Mobonomy
App and game market intelligence, built on readings not estimates
The problem
Both stores publish today's chart and nothing else. Yesterday's position is gone the moment it changes, so a studio deciding what to build next is working from a snapshot, and the tools that fill the gap — Sensor Tower, AppMagic, data.ai — start at hundreds of dollars a month and model most of what they show.
For a Malaysian or Southeast Asian studio the problem is sharper still: the big tools cover the region thinly, and the numbers they do show are estimates nobody can check.
What we observed
Before we wrote any code.
The whole product rests on one decision: collect, don't model. Every chart in every market is read on a schedule and every reading is kept, so a movement appears the day it starts, not the week it peaks. Where nothing has been read yet the page says so, rather than filling the gap with a guess.
That decision shaped everything downstream. A rank change is a fact with a timestamp, so movers, launches and breakout signals are queries over stored readings, not predictions. The APK analyser follows the same rule — it reads the build itself and reports the SDKs and hosts actually inside it.
What we built
Mobonomy tracks Google Play and App Store charts across 35 markets and keeps every reading, so movers, new launches and breakout signals come from observed data rather than estimates. Built in Laravel, Inertia and Vue on Postgres, with an APK analyser that reports what a build is made of and what it talks to.
Before vs after
The same job, the old way and the new way.
Before
After
Results
Counted from the production database on 17 September 2026. Collection has run continuously since 15 August 2026; the observation count is a row count, not an estimate.
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