The Question Every AI Visibility Tool Avoids
AI visibility tools fall into two camps. Technical audit tools tell you whether AI can find your website. Enterprise monitoring platforms tell you what AI says about your brand. When this post launched in March 2026, few tools did both; by September 2026, 7 of the 10 tools we compared described AI crawler or llms.txt checks.
Technical audit tools check your crawl access, robots.txt rules, and structured data. They answer: can AI find my website? On their own they do not tell you how ChatGPT describes your brand, whether Perplexity cites your competitors instead of you, or if Gemini is telling customers the wrong pricing.
Enterprise monitoring platforms track brand sentiment across AI responses. They answer: what does AI say about my brand? Radar v2 set out to pair that answer with the technical fix.
Radar v2 bridges this gap. It runs 13 tools in one audit that answer both questions and generates a prioritized list of what to fix, why it matters, and how to do it.
What Changed: Four New Intelligence Tools
The original Radar ran eight tools focused on technical infrastructure: crawl access, robots.txt rules, llms.txt validation, AI readiness scoring, brand citation tracking, Reddit monitoring, AEO page auditing, and answer engine citation testing.
These tools tell you whether AI search engines can access, understand, and cite your content. They are necessary. But they are not sufficient.
Radar v2 adds four new tools that close the intelligence gap.
Source Influence Map
When AI platforms discuss your category, they draw from specific sources. The Source Influence Map identifies which domains and URLs are shaping those AI narratives.
This is your editorial hit list. If a competitor's blog post is the primary source ChatGPT cites when answering questions about your industry, you now know exactly which content to create (or outperform) to shift the narrative.
The tool queries all four AI providers with competitive prompts, extracts cited URLs (Perplexity provides structured citations natively), and aggregates them by citation frequency and provider coverage. You see which domains are cited by multiple providers (strong consensus signals) versus which appear in only one.
competitor-a.com
cited
industry-blog.io
cited
techreview.com
cited
your-domain.com
cited
niche-guide.org
cited
Your domain ranks #4. Top 3 sources are shaping AI narratives in your category.
Prompt Share of Voice (SOV)
Share of voice in AI answers is a useful competitive signal. When someone asks ChatGPT "what are the best [your category] companies?" and your competitor is named first while you are absent, that is a measurable gap. It measures mentions in answers, not recommendations or market share.
Radar's Prompt SOV tool runs five industry-specific prompts across ChatGPT, Claude, Perplexity, and Gemini. It extracts every brand mentioned in each response, scores them by position (first mention gets 3x weight, top-third gets 2x, rest gets 1x), and calculates your share of voice as a percentage of total weighted mentions.
You get your rank, your SOV percentage, and a competitor leaderboard showing exactly who is above you and by how much.
#3
Your Rank
18%
Your SOV
5
Brands Found
Schema Completeness Audit
Structured data is the language AI models use to understand your content with confidence. The Schema Completeness Audit checks JSON-LD markup across 10 Schema.org types: Organization, Article, FAQ, HowTo, Speakable, BreadcrumbList, Product, LocalBusiness, WebSite, and SoftwareApplication.
Unlike the existing AI Readiness Score (which checks structured data as one of five categories), this tool does a deep, dedicated validation. It fetches your homepage plus up to four linked pages, checks each schema type against required and recommended fields, and reports completeness percentages with specific missing field names.
This is the only tool in the suite with zero LLM calls. Pure HTML parsing, the fastest and cheapest tool to run.
Hallucination Detection
AI models sometimes fabricate facts about brands. Wrong founding year, wrong product names, wrong pricing, wrong headquarters. These inaccuracies reach every user who asks about your business.
The Hallucination Detection tool extracts ground truth from your actual website (products, pricing, founding year, leadership, location), queries all four AI providers about your brand, and compares their claims against verified facts. Discrepancies are flagged by severity (high, medium, low) and category (product, pricing, founding, location, description).
A high-severity hallucination (ChatGPT telling customers your product costs $99/month when it is actually $49/month) is a different problem than a low-severity one (getting your founding year off by one). The severity ratings help you prioritize which corrections matter most.
1
High Severity
1
Medium Severity
1
Low Severity
AI claims: "Pricing starts at $149/month for the basic plan"
Reality: "Pricing starts at $49/month (Starter plan)"
AI claims: "Offers a mobile app for iOS and Android"
Reality: "No mobile app exists. Web platform only."
