AI Brand Visibility Tracker — ChatGPT, Perplexity, Gemini, Copilot & Google AI Overview

Does ChatGPT recommend your brand?

Track AI Brand Visibility across 5 AI search engines (ChatGPT, Perplexity, Microsoft Copilot, Google AI Overview, Google AI Mode) — and optionally Gemini + Grok — for $0.30 per query. Each query expands into 15–25 real AI interactions with ground-truth validated results. No subscription, no API keys, no monthly contracts. Co-designed with a senior SEO/GEO analyst. MCP-ready for Claude Desktop, Cursor, ChatGPT and AI agents.

A Profound / Otterly / AthenaHQ alternative — without the $29–$489/month subscription. $0.30/query, pay-as-you-go, 60–160× cheaper per AI datapoint than Otterly Lite.

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What is AI Brand Visibility?

AI Brand Visibility measures how often, how prominently, and how consistently your brand appears in answers from generative AI search engines — ChatGPT, Perplexity, Google AI Overview, Google AI Mode, Microsoft Copilot, Gemini — when buyers ask the questions that move their decision. It is the AI-search equivalent of organic SEO ranking.

The field has three near-synonyms:

• GEO (Generative Engine Optimization) — optimising for the answer the AI generates.
• AEO (Answer Engine Optimization) — optimising for the question-answer pair.
• AI Search Optimization — the umbrella term covering both.

This actor reports all three views in one $0.30 run, with ground-truth validation that catches the 5.6× hallucination inflation other AI Brand Monitoring tools quietly pass through.

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⚡ Quick Start — try in 30 seconds

Click Try for free above. The default input runs a real scan against Apify as the brand — you'll see actual JSON output, ground-truth validated, in 3 minutes.

Then change brand to your own:

{
  "brand": "HubSpot",
  "category": "CRM software",
  "competitors": ["Salesforce", "Pipedrive", "Zoho CRM"],
  "language": "us",
  "queries": [
    "best CRM for marketing agencies",
    "HubSpot vs Salesforce for small business",
    "AI features in HubSpot CRM"
  ]
}

Sample output (real scan, trimmed):

{
  "brand": "HubSpot",
  "summary": {
    "ais": 74.6,
    "mention_rate": 89.6,
    "share_of_voice": 14.4,
    "consistency_score": 46.7,
    "avg_position": 2.0,
    "top3_rate_pct": 86.8,
    "sentiment_positive_pct": 39.7,
    "dominant_framing": "leader",
    "ground_truth_validated": true
  },
  "competitors": [/* same metrics for each tracked competitor */],
  "per_platform_per_brand": [/* full engine × brand matrix */],
  "per_query": [/* every (query × engine) cell with framing, position, gap class */],
  "top_cited_domains": [/* domains AI engines trust on your topic */]
}

3 queries = ~$0.90 on the Free Apify tier, ~$0.54 on Gold (40% volume discount).

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💰 Why $0.30 (and not $29–$489/month subscriptions)

	Otterly Lite	AthenaHQ Self-Serve	DoesAIKnow (this actor)
Entry price	$29/mo (annual contract)	$95–$295/mo	$0.30/query, no subscription
Engines included	4 (Gemini + AI Mode are $9/mo add-ons each)	8	5 default + Gemini/Grok on demand
Cost per AI datapoint	$1.93	~$0.08	$0.012 – $0.020
Ground-truth validated	Marketing copy yes, reality varies	Not advertised	Yes — 3-layer markdown gating, documented
MCP server (Claude / Cursor)	No	No	Yes (on roadmap, same dataset)
Minimum commitment	Monthly subscription	Monthly subscription	3 queries ($0.90)

Otterly/AthenaHQ pricing verified 2026-05-15. Per-datapoint math: $0.30/query × 15–25 datapoints = $0.012–$0.020/dp. 60–160× cheaper than Otterly Lite ($1.93/dp). Even AthenaHQ's $0.08/credit is 4–8× more.

