THE FAIR QUESTION
Can’t I just do this myself with Claude?
If you’re asking, you’re probably good enough with AI to get a genuinely useful first answer. This page is about what happens after that first answer.
ASO is constant. So is Apptonomy.
You're right — you'd get a good first answer.
With a sharp prompt, your listing pasted in, and your screenshots attached, Claude will hand you a thoughtful, well-structured ASO analysis. So will GPT or Gemini. We know, because those same frontier models are our runtime — we picked them deliberately, and we use them on every audit.
But the model was never the hard part. The hard part is everything the model needs around it: live store data, calibrated benchmarks, structural validation, memory of the last run, and a trigger that fires every day whether or not you remember to. None of that lives in a chat window.
There's also a case where you shouldn't bother with us: one app, one market, a listing you rewrite twice a year. Do that in a chat window and you'll be fine. The rest of this page is about what happens when it's more than one app, or more than once.
And one thing before the machinery: what it's for. More organic installs, fewer hours of your own, and the movement proven on your own store data. Everything below exists because that's what it takes to move those numbers on every app, in every market, on a schedule — without a person doing it by hand.
Here's what's actually running under a single Apptonomy audit.
Under one audit
The numbers, counted from the codebase.
These are the counts as of today, and they move as the platform grows. They describe the platform — your plan sets how much of it each audit uses.
74
hand-crafted expert prompts
in the analysis engines alone — nearly 100 across the platform, each one written and maintained by a senior ASO practitioner.
10
analysis engines in parallel
a full audit fans out across all of them at once, each with its own methodology.
100+
model calls per audit
a single run fires more than a hundred LLM requests, routed across four providers.
96
enforced output schemas
every model response is validated against a JSON schema before it becomes data.
9
live store surfaces
five Apple, four Google Play — listings, reviews, rankings, analytics, sales.
~45
integrated third-party APIs
store, ads, analytics, search, scraping, and AI surfaces — each with its own auth and failure modes.
42
caching layers
per-signal TTLs that keep a daily cadence fast and affordable.
244
countries mapped
with 87 languages and every App Store storefront code.
39
scheduled jobs
running nightly and around the clock, whether or not anyone is watching.
The build list
Seven systems you'd be signing up to build.
Each one is real engineering with ongoing operational cost. And none of them are the prompt.
01
The prompt library and its calibration corpus
- What it is
- 74 hand-authored prompts in the engines — nearly 100 across the platform — each encoding how a senior ASO practitioner reads one specific slice of a listing. Behind them sits the calibration corpus: a hand-vetted catalogue of category-leader sentinel apps, per-category benchmark bands, and a keyword volume model fitted against Apple’s own popularity curve to a median error of six points.
- Without it
- Generic recommendations — the same thoughtful-but-uncalibrated advice any strong model produces for anyone who asks it.
- The hidden cost
- A senior practitioner authoring, testing, and re-calibrating the library continuously as Apple, Google, and the model landscape move.
02
Multi-model orchestration — with a judge
- What it is
- Four LLM providers and a dozen-plus models chosen per task. An AI-visibility panel of up to five models — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overview. And a second model, at temperature zero, that grades every engine’s output for plausibility before you ever see it — with 96 JSON schemas enforcing structure on every single response.
- Without it
- A single model’s blind spots go unmarked — no second read, no structural check, no record of which call was a guess.
- The hidden cost
- Per-provider auth, rate limits, retries, failover, circuit breakers — plus the nightly eval suite that keeps the judge itself calibrated.
03
Nine live store data sources
- What it is
- Live App Store + Google Play listing scrapers. Keyword volume data (Google Ads, calibrated against Apple Search Ads). ASC + Play Console API integrations. Review APIs. Reddit and open-web probing.
- Without it
- The model reasons from its training data — stale rankings, invented competitors, no real volume signal, and no way to check any of it.
- The hidden cost
- Nine kinds of auth — signed JWTs, service-account impersonation, and a cookie session for an undocumented Apple endpoint that survives only because a browser renews it automatically — plus schema drift to absorb the moment either store ships a change.
04
Proxy infrastructure
- What it is
- Geo-targeted residential proxies with per-country exit IPs and forced rotation the moment a request is blocked, plus a scraping API for SERP and AI Overview data — all governed by a sharded, self-healing distributed semaphore that holds global concurrency exactly at the plan limit across 25 parallel audit workers.
- Without it
- Rate-limited within hours. Geo-blocked from the exact markets you most need to audit. Entire data sources going dark mid-run.
- The hidden cost
- Provider contracts, rotation logic, circuit breakers, a budget monitor, and per-call cost accounting.
05
The cache economy
- What it is
- 42 distinct cache collections plus edge key-value stores, object storage behind CDN domains, and gateway-level caching of AI responses — with per-signal TTLs, compressed payloads, and negative caching of blocks and rate limits so a failure doesn’t get retried into a ban.
- Without it
- Per-audit cost runs 10–50× higher and takes hours instead of minutes. A daily cadence stops being affordable — so it stops.
- The hidden cost
- Cache invalidation over data that changes every day — one of the two famously hard problems, at 42 layers.
06
The always-on layer
- What it is
- 39 scheduled jobs: nightly audit runs, review ingestion from both stores, store analytics ingestion, competitor change digests, and a dozen self-monitoring jobs that watch the platform itself — feeding 15 task queues sized for portfolio-scale fan-out.
- Without it
- You find out about a competitor’s new screenshots when your conversion dips. “Continuous” quietly becomes “whenever I remember.”
- The hidden cost
- State per app × market × signal × day, and someone permanently on call for the machinery.
07
Synthesis you can act on
- What it is
- Everything above converges through invariant checks, cross-audit consistency assertions, and a scoring model with runtime-asserted weights into one ranked, reasoned plan per app — and every night, an LLM judge re-reads finished audits the way a skeptical consultant would and files what it finds.
- Without it
- A pile of model outputs you referee yourself. The plan — the actual deliverable — never materializes.
- The hidden cost
- This is the product. It’s also the part a chat window can’t hold: state, memory, ranking, and accountability across runs.
Every single day
It ran again while you were reading this page.
Everything above runs on the cadence your plan sets — up to every night — across every connected app and every tracked market. And every competitor listing you track is snapshotted and diffed nightly on every plan, feeding the engines that score your own listing, so a rival's new screenshot set or keyword shift lands in your plan, not in your quarterly retro.
Count what the overnight jobs do — reviews ingested from both stores, rankings checked, listings diffed, analytics pulled, audit quality re-graded — and try staffing it: a data engineer for the pipelines, a platform engineer for the proxies and caches, an ASO analyst to read it all, and someone on call for the 3 a.m. failures. That team exists. It's this system.
Your chat session ends when you close the tab. This doesn't.
Even round one isn't close.
Set the daily loop aside and make it the fair fight you're imagining: your best prompt against one audit, one app, today. Before you've finished pasting your listing into a second chat, the audit has made 100+ model calls through some 80 expert prompts, against live data from nine store surfaces, validated every response against a schema, had a second model grade the results, and ranked what survived into a plan. Then it did the same for your competitors.
That's the first swing of the first round. Tomorrow it happens again — and that part, no prompt can follow.
See the underlying methodology on the Methodology page, or read the architecture explainer on ASO Workflow.
Or — you could just paste your URL.
Free to start, no credit card required. The free audit runs seven of the ten engines on one app in one market — enough to see whether any of this is real on your listing. The full fan-out comes with the paid tiers.
Get a free quick audit for your app.