Cases

How the question changed.

How did I find a question we could act on? Each case starts with the reframing, then explains the approach, my contribution, and the result. Start with three representative cases; personal AI systems and prototypes follow below.

  • DELIVERED
  • ONGOING PRACTICE
  • PROTOTYPE

Start here

Three cases that show how I think.

  1. EVIDENCE ARCHITECTURE
    A route back to the evidence254GB of research records organized by tasks and observations into 86 traceable issues in six categories. Lines show relationships, not data distribution.RESEARCH RECORDS254 GBInterviews · TestsORIGINAL CONTEXTISSUE INDEX86issuesSIX CATEGORIESTask contextObservationSource recordEVERY FINDING HAS A SOURCE
    1. Source recordsKeep tasks and context
    2. Tasks & observationsSeparate actions and interpretations
    3. Traceable issuesEach finding has a source

    Records → Tasks & observations → Traceable issuesConceptual model

    DELIVERED2018Fintech · User researchFinance app: turning field records into issues a team can act onFrom organizing 254GB of records to finding grounds for product changes: review behavior by task and form 86 traceable issues.Delivery & progressDelivered 86 decision-ready issues in six categories, linked to tasks, observations, and priorities.
  2. DESIGN WITH FEEDBACK
    Build the check into the processInsight informs design, design and observation iterate, and confirmation follows. Tracks show feedback relationships, not iteration counts, durations, or project progress.DESIGN ⇄ OBSERVESTILL CHANGEABLEInsightConfirmFEEDBACK RETURNS TO DESIGN
    1. InsightDefine the assumption to examine
    2. Design ⇄ ObserveReturn feedback while changes are possible
    3. ConfirmationCheck whether the question was answered

    Insight → Design & observation in a loop → ConfirmationConceptual model

    Ongoing consulting practice2019—presentMedtech · Human factorsMedical devices: testing while there is still time to change the designFrom completing a test to checking design while change is possible: connect critical tasks, possible use errors, and test timing to development.Delivery & progressHelped establish study plans, recording templates, and evaluation processes used in work for 25+ medical-device companies and institutions.
  3. PROTOTYPE / TWO LAYERS
    Experience before implementationThe frontstage preserves the start, task, and end of the user experience. A researcher simulates unfinished responses backstage. Test understanding before choosing implementation.FRONTSTAGE / EXPERIENCEBACKSTAGE / SIMULATIONSTART · TASK · ENDWizard-of-Oz
    1. Frontstage · ExperienceStart, perform the task, end
    2. Backstage · SimulationA person follows response rules
    3. Observe & decideTest understanding before implementation

    Observe understanding · Simulate the responseConceptual model

    DELIVERED2019Intelligent hardware · HRIService robots: trying the interaction before building the featureFrom waiting for a finished feature to testing understanding now: separate interaction stages and simulate key responses before further development.Delivery & progressUsed a four-stage model and Wizard-of-Oz studies, and helped document repeatable research procedures.

More project experience

Finding the right question in each setting.

  1. DELIVERED2017—2024Automotive · HMI/UXAutomotive HMI: deciding what this round of research needs to answerConcept comparisons, pre-production checks, and field observation need different questions. I aligned the research with the development stage.Delivery & progressDeveloped an evaluation framework across four development stages for project delivery and research-team training.
  2. DELIVERED2017Consumer research · Multimodal behaviorDairy packaging: research that led to a usage instructionEEG, gaze, and observation each had a role. The task was to connect what people saw with how they picked up, opened, and used the packaging.Delivery & progressCombined measurements and natural-use observation to propose packaging changes, including instructions for using the fruit cup.
  3. DELIVERED2019Mobile products · Cross-cultural researchFive Middle Eastern markets: what can be shared, what needs local workI combined desk research, interviews, surveys, and competitor analysis to distinguish shared needs, local conditions, and open questions.Delivery & progressOrganized cross-market needs and follow-up questions to support shared-product and localization decisions.
  4. Ongoing team development2019—presentOrganization · Business developmentConsulting: making project experience usable by the next personExperienced staff repeatedly had to start from scratch. I brought study design, responsibilities, and review into the delivery process.Delivery & progressHelped build an ongoing consulting practice, using project experience in proposals, team training, and knowledge retrieval.

AI work and experiments

I build tools for my own problems, too.

These include a system I use daily and prototypes awaiting validation with real research data. Each case states its operating status and what remains to be tested.

  1. Personal daily use · Evolving2026—presentAgentic AI · Knowledge engineeringMy AI work system: connecting repeated lookup and admin tasksI use OpenClaw, Claude Code, and existing retrieval tools for email information, calendars, reference lookup, and filing, with defined write permissions.Delivery & progressUsed in my daily work, with task instructions, separate knowledge collections, and agent roles. No public efficiency benchmark is available.
  2. Prototype · End-to-end validation pending2026 (prototype public, validation paused)Agentic AI · Open sourceSRC PsyPhiClaw: making multi-device analysis inspectable step by stepI split import, time alignment, analysis, and reporting into modules to explore reducing the preparation needed between research tools.Delivery & progressPublished 18 behavioral-analysis modules. Validation is paused; reliability on real research data remains unverified.
  3. Prototype · Performance validation pending2026—presentAgentic AI · Behavior analysisBehaviorLens: keeping review points in automated video codingVideo coding takes time and requires context. I designed a three-pass prototype to explore where researchers should review automated work.Delivery & progressConcept, architecture, and frontend prototype prepared. The 20-minute processing goal and coding accuracy remain unvalidated.