Our Work
Showcase
Developed for real users — fully instrumented, production-grade software, not prototypes. From Flutter apps live on iOS and Android to an AI-native personal operating system: complex algorithms, engagement mechanics, Cloud Functions pipelines, subscription management, and zero crashes.
D4L — Design For Life
A personal operating system — R&D
Our research platform, and the clearest statement of how we engineer. D4L captures one household's entire record — email, documents, calendars, health data, app data — into a sovereign, privately owned knowledge graph with an attention engine and an AI interface on top. The hard part isn't the AI; it's the trust architecture that makes an AI safe to run over a life — and because the record speaks an open protocol, every time frontier AI gets smarter, the system does too.
- Ask your life anything — "when did the symptoms start?", "find the warranty and every email chasing it" — and get answers with the sources attached
- Two append-only ledgers are the only source of truth; every index, graph and queue is disposable and rebuilds from them exactly
- Always-on cloud node triages email and calendar into an event-sourced attention queue; push notifications are the doorbell, never the state
- A machine-enforced trust contract: the AI proposes, only a human commits — decisions are never minted by a model, advice is evidence-gated
- 60+ architecture decision records, 50+ forced-failure acceptance suites, deterministic replay, zero personal data in the repository
My 12-Step Coach
Recovery structure in your pocket
If your idea depends on users forming a habit, this is the reference. My12SC is a behaviour change platform — the hard part wasn't the content, it was engineering the engagement mechanics that keep people coming back.
- Onboarding wizard with complex state management — turns setup into an experience that predicts long-term retention
- Full habit-loop system: streak mechanics, FCM push nudge timing, dormant-user re-engagement, gamified milestone rewards
- Insights engine with progressive reveal — complex data surfaced without overwhelm
- Subscription management, home screen widgets (iOS WidgetKit + Android Glance), sharing mechanics
- DAU/MAU above peer median — Zero crash rate — Firebase Analytics with full cohort segmentation
Surplus
Financial clarity for households
If your idea involves data, calculations, or intelligence — this is the reference. Surplus is a fintech platform with a custom algorithmic insights pipeline, AI integration, and a learning categorisation engine — its v1.1.0 release rebuilt that intelligence layer from the ground up.
- Custom Buffer engine: forward-looking financial runway in days, calculated across all accounts and committed obligations
- Rebuilt insights engine: recurring payments identified from the bank reference itself — direct debits and standing orders no longer hide behind cryptic strings — surfacing locked income and upward cost drift
- Every signal declares its own confidence — Active when the data is ready, Gathering while it builds — so the UI never shows false precision
- Calibration layer plus a learning reclassification engine: user input always outranks inference, and corrections train a persistent merchant mapping that sharpens every future import
- AI API integration for OCR statement capture, real-time household sync, home screen widgets — Zero crash rate in production
The Smarts Family
Better taste, less money — from your own data
If your idea needs real intelligence — not a generic model bolted onto a database — this is the reference. Three apps, one philosophy: a user's own ratings reveal what actually drives their taste, and everything is ranked by value for money. The point is what sits underneath — we don't reuse one model, we re-architect the intelligence layer around how each domain actually works.
- Coffee Smarts — the taste drivers are printed on the bag (variety, process, origin, altitude); a structural Ridge regression finds which of them move each user's ratings
- Whisky Smarts — the drivers hide below the label, so a custom causal flavour-vector model separates intensity from character: two peated drams a drinker loves and hates aren't a contradiction
- Olive! — label-scanning captures the data most people can't read, then a cultivar/origin/intensity engine surfaces what's behind their best bottles
- Shared across all three: €/★ value rankings, a plain-language palate read, and a reference encyclopedia overlaid with the user's own collection
- Each built on the Coffee Smarts foundation — concept to live on both stores in weeks, fully instrumented, zero crashes