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🎯 What this AI Brand Visibility Tracker does

AI search is replacing classic Google. When a buyer asks ChatGPT, Perplexity, Gemini, or Google AI Overview "what's the best CRM for a marketing agency?" — does your brand appear? In what position? With what framing (leader, alternative, just-mentioned)? How do competitors perform on the same queries on the same engines?

Most AI-visibility tools give you one prompt on one or two engines and call it a day. That's a lucky-guess audit. This actor covers buyer intent. One query you type becomes 3–5 semantically related variants (sourced from Google's People-Also-Ask and related searches), each run across 5 engines — so one query buys you 15–25 validated AI data points instead of one.

On top of that, every metric in the output is ground-truth validated against the raw AI response text. Brand extraction LLMs routinely hallucinate up to 5.6× more mentions than actually appear. We catch that inflation at three layers before it touches your numbers.

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💸 Pricing detail — flat $0.30/query, automatic volume discount via Apify tier

Apify tier	Price per query	Discount
Free	$0.30	—
Bronze (Starter)	$0.27	−10%
Silver (Scale)	$0.23	−23%
Gold (Business)	$0.18	−40%

Minimum order: 3 queries ($0.90 on Free tier, $0.54 on Gold).

What you actually get for $0.30

1 query you typed
   └── 3–5 semantically related variants (People-Also-Ask + related searches)
        └── × 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overview)
              └── = 15–25 real AI responses
                    └── Each validated against the raw markdown (3-layer ground truth)
                          └── Scored, classified, cross-compared with competitors

15–25 real AI datapoints for $0.30. At the Gold tier that's $0.007–$0.012 per datapoint.

Sample costs

Scenario	Queries	Free tier	Gold tier
Quick brand check	3	$0.90	$0.54
Monthly brand scan	10	$3.00	$1.80
Deep competitive audit	30	$9.00	$5.40
Agency client report (5 clients)	100	$30.00	$12.00
Heavy power user	500	$150.00	$60.00

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🤖 MCP server — built for AI agents, not just dashboards

Most AI Brand Visibility tools think their customer is a marketer staring at a dashboard. We think half the future customer base is an AI agent acting on behalf of a marketer.

The same dataset this actor writes to your Apify dataset is being exposed (next major release) via a Model Context Protocol (MCP) server at api.doesaiknow.com/mcp. That means:

• Claude Desktop can ask "compare HubSpot vs Salesforce visibility across ChatGPT and Perplexity for Q3 buyer-intent queries" and get a typed answer back in one tool call.
• Cursor developer agents pull AI Brand Visibility data into customer-facing code without leaving the editor.
• ChatGPT custom GPTs, n8n / Zapier MCP nodes, and any in-house AI agent get the same dataset as a native tool — no scraping, no API key juggling.
• Pay-per-query economics carry over: agents pay $0.30 per query consumed, no monthly commitment.

If your competitor-tracking, AEO audit, or content-gap workflow is already MCP-driven, plug us in directly.

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🆚 How this AI Brand Visibility Tracker compares

vs. AI-visibility SaaS subscriptions (Otterly, AthenaHQ, Peec, Profound)

	Typical AI-visibility SaaS	This AI Brand Visibility Tracker
Entry price	€29–€489 / month (annual contracts common)	$0.30 per query, pay as you go
Setup	Account, onboarding call, contract	Zero setup — paste brand, click Run
AI engines covered	3–5 (depends on plan)	5 default + 2 optional (Grok, AI Mode)
Fan-out per query	None or 1–2 variants	3–5 automatic variants
Ground-truth validation	Marketing copy says yes, reality varies	3-layer markdown gating, documented
Data format	Dashboards, rarely API	Structured JSON in your Apify dataset
MCP / AI agent integration	None	MCP server on roadmap (Q3)
Kill switch	Cancel before renewal	No recurring bill to cancel

Bottom line: a 10-query audit here costs $1.80–$3.00. Otterly Lite starts at $29/month for 15 prompts ($1.93/prompt).

