BUILD VS. BUY

What would you have to build to approximate Apptonomy?

Frontier LLMs can do ASO work. We use them. The honest question isn't whether to use AI — it's what infrastructure you'd have to engineer to get a daily expert audit on every app and every market, with measured impact on your own data, every day.

ASO is constant. So is Apptonomy.

We don't think ChatGPT or Claude are bad.

They aren't. Frontier LLMs are excellent general-purpose tools, and a senior ASO consultant with a well-crafted prompt can absolutely produce useful ASO analysis from them. We use those same frontier models inside Apptonomy — they're part of the runtime.

The question isn't “can a clever prompt do ASO?” Yes, sometimes, on one app, once. The question is “what would you have to build to make that work continuously, across every app and market in your portfolio, with structured data, attribution, and methodology that stays current?”

Here's the honest answer.

The build list

Seven systems, all of which have to work together.

Each one is non-trivial. Each one has ongoing operational cost. And none of them are the prompt.

  • 01

    100+ expert-crafted ASO prompts

    What it is
    Each prompt encodes the way a senior ASO consultant thinks about a specific slice of the listing — title, subtitle, screenshot messaging, intent coverage, sentiment drivers, competitive positioning, AI-surface presence, and more.
    Without it
    Generic recommendations any LLM could produce. The methodology IS the prompts; "AI-powered" with weak prompts is the AI-slop trap.
    The hidden cost
    A senior ASO practitioner authoring and maintaining the entire library, with weekly updates as Apple, Google, and the LLM landscape evolve.

  • 02

    Multi-provider AI integrations

    What it is
    Four LLM providers — OpenAI, Anthropic, Google, Perplexity — plus Google AI Overviews via search. A dozen-plus models chosen per task based on capability, speed, and price.
    Without it
    Single-model dependence. Your audit inherits whatever blind spots that one model has today, and you discover those blind spots after they've become recommendations.
    The hidden cost
    Per-provider auth, rate limiting, retries, failover, cost optimization, and per-task model-selection logic. Cross-validation when models disagree.

  • 03

    Data source integrations

    What it is
    Live App Store + Google Play listing scrapers. Keyword volume data (Google Ads, Apple Search Ads, Google Trends). ASC + Play Console OAuth flows. Review APIs. Reddit and open-web probing.
    Without it
    Recommendations are LLM hallucinations on stale training data — no live competitive context, no real volume signal, no attribution.
    The hidden cost
    Per-integration auth, schema management, store-format change handling, OAuth refresh logic, ETL pipelines, and per-data-source freshness commitments.

  • 04

    Proxy infrastructure

    What it is
    Geo-distributed proxies for store scraping, AI-surface probing, and competitor monitoring — without rate limits or geo-restrictions blocking the daily audit at portfolio scale.
    Without it
    Rate-limited within hours. Geo-blocked from the markets you most need to audit. Anti-bot measures cut off entire data sources.
    The hidden cost
    Proxy provider contracts, rotation logic, failure detection, country coverage management, monitoring of the proxy fleet itself.

  • 05

    Multi-layer caches

    What it is
    Caching for expensive AI responses, listing data (fresh-but-not-real-time is fine), and computed audit fragments. Different cache TTLs per signal type.
    Without it
    Per-audit cost goes 10–50× higher. Audit runs take hours instead of minutes. Re-audits become unaffordable on any cadence shorter than weekly.
    The hidden cost
    Cache invalidation logic (a hard problem in any system, harder when the underlying data changes daily). Storage costs. Freshness vs. cost trade-offs per signal type.

  • 06

    Monitoring and change detection

    What it is
    Daily detection of listing changes, ranking shifts, competitor edits, sentiment movement, AI-surface behavior changes. Stateful comparison across runs.
    Without it
    You find out about regressions when the metric tanks. Competitor changes go unnoticed for weeks. The "continuous" claim collapses.
    The hidden cost
    State storage per app × per market × per signal × per day. Comparison logic. Alert-threshold calibration. The whole observability layer.

  • 07

    Orchestration and synthesis

    What it is
    The glue. Hundreds of signals from every layer above converge into a ranked, scored, reasoned recommendation set per app per cycle — with plain-language explanations a senior consultant would write.
    Without it
    You have a pile of dashboards and a queue of LLM outputs to interpret yourself. The "Weekly Revision Plan" doesn't exist as an artifact.
    The hidden cost
    This is the platform. Schema design, ranking model, conflict resolution, reasoning-string generation, the four-pillar scoring rubric, and the per-engine output structure that lets recommendations diff across audits.

And then

You'd have to run all of it. Every day. Forever.

Even with the seven systems above built and running, you'd still have to trigger the whole pipeline every day across every connected app and every market — and you'd have to keep the underlying methodology current as Apple, Google, and the LLM discovery landscape evolve.

That's a senior ASO practitioner reviewing and updating the prompts weekly. A platform team monitoring the infrastructure for breakage. A data team handling store-format changes the moment Apple or Google ships them. A finance line item for proxy contracts, LLM API spend, storage costs, observability.

Without that ongoing discipline, the system is stale within weeks. The build cost is visible; the maintenance cost is what kills most internal versions.

Even if you built all of this, you'd be running a small platform company on the side.

The work isn't the audit. The work is the engineering and the ongoing operational discipline around the audit. Apptonomy is what you get when someone else runs that company so you can focus on the apps.

Methodology is the product. AI is the runtime. Everything in the build list above is what makes the methodology run at scale on every app and every market, every day.

See the underlying methodology on the Methodology page, or read the architecture explainer on ASO Workflow.

Or — you could just paste your URL.

The first audit runs free, in minutes. All seven systems above already running.

Get a free quick audit for your app.