Does AI Choose You? — AI Visibility Audit

Audit how 8 leading AI models perceive your brand. Submit a website URL, get a structured JSON report with scores, risks, and an action plan — all using the AIDA buyer-journey framework (Awareness, Interest, Desire, Action).

Most AI-visibility tools track mentions. This one tells you why you're losing and exactly what to fix. One run asks 40 buyer-intent questions to 8 models in two passes — once without your brand and once with it — so you see the precise lift your own content gives (the inferred → informed delta), then hands you ranked risks, a prioritised action plan, and copy-paste JSON-LD schema. No subscription, no dashboard to babysit — a complete diagnosis in a single machine-readable report.

Keywords: AI visibility audit, generative engine optimization, GEO, AI SEO, brand visibility in ChatGPT, brand visibility in Claude/Gemini/Perplexity, AI answer engine optimization, LLM brand perception, AI search readiness, JSON-LD schema markup generator, AIDA buyer-journey audit.

Trigger phrases — use this actor when you see

• "How does ChatGPT see my brand?"
• "Check my AI visibility / GEO score"
• "Am I showing up in AI search results?"
• "Audit our brand across GPT, Claude, Gemini, Perplexity"
• "What should I fix so AI recommends us?"
• "Generate schema markup for AI visibility"

How we compare

	Does AI Choose You?	Other AI-audit actors	Profound	Peec AI	Otterly.ai
Price	$15 / audit	pay-per-run (varies)	$99–499 / mo	€89–199 / mo	$29–489 / mo
Billing	one-time, per run	per run	subscription	subscription	subscription
AI models covered	8 (GPT, Claude ×2, Gemini, Perplexity, DeepSeek, Kimi, Gemma)	1–3	5+	5+	3+
Inferred vs informed delta	✅ dual-pass	❌	❌	❌	❌
7-dimension AIDA* scoring	✅	partial	❌	❌	❌
Ranked action plan	✅	sometimes	❌	❌	❌
Copy-paste JSON-LD schema	✅	rare	❌	❌	❌
40 buyer-intent questions	✅ full AIDA* journey	varies	prompt-limited	prompt-limited	prompt-limited
Output	structured JSON (13 sections)	JSON / CSV	dashboard	dashboard	dashboard

* AIDA = Awareness, Interest, Desire, Action — the buyer-journey stages every audit scores against.

One $15 audit costs less than a single day of most monitoring subscriptions — and unlike the trackers, it doesn't just count mentions, it tells you why you're losing and exactly what to fix. Competitor figures reflect publicly described features/pricing as of July 2026; capabilities change — verify current specs before quoting.

What you get

A single JSON object with 13 sections covering every angle of your AI visibility:

Section	What it tells you
Findings Summary	Overall 0–10 score, verdict (WEAK/MODERATE/STRONG/EXCELLENT), per-AIDA-stage scores
AI Readiness Gap	How much your website improves AI perception (inferred vs informed scores)
Category Alignment	What category AI thinks you're in, and who it thinks your competitors are
Stage-by-Stage Analysis	7-dimensional scoring per AIDA stage (Brand Recall, Sentiment, Specificity, Evidence, Completeness, Category Clarity, Position/Rank)
Prompted Awareness	Your average rank when AI models are asked to list competitors in your category
By AI Provider	Per-model breakdown — see which AI models know you best
Proof Signals	Which evidence types (case studies, customer logos, awards, etc.) AI models detect
Message Pillars	Brand strengths, weaknesses, and competitive ranking by attribute
Website Health	Pages crawled, success rate, issues found
Key Risks	Prioritized risks ranked by category and AIDA stage
Action Plan	Immediate, short-term, and long-term recommendations
Schema Markup	Copy-paste JSON-LD templates with priority levels
Raw Questions & Responses	All 40 questions with every AI model's verbatim responses

How it works

1. Crawl — scrapes your website content, metadata, and structure
2. Profile — infers your business category, persona, and search behaviour
3. Research — pulls Google PAA questions and related searches for your category
4. Detect competitors — identifies your top 5 competitors (or uses your manual list)
5. Generate questions — creates 40 buyer-intent questions across the AIDA journey
6. Query AI models — asks each question to all 8 models in two passes: brand-agnostic (inferred) and brand-informed
7. Score — 7-dimensional scoring per response using rules-based analysis
8. Report — generates narratives, risks, action plan, and schema markup recommendations

The 8 AI models tested

Model	Provider
GPT 5.4	OpenAI
Claude Opus 4.6	Anthropic
DeepSeek V3	DeepSeek
Perplexity Sonar Pro	Perplexity AI
Gemini 2.5 Flash	Google
Kimi 2.5	Moonshot AI
Claude Sonnet 6	Anthropic
Gemma 4 27B	Google

Pricing & runtime

$15 per audit, pay-per-event — you're charged once the audit completes and a report is successfully fetched; failed or timed-out runs are not charged. One audit = 8 models × 40 questions × two passes (~640 LLM calls), typically 15–30 minutes depending on site size.

