fíor · Irish · true, real, pure
A mobile app paired with RYAKKA, a bespoke IoT sensor that reads six drinking-water parameters at home and collapses them into four plain verdicts: Excellent, Good, Medium, Bad. Designed for an Irish context where the official reports are annual PDFs and compliance is reported as averages — but the person at the tap still has no idea what came out today.
In Ireland, 497,000 people are served by at-risk public water supplies (EPA, 2023), and roughly 52% of surface waters fall short of good ecological status. Headline compliance looks reassuring at 99.8%, but the consumer still only gets annual reports in technical language. There is no daily, plain-English verdict on the water coming out of the tap right now — and no way to act on it.
Fior is a mobile companion to RYAKKA, a home sensor reading six parameters (TDS, TOC, COD, UV275, EC, temperature). The app collapses those readings into a 4-state Verdict Ladder — Excellent, Good, Medium, Bad — with context for what each result means and what, if anything, to do next. The language is calm, not alarmist; every claim is sourced, not asserted.
Conceptual MVP — not shipped, but validated. Research drove a persona pivot from a rural-first hook to an urban-renter primary persona for go-to-market. The output: a token-first design system (281 tokens across three layers, 2,356+ bindings), 15 navigable hi-fi screens, and a single Verdict Ladder component that carries the whole app's point of view on health and honesty.
I moved to Ireland in 2022. The first thing my kettle taught me was what limescale looks like — a white crust on the heating element after a week, no matter how often I rinsed it. The tap water was technically safe — the EPA said so. I still didn't really know what was in it.
That gap — between “water is technically safe on the national average” and “I, today, in my house, can confirm it” — became the brief. The consumer category barely exists in Ireland: EPA data sits in institutional reports, not in people's pockets. Fior closes the distance.
Sources: EPA · Drinking Water Quality 2024 · EPA · Water Quality Report 2019–2024
Started broad, on purpose. The prompt was “what are the impacts of untreated water in Ireland?” — not “what app should we build?”. Mapping the blast radius first stops a project from collapsing into a single feature before the problem is understood.
Each category unlocked a different angle for the next round of questions.
First cut of the problem statement: “Rural households in Ireland need a reliable and affordable way to access clean and safe water, because untreated water can cause health, environmental, and economic issues.”
That statement survived until personas were sketched — and then it pivoted.
The sharpest primary persona turned out to be the Concerned Renter (urban, 25–40, Dublin / Cork / Galway). Rural well owners have higher objective risk, but renters have higher engageable risk: they can't modify plumbing, they suspect lead pipes, they already buy filtered jugs, and they live inside the quantified-self product category.
Rural well owners became the secondary persona; new parents the tertiary. The problem statement held — the primary target shifted from who was most at risk to who was most likely to act.
Eight How-Might-We prompts, one per impact vector. The aim was not to pick a winner here — it was to surface angles so the ideation round wouldn't start pre-committed.
The HMWs collapsed into four plausible products. Each was scored against three filters: personal daily value, defensibility, and scope discipline (could a solo designer prototype it end-to-end?).
Personal value every single day — you don't check a map every day, but you do drink water every day. A hardware moat through RYAKKA, the companion IoT sensor that turns six parameters into one verdict. And a scope a solo designer can actually ship end-to-end: six readings, one verdict, one app. Clean boundary.
Before any pixel, a four-screen ASCII sketch. Home → Test → Result → Suggestions. That spine is still visible in the 15-screen hi-fi prototype: Home became Dashboard, Test became Measuring, Result became the verdict screen, Suggestions stayed as a hub.
┌────────────────┐ ┌────────────────┐ ┌────────────────┐ ┌────────────────┐ │ HOME │ │ TEST │ │ RESULT │ │ SUGGESTIONS │ │ │ │ │ │ │ │ │ │ Last test: │ → │ [ Dip RYAKKA] │ → │ GOOD · 76 │ → │ 1. Boil 3 min │ │ 08:42 · Good │ │ measuring… │ │ TDS · TOC │ │ 2. Filter jug │ │ │ │ ◉ ◉ ◉ │ │ COD · UV275 │ │ 3. Log source │ │ [Test again] │ │ │ │ EC · TEMP │ │ │ └────────────────┘ └────────────────┘ └────────────────┘ └────────────────┘// 4-screen sketch · direct ancestor of the 15-screen hi-fi ← Scroll horizontally to see the full flow →
Everything that followed — 281 design tokens, eight colour ramps, the Verdict Ladder component, the Inter → Nunito migration — was thickening the spine. The spine itself never moved.