vs. DIY using raw OpenAI / Anthropic / Google APIs

	Build it yourself	This AI Brand Visibility Tracker
Cost per validated datapoint	$0.30–$0.50 (LLM tokens + proxies + orchestration)	$0.005–$0.020
Engineering time	40+ hours (scraping, rotation, schema, validation)	0 hours
API keys to manage	5+	None
Proxy rotation, captcha handling	On you	Handled
Hallucination validation	On you	Built in

vs. other Apify brand-visibility actors

We cost more per "item" on paper than some bare scrapers. Here's what the per-item price hides:

What you actually get per query	Cheapest Apify alternatives ($0.008–$0.10 per item)	This actor ($0.30/query)
AI engines covered	1–3 (often Perplexity only, or ChatGPT only)	5 (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overview)
Real AI datapoints per unit you pay for	1	15–25
Fan-out across related queries	No — one prompt, one answer	3–5 variants per query, sourced from Google PAA + related
Ground-truth validation	No — LLM says brand X was mentioned, you trust it	Yes — literal markdown substring match, 3 layers
Hallucination inflation control	No — up to 5.6× inflated mention rates	Yes — validated numbers only
Real browser sessions vs. API	Often API-only (different answers than users see)	Real browser sessions
AI Visibility Score (AIS) with consistency gating	No	Yes — composite 0–100 score
Framing enum (leader / compared / …)	No	Yes — 7-class structured
Gap analysis	No	Yes — 5-class structured with severity
Entity resolution (dedup of "HubSpot", "HubSpot CRM")	No	Yes — 3-phase: normalize → prefix → LLM grouping
Per-platform × per-brand matrix	No	Yes — full matrix with platform-local SoV
Owned vs. earned citation split	No	Yes
Sentiment breakdown (positive / neutral / negative)	No or only positive	Full three-bucket
Top-cited domains ranking	No	Yes — with engine coverage + citation roles
Output format	Flat CSV or plain text	Structured JSON with typed enums
Cost per validated datapoint	$0.008–$0.10	$0.005–$0.020 (at Gold tier)

Cheaper per-item actors give you one scrape. We give you a full AI Brand Visibility audit for the same dollar amount, with data you can actually trust in a board deck.

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🔬 LLM SEO / AEO / GEO — the technical moat

1. Ground-Truth Validation (3 layers)

When a downstream LLM is asked "which brands are mentioned in this AI response?" it routinely invents brands that were never actually in the response. In our internal benchmarks the worst offender claimed a brand was mentioned 5.6× more often than it actually appeared in the raw text.

Our fix:

• Layer 1 — markdown evidence. Every mention rate is computed by literal lowercase substring match against the raw response markdown. Not LLM extraction rows. Real text, or nothing.
• Layer 2 — validated metrics. Sentiment, position, prominence are computed only for response rows whose markdown actually contains the brand name. Hallucinated rows are discarded before scoring.
• Layer 3 — consistency from evidence rates. Our consistency score uses markdown evidence rates, not inflated LLM counts.

Every response carries ground_truth_validated: true so you know the gate ran. This is the differentiator no SaaS competitor advertises.

2. Query fan-out that matches real buyer intent

Real buyers don't search "best CRM" once and stop. They ask 3–5 variations — "CRM for small business", "HubSpot vs. Salesforce", "affordable CRM with AI". If your tool checks just the seed query and your brand happens to miss it, your AI search visibility audit says you're invisible, and you're wrong.

Our fix: every query you submit goes through Google's People-Also-Ask and related-searches panel. An LLM picks the top 3–5 most semantically related variants. All variants run across all 5 engines.

One query you pay for = 15–25 real AI interactions.

3. Five AI engines covering every major AI ecosystem

• OpenAI ChatGPT — the consumer AI, search-enabled by default in this actor.
• Perplexity — the "AI search engine" of choice for technical buyers.
• Google Gemini — Google's standalone AI chat, distinct from AI Overview/Mode.
• Microsoft Copilot — Bing-grounded, default for 400M+ Windows users.
• Google AI Overview — the AI answer box above Google organic results. Most user-facing AI surface in 2026.
• Google AI Mode — Google's standalone conversational mode, separate from AI Overview.
• Grok — opt-in via platforms: [...].