Sample input

{
    "url": "https://acmeretail.com",
    "brand": "Acme Retail",
    "geoMarket": "United Kingdom",
    "competitors": ["Shopify", "BigCommerce"]
}

Only url is required. Everything else is auto-detected if omitted.

Sample output (trimmed)

Every run pushes one JSON object with the 13 sections below. Scores use an inferred (brand-agnostic) vs informed (brand-aware) pair, and their delta — the lift your own content gives AI perception.

{
    "overallScore": 3.1,
    "verdict": "WEAK",
    "aidaScores": {
        "awareness": { "inferred": 3.2, "informed": 5.8, "delta": 2.6 },
        "interest": { "inferred": 4.1, "informed": 6.4, "delta": 2.3 },
        "desire": { "inferred": 2.8, "informed": 5.1, "delta": 2.3 },
        "action": { "inferred": 2.5, "informed": 4.6, "delta": 2.1 }
    },
    "aiReadiness": { "inferred": 3.1, "informed": 5.5, "delta": 2.4 },
    "category": {
        "label": "Retail Technology Platform",
        "competitors": ["Shopify", "BigCommerce", "WooCommerce", "Magento"],
        "customerType": "Mid-market e-commerce brands ($1M-$50M revenue)"
    },
    "stageAnalysis": {
        "awareness": {
            "finding": "AI models recognise Acme Retail in only 3 of 8 models when asked about retail technology platforms without context...",
            "recommendation": "Publish authoritative category-defining content that positions Acme Retail as a distinct alternative in the mid-market segment..."
        }
    },
    "promptedAwareness": {
        "question_type": "prompted_awareness",
        "mean_rank": 3,
        "top3_rate": 0.625,
        "recognised_rate": 0.875
    },
    "byProvider": {
        "GPT 5.4": {
            "display_name": "GPT 5.4",
            "stages": { "awareness": { "inferred": 3.2, "informed": 5.8 } },
            "sentiment": 0.45
        }
    },
    "proofSignals": [
        {
            "label": "Case Studies",
            "detected": false,
            "why": "No case study content found on the website.",
            "fix": "Publish 3-5 customer case studies with quantified outcomes."
        }
    ],
    "messagePillars": {
        "brand_perception": [
            {
                "theme": "Mid-market focus",
                "type": "strength",
                "models": 6,
                "summary": "AI models consistently recognise the mid-market positioning as a genuine differentiator..."
            }
        ]
    },
    "websiteHealth": {
        "crawlHealth": { "total_pages": 16, "ok_pages": 14, "broken_pages": 1, "crawl_success_pct": 87.5 },
        "crawlStatus": { "usable": true, "content_pages": 14, "bot_blocked_pages": 0, "error_pages": 1 }
    },
    "keyRisks": [
        {
            "priority": 1,
            "category": "Visibility",
            "stage": "awareness",
            "finding": "Brand is invisible in 5 of 8 AI models for category-level queries. Competitors dominate the awareness funnel.",
            "recommendation": "Launch a structured content programme targeting the 12 highest-volume category questions identified in this audit.",
            "scores": { "inferred": 3.2, "informed": 5.8, "delta": 2.6 }
        }
    ],
    "actionPlan": {
        "items": [
            {
                "priority": 1,
                "category": "Content",
                "stage": "awareness",
                "dimension": "recall",
                "finding": "Brand is not mentioned in AI responses to category-level queries.",
                "recommendation": "Create 10 authoritative blog posts targeting high-volume category questions...",
                "effort": 3,
                "reward": 5
            }
        ],
        "nextSteps": {
            "week": ["Implement JSON-LD structured data (Organization, Product, FAQPage) on all primary pages", "..."],
            "month": ["..."],
            "quarter": ["..."]
        }
    },
    "schemaMarkup": {
        "existing": {
            "schemas": {
                "Organization": {
                    "quality": "incomplete",
                    "fields_present": ["name", "url"],
                    "fields_missing": ["description", "logo", "sameAs", "contactPoint"]
                }
            }
        },
        "pageFixes": {
            "p0": [
                {
                    "issue": "Missing meta descriptions",
                    "impact": "AI models and search engines cannot determine page purpose...",
                    "fix": "Add unique, descriptive meta descriptions (150-160 chars) to each page",
                    "pages": ["https://acmeretail.com/..."]
                }
            ]
        }
    },
    "questions": [
        {
            "question": "What are the best retail technology platforms?",
            "stage": "awareness",
            "responses": {
                "inferred": {
                    "GPT 5.4": { "text": "...Acme Retail offers a solid mid-market solution...", "mentions_brand": true },
                    "Claude Opus 4.6": { "text": "The leading platforms include Shopify, BigCommerce...", "mentions_brand": false }
                },
                "informed": {
                    "GPT 5.4": { "text": "...", "mentions_brand": true }
                }
            }
        }
    ]
}

