We Built a Platform to Run Our AI Search Playbook in One Audit

The Problem We Kept Running Into

Over the past two months, we published 7 parts of The AI Search Playbook. We covered everything: the traffic shift from Google to AI, the difference between SEO, GEO, and AEO, how to get cited by ChatGPT and Perplexity, how to build a brand AI search engines reference, what happened when we optimized our own site, the AI discoverability stack, and why blocking training bots actually increased citations.

Every time we audited a domain (ours or a client's), we ran 6 separate tools. Open the crawl checker, paste the URL, wait. Open the robots analyzer, paste again, wait. Open the citation tracker, enter the brand name, wait. Repeat for llms.txt, AI readiness, Reddit monitoring.

Then we'd stare at 6 separate result pages and try to connect the dots ourselves. Is the low citation rate because bots are blocked? Or because there's no structured data? Or because the llms.txt is missing? You can't tell from one tool alone.

We needed something that runs all 13 tools at once and tells us what the combination of results means. So we built it.

What Radar Does

Radar is a unified AI visibility auditing platform. You enter a domain and run the check. The free first check runs 6 technical tools; a paid audit runs all 13 in staged batches. Results stream in as each one completes. (The full walkthrough of running an audit is here.)

The 13 tools:

1. AI Crawl Checker: Tests 17 crawler user-agents (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Googlebot, and more) for actual HTTP access, robots.txt rules, structured data, and llms.txt detection.

2. Robots.txt Analyzer: Deep-parses your robots.txt against 16 known bots, validates syntax, scores AI bot coverage, and suggests specific rule changes.

3. llms.txt Validator: Checks for an llms.txt file and validates its structure, sections, entity definitions, links, and use policy.

4. AI Readiness Score: 5-category audit covering bot discoverability, structured data, LLM communication, content accessibility, and cross-signal consistency.

5. AI Citation Tracker: Queries ChatGPT, Claude, Gemini, and Perplexity with 4 template queries to measure brand mention rate, URL citation rate, and sentiment.

6. Reddit Brand Monitor: Discovers Reddit mentions via search, analyzes sentiment, and detects AI-seeded (artificially promoted) content with heuristic + GPT analysis.

7. AEO Page Auditor: Scores a page's answer-engine readiness, from front-loaded answers to extractable structure.

8. Answer Engine Citation Tester: Asks the engines a query you specify and reports which URLs each one cited.

9. Source Influence Map: Identifies which domains most influence the answers in your category.

10. Prompt Share of Voice: Measures how often your brand is named across a fixed category prompt set.

11. Schema Completeness Audit: Checks Organization and Article structured data for missing critical fields.

12. Hallucination Check: Flags factual errors the engines state about your brand, with severity scoring.

13. Brand Disambiguation: Detects when engines link your brand name to the wrong entity.

Each tool produces its own score (0-100) and grade (A through F). Radar averages them into a unified AI Visibility Score.

But the scores are not the point.

Cross-Tool Insights: What Individual Tools Cannot See

The real value of running all 13 tools together is the cross-tool analysis. Radar compares results across dimensions and surfaces patterns that no individual tool can detect.

Here are the actual insights Radar generates:

These insights map directly to the strategies we covered in the playbook series. "Blocking bots while citation rate is low" is exactly what Part 7 addressed. "Good crawlability but no llms.txt" connects to the AI discoverability stack in Part 6. "Technically visible but not cited" is the content authority problem from Part 5.

“Running tools in isolation gives you scores. Running them together gives you strategy.”

What we learned building Radar

Prioritized Action Items: What to Fix First

Every audit generates a list of specific action items. Each one tells you:

1. What to fix: The specific issue (e.g., "Unblock 3 AI browsing bots: GPTBot, Claude-Web, PerplexityBot")
2. Why it matters: The business impact (e.g., "AI browsing bots retrieve your content to include in AI-generated answers. Blocking them means those assistants cannot fetch your pages for live answers.")
3. How to fix it: Step-by-step instructions (e.g., "Edit your robots.txt: remove Disallow rules for these user-agents, or add explicit Allow: / rules.")
4. Effort level: Quick Win, Moderate, or Major Effort
5. Expected impact: High, Medium, or Low

Actions are sorted by impact and effort. Quick wins with high impact surface first. You can filter by category (Crawlability, Structured Data, LLM Communication, Citations, Community) and by effort level.

This is the part that turns an audit into a to-do list. Run the scan, hand the action items to your developer or SEO team, track progress by running again in a week.

How Each Playbook Part Connects to Radar

Every strategy we taught in the 7-part series maps to a specific Radar measurement:

Radar is the playbook in action. Instead of reading about what to check and then checking it manually, you enter a domain and get the full picture in one audit.