Want Grok or AI Mode too? Pass platforms: [...] explicitly and override the default mix (7 engines total supported).

4. Entity resolution (no double-counting)

"HubSpot", "HubSpot CRM", "HubSpot Inc." would triple-count your mention rate in a naive pipeline. We run a 3-phase resolution pipeline — L1 normalize → L1.5 prefix merge → L2 LLM grouping — so these all collapse into one canonical brand before scoring.

5. Platform-local Share of Voice

Per-platform × per-brand matrix is computed with tracked-only denominators — your target plus the competitors you defined, excluding third-party mentions the AI drops in. SoV of 28% on ChatGPT means 28% of tracked brand mentions on ChatGPT, not 28% of every brand the AI ever mentioned (which would be meaningless).

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🧑‍💼 Co-designed with a senior SEO / GEO analyst

This actor isn't a "we threw a prompt at ChatGPT and called it brand monitoring" project. The metric set, formulas, validation gates, sort orders, tier semantics and output schema were co-designed with a senior SEO / GEO analyst who runs paid AI Brand Visibility audits for agency clients. Concretely:

• AIS formula (with consistency gating) and 5-class gap analysis were ported from a battle-tested production reporting stack used in the field, then adapted to ground-truth validation.
• Per-platform × per-brand matrix matches the structure agencies already use in client decks — you can drop the JSON straight into Looker / Sheets / a slide and it makes sense without re-shaping.
• Tracked-only Share of Voice is the version that survived analyst scrutiny — global SoV (denominator = every brand the AI ever hallucinated) was rejected as misleading.
• Aggregate strengths / weaknesses and top-cited domains with role classification are pulled into the output because that's what content strategists ask for first when they get a brand audit.

Net result: the JSON we ship is what an analyst would build in Looker after a week of querying raw data, except you get it in 3–15 minutes for $0.30 per query, with hallucination control already applied.

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📊 What you get in the output

Section	What's inside
Summary	Your brand's AI Visibility Score (AIS), mention rate, share of voice, consistency score, sentiment breakdown, owned / earned citation split, top-10 aggregate strengths and weaknesses, dominant framing.
Competitors	Same aggregate metrics, one row per tracked competitor.
Per-platform	Each engine's coverage %, mention rate, AIS, average position, top-3 rate, sentiment breakdown. Sorted by AIS desc so [0] is your strongest engine.
Per-platform × per-brand matrix	One row per (engine × tracked brand). Answers "where am I winning, where am I losing" in one table.
Per-query	Every (query × engine) cell with framing, sentiment, position, gap class, opportunity type, markdown excerpt, strengths, weaknesses, and full citation list with domain types and roles.
Top cited domains	Ranked list of domains AI engines trust on your topic. Flags your own domain as is_brand_owned: true.
Upgrade CTA (demo and free tiers)	Counts of platforms, queries, domains, matrix rows hidden at this tier.

Full OpenAPI contract is in the source repo.

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🚀 Input — what you pass in

{
  "brand": "HubSpot",
  "category": "CRM software",
  "competitors": ["Salesforce", "Pipedrive", "Zoho CRM"],
  "language": "us",
  "queries": [
    "best CRM for marketing agencies",
    "HubSpot vs Salesforce for small business",
    "AI features in HubSpot CRM"
  ]
}