Fior's design system was built directly in code, not translated from Figma. Three layers of tokens, a typography migration mid-project, and a single four-state component that carries the whole product's point of view on health and honesty.
Every token in the system sits in one of three layers. Primitives hold raw values. Semantic tokens map meaning onto those values (primary, surface, danger). Component tokens bind the semantic layer to a specific UI part. Swap a primitive, the upper layers update automatically.
Mid-project the brand tone broke the typeface. Inter is crisp and neutral — right for SaaS, wrong for a calm Irish utility that wants to feel like a knowledgeable friend. Nunito's rounded terminals land warmer without surrendering legibility.
With a flat system this migration would have been a 2,356-row search-and-replace. With the three-layer architecture, the change was a single primitive swap: --font-family-base pointed from Inter to Nunito, and every semantic and component token propagated automatically.
Ten minutes of work, zero visual regressions. The architecture wasn't theoretical — it paid back the cost of setting it up on the first structural change.
Six sensor parameters (TDS, TOC, COD, UV275, EC, TEMP) are complicated. A verdict is not. The Verdict Ladder is the single component that turns the reading into a headline anyone can act on in under two seconds. Four states. Plain English. No numeric wall.
The dashboard leads with the verdict, not the readings. COD, TDS, UV275 are evidence — they live one tap deeper for the user who wants them. Most users don't. Calm tone over scary tone: the four states never say “unsafe” or “warning” — they say what you should do next (boil, filter, drink).
The eight colour ramps, the Nunito type scale, and the base-4 spacing grid — the surfaces the three-layer architecture ultimately resolves to.
A React prototype that implements the full core flow — onboarding, daily test, verdict ladder, RYAKKA pairing, history. Built from the same tokens documented above, so the design system isn’t a spec on a page — it’s what the prototype actually runs on.
Fifteen screens, end-to-end user journey. Mobile-first React + TypeScript build — the phone view is the canonical frame, with a presentation shell unlocking on desktop. Interactive verdict ladder, RYAKKA pairing sequence, and the calm-voice UX copy already in place.
A single mobile-first document with four modes — Process, Wireframe, Prototype, and System. Walks the full design trail from problem framing to the 9-step wireframe to the shipped prototype and the design tokens that power it. Built for a smartphone-first audience; the wireframe flow that used to live on desktop-only is ported in and readable on any screen.
A living, work-in-progress Figma file — primitives, semantic tokens, component specs, and the Verdict Ladder states in every branch. The trade-offs behind each token decision are worth walking through in person, so the file stays private. Walkthrough available on request during interview.
The FigJam that held the whole discovery phase — impact map, persona pivot, four directions, HMWs, EPA evidence, and the affinity clusters the five Deep Dive principles emerged from. Kept private to protect participant PII — walkthrough available on request during interview.
The EPA says Irish public water is 99.8% compliant — and yet Dublin bins are full of bottled water. That gap is the real brief. People weren’t fleeing unsafe water; they were fleeing the absence of personal confirmation. Good research looks less at what people say is wrong and more at the small, recurring workaround they’ve quietly built around the problem. Fior started there — not in a dashboard, not in a lab, but in a bin.
I built Fior’s design system directly in code — 281 tokens, three layers, 2,356+ bindings, zero hardcoded hex. Figma became documentation instead of source of truth. When the typography had to change mid-project, swapping Inter for Nunito was one primitive edit and the cascade handled the other 847 references. In tokens-first systems, refactors stop being events and become keystrokes.
Health apps usually lead with readings — TDS 128 ppm, pH 7.2, EC 245 µS/cm — and leave interpretation to the user. That’s a failure of voice disguised as transparency. Fior’s Verdict Ladder leads with the state (Good, Medium, Bad) and hides the parameters one tap away. Calm is an information architecture decision, not a tone-of-voice exercise. Alarm earns nothing; plain-language clarity earns trust.
End-to-end solo only because of disciplined AI co-pilot use across the stack: research synthesis (the 5-step thinking sequence), token architecture (281 tokens, 0 hardcoded hex), and the React + TypeScript prototype (15 screens, mobile-first, 28 passing tests). I direct the system thinking — personas, IA, calm-voice tone, the verdict ladder as the central design decision — and the AI executes the heavy lifting. The result is a single-designer product that holds up to engineering scrutiny, in a timeline a single person could realistically own.