About appXL
appXL builds an autonomous App Store agent: software that does app store optimization work rather than presenting data about it. We started it because every ASO tool we used produced the same outcome — a dashboard full of correct observations that nobody had time to act on. Our position is that the bottleneck in app growth is execution, not information, so the agent researches keywords, drafts metadata, tracks the result and proposes the next change on a standing cadence, with a human approving anything that ships.
Key takeaways
- We build an agent that does the work, not a dashboard that reports on it.
- Every recommendation is traceable to observed store data, and we publish our method.
- A human approves anything that reaches a live listing — the agent proposes, you ship.
- Our research is published openly, including where our own product is the wrong choice.
Why we built an agent instead of a dashboard
App store optimization has a strange economics problem. The analysis is cheap and increasingly commoditised; the execution — writing forty character-perfect metadata variants, localizing them into nine markets, watching what happened and then doing it again — is expensive and unglamorous. So teams buy the analysis and skip the execution, and their listings stay exactly as they were.
An agent inverts that. It treats the analysis as an input to work it performs itself, on a schedule, without needing a person to remember. The person's job becomes judgement: approving, rejecting and steering, rather than transcribing a dashboard into a spreadsheet.
How our data and research work
- Ranking positions are sampled from store searches across devices and storefronts, then reconciled — nobody has a published ranking API, and any tool claiming otherwise is inferring too.
- Keyword volume and difficulty are modelled estimates, and we label them as estimates rather than presenting them as measurements.
- Metadata history is recorded directly from the stores, so a ranking movement can be attributed to the change that preceded it.
- Anything we publish as a benchmark states its sample and its date, so you can judge how much weight it deserves.
Our research team writes the guides in the learning hub from the same data the agent works from. When our data is thin on a question, we say so in the page rather than filling the gap with a confident number.
What we will and will not do
| Commitment | What it means in practice |
|---|---|
| Human approval | No metadata reaches a live store listing without someone approving it. |
| No grey-hat tactics | We will not touch incentivised installs, purchased reviews or keyword stuffing. |
| Honest comparisons | Our competitor pages state plainly where the other tool is the better buy. |
| Your data is yours | We do not train shared models on your private listing performance data. |
| Exportable | Everything the agent produces can be exported over the API. |
Working with us
appXL is available to app teams of every size, from a solo developer with one listing to publishers running hundreds. Access is currently onboarded in batches so each new app gets a properly reviewed first audit.
If you want to see the output before talking to anyone, run the free ASO score against your live listing — it uses the same rubric as the paid audit.
Frequently asked questions
Does appXL publish changes to my app store listing automatically?
No. The agent drafts and proposes changes; a person on your team approves before anything is pushed to App Store Connect or Play Console.
Where does appXL's ranking data come from?
From sampled store searches across devices and storefronts, reconciled into a position estimate. Neither Apple nor Google publishes a ranking API, so every tool infers this the same way.
Is my app's performance data used to train models?
No. Your private listing and performance data is not used to train shared models.
appXL Research
App Store Optimization Research Team
The appXL research team analyzes App Store and Google Play ranking data across the apps our agent manages, and publishes what it finds.