FAQ

What exactly does "AI visibility" mean, and why does it matter? It's whether AI models like ChatGPT, Claude, and Perplexity know your brand, describe it accurately, and recommend it when someone asks a buying question in your category. As more people ask an AI instead of Googling, a brand that's invisible or misrepresented in those answers loses the sale before a human ever sees a search result.

How is this different from tools that just track brand mentions? Mention-tracking tools tell you that a model said your name. This audit runs the same 40 buyer-intent questions twice — once with no brand context (inferred) and once with it (informed) — so it isolates why you're winning or losing at each AIDA stage, then turns that into a ranked, prioritised action plan instead of a raw mention count.

Which AI models does it actually query? Eight, in a single run: GPT, two Claude models, Gemini, Perplexity, DeepSeek, Kimi, and Gemma — see The 8 AI models tested above for the exact versions.

What's the "inferred vs informed" delta, and why does it matter? inferred is how a model answers with no brand context — its baseline opinion of the category. informed is how it answers once your brand is in the prompt. The gap between the two (delta) shows exactly how much lift your own content and positioning are giving you versus the category default — a small delta means your content isn't moving the needle even when the model knows who you are.

How long does a full audit take, and what does it cost? $15 per audit, pay-per-event — 8 models × 40 questions × two passes (~640 LLM calls), typically 15–30 minutes. liteMode: true runs a single model on sampled questions in under a minute if you want a fast directional check first.

Do I need to give it my own OpenAI/Anthropic/etc. API keys? No — the actor calls all 8 models itself through its own backend. You only need your Apify token to run it.

Can I run this on a schedule to track visibility over time? Yes — set up an Apify schedule to re-run the audit weekly or monthly against the same URL and watch overallScore, the AIDA stage scores, and keyRisks move as you ship the action plan's recommendations.

What do I actually do with the JSON-LD schema markup it gives me? Copy the templates from the schemaMarkup section straight into your site's <head> (or your CMS's structured-data field) — they're prioritised (p0/p1/...) so you know which to ship first, and they're the same Organization/Product/FAQPage schema types AI crawlers and traditional search both parse.

Does a good score here mean I'll rank well in traditional Google search too? Not automatically — this audit measures how AI models perceive and recommend your brand in conversational answers, which overlaps with but isn't identical to classic SEO ranking factors. Many of the fixes (structured data, clear category positioning, proof signals) help both, but a strong AIDA score isn't a proxy for a Google ranking.

Can I audit competitors instead of just my own brand? The audit is built around your url/brand, but every question set also surfaces your top 5 competitors (auto-detected or supplied via competitors) and scores your promptedAwareness rank against them — so you get comparative standing as part of the same run, not a separate mode.

Use cases

• SEO agencies — add AI visibility audits to your service offering
• Brand managers — understand how AI assistants recommend (or ignore) your brand
• Content strategists — find the gaps in your AI-facing content
• Competitive intelligence — see how AI models rank you against competitors
• Web developers — get copy-paste JSON-LD schema markup with priority levels
• AI-first companies — monitor your visibility as AI search grows

Integrations

This actor outputs structured JSON to the default Apify dataset. Use it with:

• Apify API — call programmatically from any language
• Webhooks — trigger downstream workflows when the audit completes
• Integrations — connect to Google Sheets, Slack, Zapier, Make, etc.
• Scheduled runs — monitor AI visibility weekly or monthly

Built by AI experts

Does AI Choose You? is built and maintained by the AI experts at NativeFoundation.