• brand — required. The brand you're auditing. Unicode letters supported (e.g. Żabka, Müller, L'Oréal).
• queries — required for paid scans (min 3, max 15). Write them in the language your customers use; each becomes 3–5 fan-out variants × 5 engines = 15–25 real AI calls. Total cost = N_queries × $0.30. Omit only when tier="demo" (cached preview, no charge).
• category — optional. Plain-English product/market category (e.g. "CRM software", "opony do samochodu"). Backend treats it as "general" if omitted; paid scans use your queries verbatim and don't strictly need it.
• language — optional. ISO-3166 country code (lowercase, 2 chars: us, pl, de, fr, es, it, gb, br, jp, ...). Drives DataForSEO geo + locale routing for the AI Overview / AI Mode engines (53 countries supported). Unknown codes silently fall back to US/English. Default: us.
• competitors — optional. Up to 5 brands you want ranked alongside you (matrix + scoring covers them). Each gets the same enriched output: AIS, mention rate, share of voice, top-3 rate, sentiment trio, dominant framing, plus aggregate strengths/weaknesses and citation_mix per competitor (since v0.5).
• platforms — optional override of the default 5-engine mix (chatgpt, perplexity, gemini, copilot, ai_overview). Pick from the 7 supported (above + grok, ai_mode).

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📦 Sample output — full

{
  "brand": "Ahrefs",
  "category": "SEO tools",
  "summary": {
    "ais": 74.6,
    "mention_rate": 89.6,
    "share_of_voice": 14.4,
    "consistency_score": 46.7,
    "avg_position": 2.0,
    "top3_rate_pct": 86.8,
    "sentiment_positive_pct": 39.7,
    "sentiment_neutral_pct": 57.2,
    "sentiment_negative_pct": 2.7,
    "dominant_framing": "leader",
    "owned_citation_pct": 9.3,
    "earned_citation_pct": 90.7,
    "total_citations_seen": 2735,
    "aggregate_strengths": ["backlink analysis", "keyword research", "Content Explorer", "Site Explorer"],
    "aggregate_weaknesses": ["no free trial", "expensive", "steep learning curve", "restrictive credit system"],
    "avg_prominence": 87.3,
    "ground_truth_validated": true
  },
  "competitors": [
    {
      "brand": "Semrush",
      "ais": 67.2, "mention_rate": 82.0, "share_of_voice": 13.6, "top3_rate_pct": 78.4,
      "sentiment_positive_pct": 46.9, "owned_citation_pct": 4.2, "earned_citation_pct": 95.8,
      "aggregate_strengths": ["all-in-one platform", "broader keyword database", "site audit"],
      "aggregate_weaknesses": ["UI clutter", "weaker backlink data than Ahrefs"],
      "citation_mix": {"blog": 38.5, "media": 12.1, "review_site": 8.2, "brand_owned": 4.2, "other": 37.0}
    }
  ],
  "per_platform_per_brand": [
    { "platform": "copilot",    "brand": "Ahrefs",  "ais": 84.7, "mention_rate": 98.0, "share_of_voice": 28.0, "top3_rate_pct": 87.0 },
    { "platform": "ai_mode",    "brand": "Ahrefs",  "ais": 80.9, "mention_rate": 96.0, "share_of_voice": 29.7, "top3_rate_pct": 82.7 },
    { "platform": "chatgpt",    "brand": "Ahrefs",  "ais": 77.7, "mention_rate": 94.0, "share_of_voice": 28.7, "top3_rate_pct": 85.4 },
    { "platform": "perplexity", "brand": "Ahrefs",  "ais": 73.6, "mention_rate": 90.0, "share_of_voice": 26.9, "top3_rate_pct": 93.0 },
    { "platform": "ai_mode",    "brand": "Semrush", "ais": 64.9, "mention_rate": 82.0, "share_of_voice": 25.3, "top3_rate_pct": 77.5 },
    { "platform": "copilot",    "brand": "Semrush", "ais": 62.4, "mention_rate": 78.0, "share_of_voice": 27.4, "top3_rate_pct": 82.1 }
  ]
}

Three insights this AI Brand Visibility Tracker already hands you for free from the data above:

1. Ahrefs dominates Copilot (AIS 84.7, 98% mention rate) — that's your strongest engine.
2. Perplexity puts you top-3 in 93% of responses — highest top-3 rate anywhere. Strong editorial signal on that engine.
3. 11-point AIS spread across engines (73.6 → 84.7) — your brand is not uniformly visible. Content ops have clear per-engine targets.

Pay $0.30, get actionable board-deck material like this.