AI claims: "Founded in 2023"
Reality: "Founded in 2024"
Enhanced Citation Tracker: Sentiment, Narratives, and Locale
The existing Citation Tracker already queried four AI providers to check if your brand gets mentioned. Radar v2 transforms it from a binary "cited or not" check into a full brand perception analysis.
LLM-Based Sentiment Analysis
The old sentiment detection used a regex pattern matching roughly 15 positive words and 12 negative words. It worked, but it was crude.
The new system sends all provider responses through a structured GPT-4o-mini classification that returns four data points per response:
| Data Point | What It Tells You | Example |
|---|---|---|
| Tone | How the AI frames your brand (positive, neutral, negative, or cautionary) | ChatGPT: cautionary, Claude: positive |
| Confidence | How clearly the sentiment comes through (0.0 to 1.0) | 0.85 confidence on "positive" vs 0.4 on "neutral" |
| Emotional Signals | Key phrases driving the classification | "innovative", "market leader", "trusted by enterprises" |
| Narrative Frame | How the AI positions your brand in one phrase | "emerging challenger", "industry standard", "niche specialist" |
The "cautionary" category is new and important. It captures responses where the AI acknowledges your brand but adds caveats: "X is a good option, but consider Y for larger teams" or "X offers competitive pricing, though some users report limited support." This is distinct from negative sentiment and requires a different response strategy.
All responses are batched into a single LLM call (not 16 separate calls), keeping the analysis within the 60-second serverless timeout.
Narrative: Industry standard
Narrative: Established with caveats
Narrative: Top recommendation
Narrative: One of several
Per-Provider Narrative Summaries
Each AI provider frames your brand differently. ChatGPT might describe you as an "innovative startup," while Gemini calls you a "niche player." These narrative differences matter for brand strategy.
Radar v2 generates a 2-3 sentence narrative summary for each provider, capturing how it positions your brand, the dominant tone, and the key themes it emphasizes. This runs in parallel with the existing AI summary, adding zero latency to the audit.
Locale Targeting
AI models respond differently depending on the user's perceived location. A global brand may be described as a "market leader" in the US but unknown in APAC markets.
Radar v2 adds a locale selector with six options: Global (default), United States, United Kingdom, European Union, Asia Pacific, and Latin America. When a locale is selected, a system message prefix is added to AI queries that frames the response from that regional perspective.
This is particularly valuable for agencies managing international clients or brands expanding into new markets.
The Competitive Landscape, Then and Now
When we launched Radar v2 in March 2026, we split the market into free single-purpose audit tools and enterprise monitors priced at tens of thousands of dollars a year, and we marked the monitors as having no technical audit. That picture did not last. Our dated comparison of AI visibility tools shows what changed:
| Market fact | Our March 2026 framing | Our September 2026 comparison |
|---|---|---|
| Lowest paid monitoring plan | Tens of thousands of dollars a year | From $29 a month |
| Technical checks in monitoring tools | None | 7 of 10 tools describe AI crawler, robots.txt or llms.txt checks on their own pages |
As of September 2026, Ahrefs also sells Brand Radar on its own.
What Radar v2 set out to do still holds: monitoring tells you what is happening to your brand in AI answers, and a technical audit tells you what is broken on your site. Radar puts both in one report and ranks what to fix by impact and effort.
Cross-Tool Intelligence: Where the Real Value Lives
Running 13 tools in one audit is useful. But the most useful part is what emerges from reading their results together.
Radar v2 now generates insights from cross-tool patterns that no individual tool can surface:
| Cross-Tool Pattern | What It Means | Action |
|---|---|---|
| High SOV but low citation rate | Models name your brand in category prompts but do not cite it in direct queries | Create brand-specific content that targets direct brand queries |
| Good AEO score but weak schema | Content is well-structured but missing JSON-LD validation signals | Complete Organization and Article schema (no engine documents schema as a citation requirement) |
| Cited but cautionary sentiment | AI mentions you but adds warnings or suggests alternatives | Review the sentiment details and address the concerns on your own pages |
| Hallucinations + no llms.txt | AI states wrong facts about you | Correct the facts on your own pages and in the sources the engines cite |
| High crawlability but zero SOV | AI can access your site but does not include you in category rankings | Build topical authority with comprehensive category content |
| Source influence gap | Competitors are cited as sources but you are not | Create definitive content that competes for source authority |
These patterns are hard to see when tools run in isolation. A hallucination check alone tells you AI has wrong facts. Read with the citation and source checks, it tells you where those facts likely come from, which is where a correction can start.
