I make complex health and technology products feel human.
I bring together a decade in healthcare product leadership, large-scale platform delivery and hands-on AI product building. The result is product judgement grounded in users, evidence and what engineering can deliver.
The next generation of health products should help people recognise what changed, understand why it may matter and prepare for a useful next step. That means unifying fragmented records, making uncertainty visible and grounding every explanation in evidence the user can inspect.
Signal over volumeEvidence before inferenceAction without overreach
Selected product work
Evidence of product thinking—not just output.
Independent builds and professional platforms that show how I frame problems, make trade-offs and bring technical and user needs together.
Health technologyiOS · Swift · AI TestFlight beta · In testing
01 / Featured case study
HealthTrends
An independently designed and built iOS app that turns fragmented health records into an understandable longitudinal story—now distributed through Apple TestFlight for user testing.
Current stage
HealthTrends is in active TestFlight testing. Feedback is being used to validate the information architecture, comprehension of health trends and trust in AI-generated explanations before wider release.
The problem
Results, conditions, medicines and guidance live in different places. A person can have plenty of data and still struggle to understand what changed, what influenced it or what to discuss with a clinician.
My product approach
I designed and built an iOS experience that combines HealthKit, imported NHS App records, medication phases, formulary analysis and relevant NICE guidance in one coherent journey.
Key decisions
Show results over time, not as isolated readings.
Map medication phases against clinical trends.
Constrain AI to facts already structured in the app.
Let users inspect the facts behind every answer.
What it demonstrates
Consumer-health judgement, information architecture, mobile UX, technical fluency and a practical model for explainable AI in a high-trust setting.
Labs overviewStructured markers at a glanceLongitudinal viewTreatment context alongside trends
TestFlight beta screens · fictional demonstration data
A defining product decision
How can AI explain a health trend without pretending to be a clinician?
01
Risk
An unconstrained model can sound authoritative even when it lacks the user’s full clinical context.
02
Decision
Ground every response only in structured facts held by the app and relevant source material.
03
Trust mechanism
Expose the exact facts used, label AI interpretation and preserve clear clinical boundaries.
04
Validation
Test comprehension, source confidence, unsafe inference rates and whether users can identify the right next step.
AnswerInterpretation is explicitly labelled
Evidence, not magic
The useful part is the constraint.
The feature is designed around traceability rather than an open-ended chatbot. The user can see which structured facts informed the response, understand the limits of the interpretation and take that context into a clinical conversation.
Proposed success measures
Time to identify a meaningful change
Comprehension of the explanation
Confidence in the stated evidence
Rate of unsafe or unsupported inference
Facts usedEvidence remains inspectable
Product operationsNext.js · APIs
02 / Product workflow
ProdLens
A focused workspace designed to help Product Owners connect delivery detail with the risks and decisions that matter.
Links Jira backlog items to product risks and outcomes
Brings Microsoft To Do actions into delivery context
Makes dependencies and decision points easier to surface
Demonstrates: first-hand user discovery, API integration and workflow design.
Consumer mobileConcept · MVP
03 / Consumer concept
ClosetLoop
A wardrobe and outfit-management concept built around reducing choice friction and helping people make more of what they own.
Frames the job around choosing, not cataloguing
Connects outfits, occasions and wardrobe availability
Uses MVP prioritisation to test the riskiest assumptions
Demonstrates: consumer intuition, journey design and lean validation.
Professional experience
Comfortable where complex technology meets real-world impact.
2023—Now
Jaguar Land Rover
Senior Product Owner
Leading connected-vehicle platform capabilities spanning large-file transfer, vehicle enablement and observability. I shape roadmaps, manage dependencies and align engineering delivery with product and operational outcomes.
Shaped clinical and operational software in a complex, regulated environment, translating the needs of clinicians and NHS stakeholders into deliverable product outcomes.
Health systemsClinical usersDiscoveryEnd-to-end delivery
Stakeholder range
I communicate at the right altitude.
From engineers working through implementation detail to consultant clinicians, CCOs, CTOs and senior leadership teams making strategic decisions.
How I work
Opinionated about outcomes. Curious about evidence.
01
Start with the real problem
I look beyond the feature request to understand the user need, operating constraint and outcome worth changing.
02
Make complexity navigable
I turn technical and organisational uncertainty into clear choices, prioritised work and visible dependencies.
03
Learn through delivery
I define the smallest useful release, measure behaviour and use feedback to improve the roadmap—not merely validate it.
Kelly Pearson · Product leader
Building useful products in health, AI and connected technology.
Based in the Wirral, working with teams across the UK.
