They communicate compatibility without rewarding the wrong behavior.
Vertical scroll over swipe cards Choosing a training partner is a multi-variable decision: discipline, experience, availability, location, belt rank. Swipe cards force a binary judgment before you have context. Vertical scroll lets you compare. Less exciting, more honest about what the decision actually is.
THE AI-SPECIFIC INSIGHT
Working with Claude and Lovable surfaced a specific failure mode: AI-generated UI is locally coherent but globally inconsistent without explicit upfront contracts. The fix wasn't better prompting; it was declaring a canonical state machine before writing a single prompt, and referencing it by name in every subsequent one. The spec document isn't documentation. It's the primary design artifact.
OUTCOME
8/10 moderated testers completed the core loop without instruction
Most-cited insight: availability overlap visualization reduced evaluation cost before any profile tap
3 of 10 testers forwarded the link to someone else without being asked
WHAT I'D DO DIFFERENTLY
I'd validate the use-case split between travelers and local seekers before building the feed; they have different retention curves and the product needed to account for both from the start.
PORTFOLIO
MORE CASE STUDIES
LIVE APP
COMPLEXITY MAPPING
AI SHIPPED PRODUCT
Social Feed
Social Feed
Social Feed
MOBILE PRODUCT DESIGN
INTERACTION STRATEGY
ENGAGEMENT SYSTEMS