Wrong pricing on ChatGPT
No llms.txt file found
Cross-Tool Insight
The engines state wrong facts about you. Correct them on your own pages and in the sources the engines cite; no engine documents reading llms.txt.
18% SOV, rank #3
12% citation rate
Cross-Tool Insight
You are named in category prompts but not in brand-specific answers. Check how your own pages answer direct questions about you.
Missing Speakable schema
AEO score 78/100
Cross-Tool Insight
Content is well-structured but its structured data is incomplete. Worth fixing; no engine documents schema as a citation requirement.
Architecture: Built With Thread-Based Engineering
Radar v2 was built using Thread-Based Engineering, our framework for scaling AI-assisted development. The entire v2 upgrade (4 new tools, 5 enhanced features, 30 new files, 3 database migrations) was structured as a B-Thread orchestrating 14 sub-threads across 4 phases.
Phase 1 decomposed the 5,046-line dashboard monolith into shared modules and composable components. Phase 2 built the four Tier 1 intelligence tools in parallel. Phase 3 added Tier 2 features with mixed dependencies. Phase 4 delivered strategic infrastructure (Supabase persistence, webhooks).
The tool registry pattern means new tools can be added without modifying the dashboard page. Each tool registers itself with its endpoint, display properties, body builder, and rate limits. The dashboard reads from the registry and renders dynamically.
v2.1 Update: From Audit to DIY Implementation
Radar v2 told you what was wrong. Radar v2.1 helps you fix it.
The gap we kept hearing: "The audit is great, but I don't know how to actually implement the fixes." Users would get a prioritized action list, then stare at items like "Add JSON-LD structured data" without knowing where to start. So we built the bridge.
AI Prompt Generator
Every action item now has a "Generate AI Prompt" button. Click it and Radar produces a context-rich prompt pre-filled with your actual audit data: your blocked bots by name, your missing schema types, your citation rates per provider, your SOV percentage. Copy it into Claude, ChatGPT, or Cursor and the AI tool has everything it needs to implement the fix.
This is not a generic template. If Radar found that GPTBot and PerplexityBot are blocked on your site, the prompt says exactly that. If your Organization schema is missing logo and contactPoint fields, the prompt lists those specific fields. Your audit data becomes the implementation context.
Implementation Threads
Actions are no longer a flat list. They are grouped into five structured threads following our Thread-Based Engineering methodology:
| Thread | What It Covers | Verify With |
|---|---|---|
| Crawlability | Unblock AI bots, fix robots.txt, enable server-side rendering | Crawl Check + Robots.txt |
| Structured Data | Add missing schema types, complete incomplete fields, align cross-signals | Schema Audit |
| LLM Communication | Create or improve llms.txt, add llms-full.txt | llms.txt Validator |
| Content Authority | Add speakable schema, restructure for AEO, add extractable data, optimize citations | AEO Auditor + Citation Test |
| Citation Building | Fix hallucinations, build community, create definitive content, grow SOV | Citations + SOV + Reddit |
Each thread has ordered steps. Each step generates a thread-aware AI prompt that includes context from previous steps, so the AI tool does not redo or conflict with earlier work. You check off steps as you go, and progress persists across sessions.
Generators: llms.txt and Schema Markup
Two actions that came up constantly: "Create an llms.txt file" and "Add JSON-LD structured data." Users had never written either one. So Radar now generates them.
The llms.txt starter generator pulls from your audit data (meta tags, detected schema, domain structure) and produces a structured file you can deploy immediately, with placeholder comments where manual input is needed.
The schema markup generator reads your Schema Audit results and produces copy-pasteable JSON-LD blocks for every missing type: Organization, Article, BreadcrumbList, FAQPage, Product, WebSite, and HowTo. Pre-filled with your domain and brand name.
Single-Tool Re-verify
Previously, verifying a fix meant re-running the full 13-tool audit. Now every completed tool has a "Re-verify" button. Fix your robots.txt, then re-run just the Crawl Check. Update your schema, re-verify just the Schema Audit. No full re-audit needed.
The Shift: Audit Tool to DIY Platform
Radar v2 was diagnostic: here is what is wrong and why it matters.
Radar v2.1 is operational: here is what is wrong, here is the exact prompt to fix it, here is the order to do it in, here is how to verify it worked, and here is where you left off last time.
