Hiring systems · Mobile strategy · Product learning
25+ participant Employer Mobile sprint · 40+ participant remote Virtual Recruiter sprint · first UX Indeed University Lead
Product systems · Design leadership · Human-controlled AI · Tokyo
I design the systems around hard decisions.
I turn ambiguous workflows, scattered evidence, and automation risk into product direction teams can act on. My work spans hiring platforms, mobile products, regional systems, and governed AI.
The common thread is simple: help people understand what matters, what the system can do, and where human judgment needs to remain visible.
Professional proof
The systems pattern is older than AI.
Selected scale signals from hiring platforms, regional marketplaces, service capability, and global learning products.
Regional marketplace · Design governance
Product and design guidance across seven regional contexts · shared-library taskforce with five engineers and two UX designers
Service capability · Localization · Training
Training and service-system work at 2,000+ employee scale for the first Hong Kong Apple Store context
Learning products · Web/mobile systems
Product and design guidance for a remote 50+ development team across 13+ regional and country contexts
Selected work
Three cases. Three different decision pressures.
Trust and delegation, product synthesis, and decision-load reduction.
What the employer believes
What the system is allowed to do
01 / BurdenRecruiting effort is too highTargeting, qualification, outreach, prioritization.
02 / GuideProduct can narrow choicesMake the next decision easier without hiding why.
03 / DelegateSystem may act only inside clear boundsExplain action, rationale, and recovery path.
04 / ApproveEmployer remains accountableHuman control stays visible at high-trust moments.
Trust contract mapThemed reconstruction
Virtual Recruiter / D.I.F.M.
A high-ambiguity recruiting concept shaped into a clearer product/service direction by making explainable delegation, trust boundaries, explanation moments, and MVP decisions visible.
Employer effort → trust contract → delegated product system
SignalsMessy evidence from many surfaces
- Vision + whiteboard → app-home ambition
- Feedback synthesis → repeated employer friction
- App reviews + churn → trust and usability gaps
- A11y + localization → inclusive rollout constraints
- Interview / agenda → time-sensitive mobile moments
- Career Scout / AI → assistance boundary questions
SynthesisTurn signals into comparable choices
- Cluster recurring themes
- Separate symptom from cause
- Map confidence and evidence gaps
- Expose tradeoffs across teams
- Connect evidence to employer decisions
DecisionsConvert evidence into product calls
- Prioritize reliability and comprehension fixes
- Sequence app-home status and next actions
- Support interview / agenda moments
- Defer unclear AI bets until boundaries exist
- Route dependencies to design-system work
Supported outputsWhat the synthesis helped clarify
- Prioritized: fix confusing mobile moments
- Sequenced: app home → interviews → assistance
- Deferred: broad automation without trust model
- Framed: roadmap discussion around employer decisions
What made it complexDifferent signal typesMobile moment constraintsRegional rollout needsDesign-system dependenciesAI boundary uncertainty
Direction
Mobile employer experience can be discussed as a product-intelligence system: app home explains status, interviews get timely support, inclusive foundations stay explicit, and AI assistance waits for clear employer control.
Signal-to-roadmap synthesis mapThemed reconstruction
Employer Mobile App / EMA
A product intelligence case about synthesizing app feedback, churn, accessibility, rollout, interview workflows, design-system constraints, and AI/Scout opportunities to inform clearer roadmap discussion.
Signals → synthesis → priorities → decision support
Old burden11 pages / 59 clicksReading, typing, context switches, quality worries, and mobile dropoff before enough momentum existed.
Responsibility sortKeep / remove / defer / prefill / explainEach input earns its place by job quality, confidence, timing, or product support.
New modelOne-page direction + checklist supportMake progress feel lighter while keeping the posting complete enough to trust.
Measurement logicDropoffRead burdenType burdenClicksCompletion behavior
Decision-load reduction mapThemed reconstruction
The Flash Funnel
A mobile-first job-posting case about reducing decision load: auditing an 11-page, 59-click legacy funnel, sorting each input by responsibility, and protecting employer momentum without making the posting feel careless.
Decision load → responsibility sorting → mobile momentum → behavior signals
Operating pattern
Make complexity clear enough to act on.
The project changes. The working pattern repeats.
Frame the decision
Name the real tension before the artifact: trust, burden, direction, standards, or control.
Make evidence comparable
Turn research, metrics, feedback, and constraints into choices a team can evaluate together.
Design the environment
Use product models, facilitation, roles, standards, and governance so the room can move.
Leave capability behind
Leave guides, systems, approval paths, and habits that keep working without one person in every room.
Labs / systems in use
The same questions now move into AI.
AI is the newest chapter—not the foundation. Habitat and KIREI show how I test human authority, evidence, uncertainty, and recovery in working systems.
Built and operating
Habitat / Local-first AI Agent Environment
A local-first AI environment where agents can help, but only inside visible boundaries: memory, tools, approvals, logs, reflections, and recovery paths.
Human authority · Permission gates · Observable work · Recovery paths
Working prototype
KIREI
A photo-to-evidence prototype that uses OCR and barcodes to resolve product identity, locate ingredient sources, show uncertainty, and provide personal-fit context without pretending to offer a medical verdict.
Capture → identity → source evidence → ingredient context → human decision
Leadership & capability
A lot of leadership is noticing when the room is stuck.
That has meant facilitating large cross-functional sprints, building UX capability, guiding regional systems, mentoring, localizing service standards, and designing AI environments where human authority remains visible.