Does the LLM Discoverability Audit Playbook 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 paid tools to run an LLM discoverability audit?

No. The audit uses free tiers of ChatGPT, Claude, Perplexity, and Gemini plus Google Search. The included Google Sheets and Notion template cover all tracking needs. No third-party subscriptions required.

How long does a full LLM discoverability audit take?

Plan for 4-5 hours for a thorough first audit. LLM testing across 4 platforms takes about 1-2 hours, training signal audit takes 1-2 hours, and scoring plus documentation takes another hour. Re-audits are faster since you compare against a baseline.

How quickly do LLM responses change after optimization?

LLM training data is not real-time. Changes to web presence take weeks to months to appear in responses. The audit measures signal infrastructure (a leading indicator), while LLM recommendations are a lagging indicator. Re-audit cadence is 30-60 days.

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New for 2026 Launch Price

LLM Discoverability Audit Playbook

Name: LLM Discoverability Audit Playbook
Brand: Growth by Kev
Price: 99 USD
Availability: InStock
Author: Kevser Imirogullari

Does ChatGPT, Claude, Perplexity, or Gemini recommend your app? The complete methodology to audit, score, and fix how AI assistants discover non-gaming apps. Backed by primary research from 4,265 AI recommendations.

$99 $199

Get the Playbook

PDF

The Playbook (60+ pages)

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

AI-Ready Version

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

Sheet

Query Testing Tracker

Google Sheet with 4 platform tabs (ChatGPT, Claude, Perplexity, Gemini), 18 pre-filled query templates, and recording columns

Sheet

Signal Audit + Scoring Calculator

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

Built from real audits Any non-gaming category No paid tools required Complete in one afternoon

The problem

A 4.8-star app with 17,000+ ratings can be completely absent from every major LLM's recommendations. ChatGPT, Claude, Perplexity, and Gemini don't crawl the App Store. They learn about apps from web content: listicles, review platforms, Reddit threads, and editorial coverage. In our research across 4,265 AI app recommendations, only 16.2% of apps appeared on all four platforms.

There's no standard way to audit this, measure it, or fix it. This playbook is the practitioner-grade methodology that doesn't exist anywhere else.

What's inside

Part 1: The Audit Process

How to build query sets (including price-qualified queries that produce completely different recommendations), test across 4 LLM platforms, audit training signal sources, and map the competitive landscape.

Part 2: The Scoring System

A repeatable 0-10 scoring rubric across Discovery Presence, Training Signal Infrastructure, and Brand Accuracy.

Part 3: The Optimization Playbook

7 actions ordered by impact: comparison pages, review platform distribution, listicle outreach, community seeding, website structure (including llms.txt), stale information checks, and competitive monitoring.

Parts 4-5: Templates + Query Sets

Complete audit report template and pre-built query sets for Productivity, Health/Fitness, Finance, and Education categories.

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." 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.

ASO / Growth Consultants

Adding LLM discoverability as a service offering. Deliver audits your competitors can't.

Indie Developers

Great product but invisible to AI assistants. Find the gaps and fix them with limited resources.

Stop guessing. Start auditing.

The first app teams to build LLM discovery infrastructure will own the recommendations for years.

Get the Playbook for $99

One-time purchase. Instant delivery. Secure checkout via Stripe.

Questions

Does this work for gaming apps?

No. Gaming app discovery is driven by different dynamics (streamers, gameplay content, platform features). This is built for utility, productivity, health, finance, education, and similar non-gaming categories.

Do I need any paid tools?

No. The audit uses free tiers of ChatGPT, Claude, Perplexity, and Gemini plus Google Search. Everything else is included.

How long does the full audit take?

4-5 hours for a thorough first audit. LLM testing across 4 platforms takes about 1-2 hours, training signal audit 1-2 hours, scoring and documentation another hour. Re-audits are faster since you compare against a baseline.

How quickly do LLM responses change?

Weeks to months. LLM training data isn't real-time. The audit measures your signal infrastructure (leading indicator), while LLM recommendations are lagging. Re-audit cadence is 30-60 days.

Can I use this with my own clients?

Yes. Many buyers are ASO consultants and growth agencies. The audit report template in Part 4 is designed for client deliverables.