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LLM Discoverability Audit Playbook

Does ChatGPT recommend your app? Find out. The complete methodology to audit, score, and fix how AI assistants discover and recommend non-gaming apps.

The Complete Package

Everything you need to run the audit yourself

PDF

The Playbook (60+ pages)

Full audit methodology, scoring system, optimization actions, and category-specific query templates

MD

AI-Ready Version

Same playbook as a markdown file. Feed it to your AI assistant and run the audit together

Query Testing Tracker

Google Sheet with 3 platform tabs, 18 pre-filled query templates, and recording columns

Signal Audit + Scoring Calculator

Google Sheet that audits all 4 training signal sources and auto-calculates your 0-10 score

Notion Audit Template

Living tracker for findings, competitive landscape, priority actions, and baseline metrics for re-audits

$99 $199 Launch Price

Introductory price for early buyers. One-time purchase, all future updates included.

Get the Playbook

Instant delivery. PDF + Markdown + Google Sheets + Notion template.

Built from real audits on production apps
Works for any non-gaming app category
No paid tools required
Complete in one afternoon

Secure checkout via Lemon Squeezy. VAT handled automatically.

The Problem

Your App Store ratings are invisible to AI

A 4.8-star app with 17,000+ ratings can be completely absent from every major LLM's recommendations. ChatGPT, Perplexity, and Gemini don't crawl the App Store. They learn about apps from web content: listicles, review platforms, Reddit threads, and third-party editorial coverage.

No established methodology exists

LLM discoverability is still the Wild West. There's no standard way to audit it, measure it, or fix it. Research is fragmented across "GEO," "LLMO," and "AIO" terminology. This playbook is the practitioner-grade methodology that doesn't exist anywhere else.

What's Inside

Part 1: The Audit Process

Step-by-step methodology: how to build query sets, test across 3 LLM platforms, audit all 4 training signal sources, and map the competitive landscape. Every detail documented so you can run it yourself.

Part 2: The Scoring System

A repeatable 0-10 scoring rubric across three dimensions: Discovery Presence, Training Signal Infrastructure, and Brand Accuracy. Plus the branded/category diagnostic that tells you the nature of your problem.

Part 3: The Optimization Playbook

6 actions ordered by impact: comparison pages, review platform distribution, listicle outreach, community seeding, website structure for LLM readability, and competitive narrative monitoring.

Parts 4-5: Templates + Query Sets

Complete audit report template matching production client deliverables. Pre-built query sets for Productivity, Health/Fitness, Finance, and Education categories. Ready to customize and run.

The insight that changes everything

When an app scores 6/6 on branded queries but 1/12 on category queries, the diagnosis is clear: "The product is known, the discovery infrastructure isn't built." The product quality isn't the problem. The distribution of signals that feed LLM recommendations is. This playbook shows you exactly which signals are missing and how to build them.

Who This Is For

App Growth Teams

Running a non-gaming app and wondering why LLMs recommend your competitors. You need a structured way to diagnose and fix it.

ASO / Growth Consultants

Adding LLM discoverability as a service offering. Use this methodology with your clients and deliver audits they can't get anywhere else.

Indie Developers

Building a great product but invisible to AI assistants. This playbook shows you where the gaps are and what to fix first with limited resources.

The 4 Training Signal Sources

LLMs don't crawl the App Store. They learn about apps from these four types of web content. If you're missing from them, you won't be recommended.

Listicles & Roundups

"Best [category] apps" articles

Review Platforms

G2, Capterra, Trustpilot

Community Mentions

Reddit, forums, Quora

Third-Party Editorial

Independent reviews & features

Stop guessing. Start auditing.

The first app teams to build LLM discovery infrastructure will own the recommendations for years. This is the methodology to do it.

Get the Playbook for $99

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Questions

Does this work for gaming apps?
No. Gaming app discovery is driven by different dynamics (streamers, gameplay content, platform features). This methodology is built for utility, productivity, health, finance, education, and similar non-gaming categories where LLM recommendations directly influence user acquisition.
Do I need any paid tools to run the audit?
No. The audit uses free tiers of ChatGPT, Perplexity, and Gemini plus Google Search. The Google Sheets and Notion template are included. No third-party subscriptions required.
How long does the full audit take?
Plan for 3-4 hours for a thorough first audit. The LLM testing (Step 2) takes about an hour. The training signal audit (Step 3) takes 1-2 hours. Scoring and documentation take another hour. Re-audits are faster since you're comparing against a baseline.
Will changes show up in LLM responses immediately?
No. LLM training data is not real-time. Changes to your web presence take weeks to months to appear in responses. The audit measures your signal infrastructure (a leading indicator), while LLM recommendations are a lagging indicator. This is why the re-audit cadence is 30-60 days.
Can I use this methodology with my own clients?
Yes. Many buyers are ASO consultants and growth agencies adding LLM discoverability to their service offerings. The audit report template in Part 4 is designed for client deliverables.