What This Means for Your AI Visibility Strategy
If you ran a Radar audit before this update, run it again. The same domain, the same brand name. You will see four new tool cards, enhanced sentiment data on your citation results, cross-tool insights that did not exist before, and now a Threads tab with structured implementation paths.
The actionable difference:
Before Radar v2: You knew if AI could find your site and whether it mentioned your brand. Twelve dimensions of technical and citation data.
After Radar v2: You know if AI can find your site, what it says about you, how it frames your brand compared to competitors, whether it is telling customers the wrong facts, which sources are shaping the narrative, where you rank in category recommendations, and how all of these signals change over time. Plus a prioritized fix list for every issue found.
After Radar v2.1: All of the above, plus AI-ready implementation prompts for every fix, structured threads that guide you from audit to verified improvement, and generators that produce the files you need. Audit, understand, fix.
Radar v2: Questions About the New Intelligence Features
Common questions about this topic, answered.
What is Radar by Pixelmojo?
Radar is a DIY AI visibility platform that runs 13 tools in staged batches on any domain. It combines technical auditing with intelligence monitoring, then generates AI-ready implementation prompts and structured fix threads. Every action item produces a prompt pre-filled with your audit data that you paste into Claude, ChatGPT, or Cursor. Five implementation threads guide you from audit to verified improvement.
How does Radar compare to enterprise AI monitoring platforms?
When this post launched in March 2026, we compared Radar with enterprise monitors priced at tens of thousands a year. That comparison no longer holds: as of September 2026, monitoring tools publish paid plans from $29 a month, and 7 of the 10 tools in our comparison describe AI crawler, robots.txt or llms.txt checks. Radar combines monitoring and a technical audit with prioritized action items, starting with a free check. See our dated comparison of AI visibility tools for current prices.
What new tools were added in Radar v2?
Four new tools: Source Influence Map (identifies which domains shape AI narratives in your category), Prompt SOV Score (measures position-weighted share of brand mentions across ChatGPT, Claude, Perplexity, and Gemini), Schema Completeness Audit (validates JSON-LD structured data across 10 schema types with zero LLM calls), and Hallucination Detection (compares AI claims against verified facts from your website, flagging inaccuracies by severity and category).
What is AI share of voice and why does it matter?
AI share of voice measures how often your brand appears when AI platforms are asked about your category, relative to competitors. If someone asks ChatGPT for the best solution in your space and your competitor is named while you are not, that is a measurable gap. Radar measures SOV across four providers using position-weighted scoring (first mentions get 3x weight). It is a share of brand mentions in model answers, not market share and not a measure of recommendations.
How does hallucination detection work?
Radar fetches your actual website to extract verified facts (company name, products, pricing, founding year, headquarters, leadership). It then queries ChatGPT, Claude, Perplexity, and Gemini with questions about your brand and compares their responses against the ground truth. Factual discrepancies are flagged by severity (high, medium, low) and category (product, pricing, founding, location, description). This helps you identify and prioritize which AI inaccuracies to address on your own pages and in the sources the engines cite.
How does the enhanced sentiment analysis work?
Radar queries four AI providers about your brand, then classifies each response using GPT-4o-mini into four tone categories: positive, neutral, negative, and cautionary. Cautionary is a new category that captures responses where AI acknowledges your brand but adds warnings or suggests alternatives. Each classification includes a confidence score (0 to 1), emotional signal phrases from the text, and a narrative frame describing how the AI positions your brand (like "market leader" or "emerging challenger").
What is locale targeting?
Locale targeting lets you audit how AI describes your brand from different regional perspectives. Radar supports six locales: Global (default), United States, United Kingdom, European Union, Asia Pacific, and Latin America. When selected, AI providers receive a system message that frames their response from that regional context. This reveals regional citation gaps and narrative differences that matter for international brands and agencies managing multi-market clients.
How much does Radar v2 cost?
As of October 2026, the first technical check is free (one audit per domain, no credit card), and full 13-tool audits are paid, as audit credits or a Pro plan; current prices are on www.pixelmojo.io/platform. For other AI visibility tools, see our dated comparison: as of September 2026, paid plans start at $29 a month.
Try Radar v2.1
Run a new audit on your domain. The four intelligence tools appear alongside the originals. After the audit, open the Threads tab to see your structured implementation path with AI-ready prompts for every fix.
- Run a Radar audit: free technical check, then the full 13-tool audit
- Try individual tools: No signup required for standalone tools
- Read the user guide: Step-by-step walkthrough of all 13 tools and the new DIY features