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🧭 Who this AI Brand Visibility Tracker is for

• SEO & GEO agencies running monthly AI Brand Visibility audits for clients. Use this to export directly into Looker or a client dashboard.
• B2B SaaS brand managers who need to prove "we won the AI mention race" in a quarterly board deck. Full competitor comparison included.
• LLM SEO consultants focused on AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) — the per-query gap class and opportunity type are the work order for your editorial calendar.
• Competitive intelligence & RevOps teams tracking competitor share-of-voice across ChatGPT, Perplexity, Gemini and Copilot — without paying $1,600/mo for a generic CI platform.
• Content strategists looking to see exactly which query intents leave them absent from AI answers.
• Sales Ops personalising cold outreach at scale: 1 query per prospect via Clay or Apify API.
• Freelance SEO consultants who don't want a $99+/month subscription just to run one audit.
• AI agents (Claude Desktop, Cursor, ChatGPT custom GPTs) querying the dataset natively via MCP.

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⏱️ How long does a scan take?

Queries	Approx. runtime	Approx. data points
3	3–5 min	45–75
10	8–15 min	150–250
30	20–40 min	450–750
100	60–120 min	1,500–2,500

Scans are parallel across engines; the bottleneck is slower AI engines (ChatGPT, AI Mode) on heavy load. Your Apify dataset populates the moment aggregation finishes, and PPE events only fire on successful completion — partial / failed scans aren't charged.

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🛡️ Legal & compliance

This actor only queries publicly available AI search interfaces (consumer ChatGPT, Perplexity, Gemini, Microsoft Copilot, Google AI Overview / AI Mode). It does not bypass authentication, scrape gated APIs, or store user-generated content. Brand and category strings you submit are processed transiently for the scan; results are written into your Apify dataset under your account. We don't share or sell scan content. Each AI engine is queried per its public Terms of Service through real-browser automation; if an engine returns a rate-limit or compliance signal, the scan retries within published limits and the failure is surfaced in the result. GDPR / CCPA compliance applies to the brand/category strings you submit — don't pass personally identifying information.

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🧰 FAQ

What exactly counts as 1 query? One topic or question you care about. Behind the scenes we expand it to 3–5 semantic variants (from Google PAA + related) and run each across 5 engines. You pay once; you get 15–25 real AI data points.

Do I need to bring my own OpenAI / Anthropic / Google API keys? No. Everything is included in the $0.30 per query.

Why is this called a "tracker" — can I run it on a schedule? Yes. Apify supports scheduled runs out of the box. Set a weekly schedule with queries for your core buyer-intent topics and you get continuous AI Brand Visibility tracking without any subscription — billed only on each successful run.

What happens if a scan fails mid-way? Every task is idempotent (tracked by a unique correlation key). If one AI engine throws, we retry automatically. The PPE event fires only on successful completion, so a dead-ended scan isn't charged.

Can I scan a brand in Polish, German, or another language? Yes. Ground-truth validation uses Unicode NFC + casefold normalization, so "Żabka", "München", "İstanbul" all match reliably against any normalization form the AI uses.

Can I trust the numbers? Yes, and the system is designed around that question. Every metric except Sentiment is backed by a literal markdown substring match. Sentiment is LLM-classified but gated by markdown evidence. Every response carries ground_truth_validated: true confirming the validation ran.

Where does the data come from? AI engines are scraped through real browser automation and public SERP APIs. Raw markdown is preserved for every response because hallucination validation needs it.

Is my input private? Brand and category are public information. Results live in your Apify dataset under your account. We don't share or sell scan content.

Can I use this AI Brand Visibility Tracker for a client's brand? Yes. The output is a complete, self-contained AI Brand Visibility report you can deliver as-is to clients.

How does this compare to running 100 scans on a cheaper Apify actor? A cheaper actor at $0.008 per item gives you 100 items. This AI Brand Visibility Tracker at $0.30 per query gives you 1,500–2,500 validated AI data points across 5 engines with scoring, ground-truth validation, entity resolution, fan-out, and a full competitor matrix. On a per-validated-datapoint basis we are cheaper, not more expensive.

