RoutineArc — on-device AI planner for Android - Sell Project

RoutineArc is a working Android productivity app built around one idea: the AI should run on your phone, not on someone else's server. It is not a finished product. It is a substantial, tested codebase that runs on real hardware and still has a known list of work to do before it could ship — that list is at the bottom of this listing, not hidden. You capture the way you think — type "dentist tomorrow at 3pm", say it out loud, or photograph an appointment letter — and it becomes a properly dated task or event with priority, duration, location and repeat already filled in. It then plans your day around the calendar events you already have. THE ENGINEERING THAT MAKES IT WORTH BUYING Three interchangeable AI backends sit behind a single interface, and the user picks which one runs. All three are scored against the same 27-case, 91-field capture fixture: • Gemini Nano — uses Android's built-in AICore, nothing to download. 91/91 at best; 88–89/91 under sustained use, because AICore rate-limits repeated calls and the run retries. • Gemma 4 E2B — one-time 3.68 GB download, runs on the Tensor G5 NPU. 90/91, verified on device. Recorded at 91/91 earlier; re-measured across four runs on 15 Aug 2026 it scored 90/91 every time, always losing the same field. • Rules engine — needs nothing at all. 91/91, verified in the test suite. Deterministic, so it is reproducible in CI rather than needing a phone; individual captures were also confirmed on device. That third one is the commercially important one. The app still works on phones that can run neither model, with no cloud fallback — so there is no inference cost per user, ever, and no server to keep alive. Because there is no backend, there is no hosting bill and no scaling work. Everything lives in SQLite on the device. The app makes no network calls of its own except an optional address lookup that ships switched off; choosing Gemma 4 downloads the model once, and tapping "Navigate" hands an address to Google Maps. WHAT'S IN IT Capture by typing, voice, or photo with on-device OCR · AI day planning around real events · Eisenhower matrix with drag-to-reprioritise · focus sessions (Pomodoro, timer, stopwatch) · habits with streaks · weekly review, evening triage and a "Keep It Honest" screen · saved places with "leave by" travel times · work schedules and shifts · recurring tasks · home-screen widgets · Wear OS companion · PDF export · backup, restore and CSV import · a built-in user guide that exports itself as a PDF. 28 screens. MEASURED, NOT ASSERTED Every figure below came off a real device or a real build, and the method is recorded in the engineering audit inside the repo: • Capture accuracy — the three scores above, against a shared fixture. • Cold start 251–287 ms on a Pixel 10 Pro XL, force-stopped between runs. • Scroll jank 0.00% on the calendar at 30 tasks and 8 events; no missed vsync on any screen measured. • Download size about 61 MB per device on arm64, built as an App Bundle. • 2,141 tests across 167 suites, running in about 15 seconds from a clean clone. Where an earlier claim turned out to be wrong, it was corrected rather than quietly dropped — the audit records the withdrawn ones too. BUILT TO BE HANDED OVER • 2,141 tests across 167 suites — including the data layer tested against a real SQLite engine, and a smoke test that mounts every one of the 28 screens. • A custom Kotlin native module exposing Gemma 4 on the Tensor NPU through LiteRT-LM. This was the hard part. • A measurement harness that scores capture accuracy against a fixture, so any future change to the AI can be proven not to have regressed it. • A guided code tour written for whoever owns it next: where to start, why the unusual parts are the way they are, and the traps that would otherwise cost a day each. • An engineering audit recording what was measured, what was fixed, and what was deliberately left alone. TECH STACK Expo SDK 54 · React Native 0.81 · TypeScript 5.9 · expo-router · SQLite (expo-sqlite) · ML Kit GenAI (Gemini Nano) · ML Kit text recognition (OCR) · LiteRT-LM + custom Kotlin Expo module (Gemma 4 on NPU) · Jest TRAFFIC AND REVENUE None. Never published to Google Play. No users, no downloads, no revenue. This is a working codebase with known remaining work, not a running business. WHAT'S INCLUDED Full source repository (private GitLab, access granted on agreement) · the custom Gemma NPU native module · 2,141 tests · capture measurement fixtures and harness · in-app user guide and its PDF generator · guided code tour, engineering audit and release documentation · app icons and branding · copyright assignment on completion. KNOWN ISSUES AND LIMITATIONS — DISCLOSED UP FRONT • Release builds are currently signed with the debug keystore, so a real signing key must be configured before Play submission. Documented; roughly an hour. This is the only hard blocker to publishing. • Tested on one phone model. All device verification was done on a Pixel 10 Pro XL, plus one Android em...