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.

Sure — and here's exactly what it would take.

We wrote the whole build down, step by step — hire the practitioner, wire up both stores, build the engines, keep it running every day. Nothing hidden. Read the manual →

With a sharp prompt and your listing pasted in, Claude, GPT and Gemini will all give you a thoughtful analysis — they're our runtime too.

Feeding it is the real work. Store data has to come in fresh, every day. Benchmarks have to stay calibrated as the market moves. And it has to remember what last week's run found — a chat window doesn't.

One app, one market, a listing you touch twice a year? A chat window is genuinely enough. This page is about what changes once it's more than that.

Here's what's actually running under one 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.

  • 10

    analysis engines in parallel

    a full audit fans out across all of them at once, each with its own methodology.

  • 244

    countries mapped

    with 87 languages and every App Store storefront code.

  • 80

    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
    Hand-authored prompts in every engine, 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.
    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

    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
    A different kind of auth for each source — 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
    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.

  • 06

    The always-on layer

    What it is
    80 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.
    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 run every engine's expert prompts against live store data, 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. If your version of do-it-yourself is a build team rather than a chat window, the build-it-yourself manual hands over the whole blueprint.

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.