How is this different from Otterly.ai / AthenaHQ / Peec.ai / Profound? Those are SaaS subscriptions ($29–$489/month, account, dashboard). This is an Apify actor — pay-per-query, no subscription, structured JSON output, MCP-ready for AI agents. Same data class, different distribution + pricing model. Use Otterly/AthenaHQ if you want a hosted dashboard for daily monitoring; use this actor for programmatic / agency-client / one-off audit work — or when you want to send the data straight to an MCP-aware AI agent.

Will there be an MCP server? Yes — it's the next major channel after the Apify actor. The same ground-truth validated AI Brand Visibility analytics will be exposed via a Model Context Protocol server so Claude Desktop, Cursor, ChatGPT custom GPTs, n8n / Zapier MCP nodes and any other MCP-aware AI agent can call the dataset as a native tool. Pricing model carries over: agents pay per query, no monthly commitment.

Refund policy? Apify's standard PPE model: you're only billed on successful task completion. Failed or partial scans are not charged.

Who designed the metric set? The actor is co-developed with a senior SEO / GEO analyst who runs paid AI Brand Visibility audits for agency clients. The AIS formula, the 5-class gap analysis, the per-platform × per-brand matrix layout, the tracked-only Share of Voice convention, and the citation-domain rankings are all built around what an analyst actually puts in a client deck — not what looks good in a tool screenshot.

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🗺️ Roadmap

Features we're actively building. No ETAs — we ship when it's good, and changes land behind the per_* fields you already consume (backward compatible).

Coming soon

• MCP server (Model Context Protocol) — a hosted MCP endpoint so any MCP-aware client (Claude Desktop, Cursor, ChatGPT custom GPTs, n8n / Zapier MCP nodes, in-house AI agents) can ask "how does HubSpot perform across all major AI engines vs Salesforce on Q3 buyer-intent queries?" and receive ground-truth validated, structured AI Brand Visibility analytics in a single tool call. Same pricing model — billed per query consumed by the agent.
• Executive summary (LLM narrative) — 2–4 sentence plain-English interpretation of your AI Brand Visibility profile. "Ahrefs dominates Copilot at AIS 84.7 but loses 11 points on Perplexity — invest in Perplexity-native citation sources to close the gap." Co-written with our SEO analyst. Available on every paid tier.

Planned

• Discovered brands — brands AI engines surface organically around your topic, outside the competitors[] list. Catches blind spots.
• Cited pages per owned domain — per-engine citation counts for every URL on your own domain. Tells you which pages to double down on and which to kill.
• Content opportunity map — full dump of People-Also-Ask + related candidates from the fan-out pass, including those the selector didn't pick. Idea-generation surface for content strategists.
• Clay / Zapier / Make.com native integrations — 1-click action nodes.
• Looker / Sheets / BI exports — ready-to-paste dataset templates for analyst-grade reporting.

Shipped recently

• Per-platform × per-brand matrix with tracked-only Share of Voice.
• Owned vs. earned citation split.
• Top-3 rate and position scoring on flowing-markdown engines (Copilot, AI Mode, AI Overview).
• Aggregate strengths and weaknesses (top-10 per brand).
• Full sentiment breakdown (positive / neutral / negative).
• Preview mode on demo tier with upgrade hints.

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🔗 Sibling actors from the same doesaiknow developer

• Keyword Metrics Pro — bulk Google + Bing search volume + CPC + trend data ($1.35 per 1,000 keywords).
• AI Keyword Clustering Tool — AI-driven topical clustering with bulk SERP MERGE/SPLIT logic, built for AEO/GEO content strategy.

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🏷️ Keywords

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🤝 Built by

doesaiknow.com — the only AI Brand Visibility Tracker built around 3-layer Ground-Truth Validation to eliminate LLM hallucination from your data, co-designed with a senior SEO / GEO analyst, and built to ship the same dataset to Apify, MCP-aware AI agents, BI tools and APIs under a single pay-per-query economic model.