The AI Advisor: Strategy on Demand

Radar includes a built-in AI strategy advisor. It's a chat interface powered by our knowledge graph (the same one that drives our llms.txt and structured data).

You can ask it questions like:

• "How do I improve my AI visibility score?"
• "Why are AI bots blocked on my site?"
• "What is GEO and how does it apply to my results?"
• "How should I structure my llms.txt?"

The advisor references your specific audit data when answering. If your crawl score is 92 but your citation rate is 30%, it knows that and tailors the advice accordingly.

It uses the consultative approach we built for Vector: enough depth to demonstrate expertise, with a path to deeper engagement when the problem is complex.

Competitor Comparison

Radar lets you run the same audit on a competitor domain. You get a side-by-side comparison table showing scores across all 13 tools, with color-coded diffs (green where you lead, red where they lead).

This is useful for agencies presenting to clients. "Here is your AI visibility score versus your top competitor. Here are the 5 things they do better, and here is the priority order to close the gap."

Who Radar Is Built For

In-house SEO teams: Run audits on your own domain, get action items, hand them to your dev team. Save runs and compare scores over time to track whether your changes are working.

SEO agencies: Audit client domains, export PDF reports, compare against competitors. Each action item includes "why it matters" language you can use in client presentations.

Content strategists: Understand how AI search engines see your content. Are you being cited? Is your structured data working? What is Reddit saying about your brand?

Founders building in public: Check if AI search engines can find your product. Many never check whether AI crawlers can reach their site at all. Radar's free check tells you where you stand.

The Technical Architecture

Radar runs the 13 tools in staged batches. Each tool is an independent API call, and results stream into the dashboard as they complete, so you see scores filling in rather than waiting for everything to finish.

Radar reads the site itself to identify the company behind a domain, and when it is not confident it asks you to confirm before the audit runs. That replaced an earlier version that guessed the company from the domain name alone, which once audited the wrong company.

Cross-tool insights are generated by a rule engine that compares results across all completed tools. The insight generator runs after all tools finish, looking for specific patterns (blocked bots + low citations, good crawl + missing llms.txt, etc.).

Action items are generated from tool-specific results and cross-tool patterns, then sorted by an impact/effort scoring algorithm. High-impact, low-effort items (like adding a sitemap directive to robots.txt) surface before high-impact, high-effort items (like building content authority).

“We did not build Radar because we wanted another product to sell. We built it because we got tired of running 6 tabs and a spreadsheet every time we audited a domain.”

The honest reason this exists

v2.1 Update: DIY Implementation Features

The most common feedback from early users: "The audit is great, but how do I actually fix the issues?" Radar v2.1 bridges the gap from diagnosis to implementation.

AI Prompt Generator: Every action item now has a "Generate AI Prompt" button that produces a context-rich prompt pre-filled with your actual audit data. Copy it into Claude, ChatGPT, or Cursor and the AI tool has everything it needs to implement the fix. Not a generic template: your specific blocked bots, missing schema types, and citation rates are baked into the prompt.

5 Implementation Threads: Actions are grouped into structured fix threads (Crawlability, Structured Data, LLM Communication, Content Authority, Citation Building) with ordered steps. Each step generates a thread-aware prompt that includes context from previous steps.

Generators: Radar now generates starter llms.txt files and copy-pasteable JSON-LD schema markup from your audit data. No need to write these from scratch.

Single-Tool Re-verify: Fix something, then re-run just that tool. No full re-audit needed.

Progress Tracking: Check off completed steps. Progress persists across sessions so you pick up where you left off.

Read the full breakdown in Radar v2: From Technical Audit to AI Intelligence Platform.

What Comes Next

When this post was published in March 2026, we were gathering feedback from early users on which features mattered most. Radar has since launched publicly, with a free check and paid full audits.

The roadmap at launch (March 2026):

• Scheduled auto-scans: Weekly or monthly audits that run automatically and alert you when scores drop
• Historical trend charts: Visualize how each tool score changes over time
• Team access: Multiple people viewing the same audit results
• White-label PDF reports: Branded exports for agency-client presentations
• API access: Programmatic access for teams integrating Radar into their workflows

How Do You Start With Radar?

As of October 2026, start with the free check at www.pixelmojo.io/platform: enter your domain and email, confirm the six-digit code, and Radar scores your site's technical readiness. The full 13-tool audit is paid. The Radar user guide walks through every step.

Free individual tools remain available at www.pixelmojo.io/tools. Radar wraps the full set together with cross-tool insights and prioritized actions; the platform free check scores 6 of 13, and the full audit unlocks from $5.

If you have been following this playbook series, Radar is the tool that puts the playbook into practice. Enter your domain, see your score, get your action items.

Ready to see your AI visibility score?

• Run the free Radar check - One technical readiness audit per domain, no credit card
• Try individual tools - Free single-tool checks
• Contact us - Questions about AI visibility strategy