Autonomé

• Platform: iOS, Android, Desktop
• Design system management: Supernova.io
• Product type: Consumer App, Enterprise Data Platform
• Design system: Material 3, Custom design system
• Design tools: Figma, Cursor, Antigravity, Pen & Paper
• Approach: AI-driven workflows
• Core Technology: Distributed Ledger (DLT), Smart Contracts

People generate valuable health data every day through smartwatches, health apps, and hospital visits. Data brokers take it through confusing terms and conditions. The data sits in silos with Apple, Google, and Samsung, doing nothing.

Meanwhile, researchers, pharmaceutical companies, and AI-health startups desperately need clean, long-term health data. Traditional methods like web scraping, or data brokers with opaque terms often operate in a grey area, likely breaching consent laws. The data they can acquire is often biased, incomplete, or ethically questionable.

Autonomé sits in the middle. People get paid for their data. Buyers get compliant, continuous datasets. I was brought on as the sole product consultant to design how both sides of this marketplace actually work.

I’m the lead experience design consultant and product owner. Staff designers handle branding and identity. I handle everything else: the consumer app, corporate portal, and data dashboards. Day to day that means service blueprints, workshops, discovery, prototyping, and production-ready designs in Figma or directly in code.

The core tension is designing for two timelines at once. The consumer app needs to work now: earning trust, collecting data, paying users. But every decision also needs to support the future enterprise data marketplace without requiring a rebuild.

What I do

• Own the product backlog — prioritise features, defects, and technical debt against adoption, satisfaction, and commercial outcomes.
• Drive strategy and roadmap — work directly with leadership to set direction, then test assumptions with real user feedback rather than opinion.
• Define requirements with engineering and clinical teams — understand the actual problem before building. I run workshops with engineers, designers, and clinicians so we’re solving the right thing.
• Write stories and acceptance criteria — break complex clinical and business needs into sprint-ready work.
• Support agile delivery — James runs the sprints. I help with backlog refinement and product demos, keeping stakeholders across progress.
• Triage incoming work — evaluate enhancement requests, bugs, and pain points against commercial goals and customer feedback.

What I design

• System maps — actor mapping, causal loops, and user journeys to understand how data and money actually flow through the system. I also map revenue loops and data flows (phone → secure database) to keep design, engineering, and legal on the same page.
• Design system — one system that scales across consumer, corporate, and internal tools, plus pitch decks.
• Both sides of the marketplace — patient-side journeys that collect data simply while meeting legal standards for enterprise sales later. Corporate-side onboarding with strict identity verification. Planning how the experience shifts as the client moves from small academic datasets to large corporate buyers.

How I approach the work — I talk directly to stakeholders to find out what frustrates and drives them. We prototype and test before committing to code. I run sessions with legal and research partners so regulatory constraints become design features early, not late blockers. Goals are specific: a patient with MS can sell their data in under 10 minutes without reading legal jargon. Researcher goals are bit more nuanced, and a WIP.

Frameworks I draw on: SWOT, Porter’s Five Forces, RICE, MoSCoW, Cost of Delay, GIST, JTBD, Double Diamond, User Story Mapping, ATDD, Kano.

• Onboarding: ID verification and clear, plain-language consent so data can be legally used
• Continuous tracking: Automatic smartwatch syncing plus quick weekly questions to build a three-year health history
• Value exchange: A transparent payment system so patients see exactly what they earn
• Enterprise portal (future): A secure dashboard where researchers search anonymised patient cohorts

• Two cultures, one product: Autonomé has a privacy-focused team and a sales-focused team. I used system maps to show how corporate sales fund the privacy work, then designed separate interfaces so neither side compromises. Same database, completely different experiences.

• Three-year data gap: Enterprise buyers need three years of continuous data. If sellers churn, the product dies. I designed passive smartwatch syncing with only two minutes of weekly input, plus a clear payment screen so users see the direct financial benefit of staying.

• Bias in medical data: AI trained only on wealthy, tech-savvy users is dangerous. I turned this into a product rule: the corporate portal limits data access if the patient cohort doesn’t meet diversity standards.

• Hidden legal complexity: AU, SG, and US healthcare laws require intense consent flows that scare users off and inflate acquisition costs. I worked with legal to turn complex contracts into a step-by-step process in plain English.

• IP protection through partners: Channel partners in the US create IP risk. I designed a limited partner portal: they can search and close sales, but never access underlying code or identifiable user data.

• Lower acquisition cost — simplified sign-up and ID verification. Fewer steps, more completions.
• Higher patient retention — clear payment screens and plain-language consent build trust. Trust keeps users contributing data long enough for the enterprise side to work.
• Corporate renewal — the enterprise portal matches how data scientists actually search and filter. If it saves them time, they renew.
• Ethics in the process, not just the product — privacy principles are built into hiring, workflows, and team culture. Trust starts internally.

Next: finalising accessibility standards for the consumer app, and testing early enterprise portal designs with APAC and US-based research partners.