| Option | What it constrains | Right when |
|---|---|---|
| Writesonic Starter, $79 | Answers. 50 a day against 150 needed for its own 50 prompts on three engines | Never, for measurement. It reads a third of its own prompt set and carries no sentiment. |
| Writesonic Basic, $199 | Engines. Three, and no sentiment analysis | Your buyers sit on ChatGPT and Google, and you want articles and site audits in the same seat. |
| Writesonic Growth, $399 | Engines again. Still three, now with sentiment | You need sentiment and 200 prompts, and the seven gated engines genuinely do not carry your category. |
| Writesonic Agency Starter, $200 | Duration. Pitch projects auto-pause after seven days | You are auditing prospects rather than tracking clients, and nine engines for $200 is the point. |
| Writesonic Enterprise | Price, which is quoted rather than published | You need all ten engines, GA4 and Looker, SSO and SOC 2 Type II in one contract. |
| Peec AI | Three of six engines on every self-serve tier, with Claude held for Enterprise | You want unlimited seats on a similar engine count. What Peec tracks covers the gating. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 a month | You need daily depth on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Setting | What goes wrong when it is unknown | What to ask for |
|---|---|---|
| Engine mix | Your trend line tracks the vendor's engine weighting, not your work. Adding a citation-dense engine looks like a win. | The per-engine breakdown, and the weighting used in the blended score. |
| Prompt set | You cannot compare quarters, because a refreshed prompt list resets the baseline silently. | The full prompt list as text, plus a written change policy. |
| Runs per prompt | Weekly churn gets read as performance. Teams reallocate budget against sampling noise. | Runs per prompt per day, and whether the figure shown is a single run or a mean. |
| Interface and grounding | You optimise for a surface your buyers never touch, and the wins do not reproduce. | Which route produced each reading: raw API, grounded API, or consumer interface. |
| Counting rule | Two dashboards disagree by 20 points and both are right, which destroys trust in the whole program. | The definition, in one sentence, of what increments the headline metric. |
| None of the above | Nothing, if you freeze all five and only compare a tool to itself. | A settings sheet you keep beside the dashboard. |
| Option | What it constrains | Right when |
|---|---|---|
| Bluefish AI | Budget and org readiness. No published tier, quota or cadence, so unit price cannot be computed before a call. Onboarding runs four to six weeks | You run many products across many AI channels, wrong AI answers cost you revenue, and you have the team to run campaigns rather than read charts. |
| Brandlight | Price and scope, both of which are quoted. Three pricing URLs return 404 | You need a portfolio view across brands and regions. What it costs and covers separates published from inferred. |
| Goodie AI | Optimization actions. Ten fixes a month against roughly 15,000 observed answers | You want the acting half metered rather than assumed, at a published price. The two meters are divided out. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 a month | You need daily depth on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| Conductor | Sampling depth. The AI allowance is annual rather than monthly, and no price is published | You want AI visibility read beside ten years of your own organic data. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| Brandlight | Price and scope, both of which are quoted. No published tier, quota or cadence, so cost per answer cannot be computed before a call | You run many brands or markets, need a portfolio view, and the Partnerships and agentic-commerce modules map to real budget lines. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 a month | You need daily depth on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| Peec AI | Three of six engines on every self-serve tier, with Claude held for Enterprise | Your buyers cluster on three engines and you need unlimited seats. What Peec tracks covers the gating. |
| Rankscale | Credit balance, but scheduling runs from hourly to monthly | You need one converged reading rather than a standing dashboard. What Rankscale measures prices the depth. |
| Conductor | Sampling depth. The AI allowance is annual rather than monthly, and no price is published | You want AI visibility read beside ten years of your own organic data. The annual credit is priced out. |
| Goodie AI | Optimization actions. Ten fixes a month against roughly 15,000 observed answers | You sell through retail assistants, or you want the acting half metered rather than assumed. The two meters are divided out. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Schema type | What it actually does for AI | Evidence | Verdict |
|---|---|---|---|
| Organization, Person | Resolves your brand to the right entity in a knowledge graph | No citation lift measured; disambiguation is a separate job | Ship once |
| Product, Offer, Review | Gates eligibility for shopping and comparison surfaces, carries liftable facts | 61.7% citation rate when fields are populated, against 41.6% generic | Ship where facts exist |
| FAQPage | Mirrors the question-and-answer shape a model reaches for | 350% and 41-vs-15 claims are uncontrolled; 45.6% in the AirOps split | Keep, expect little |
| Article, BlogPosting | Describes the page without asserting a fact about the world | Sits in the generic bucket that underperformed no markup | Low priority |
| BreadcrumbList | Site structure signal for Google, no answer payload | Top of the AirOps split at 46.2%, which rank explains | Low priority |
| HowTo | Step sequences a model can enumerate | Not isolated in any study; Ahrefs pooled it with everything else | Ship only for real procedures |
| Option | What it constrains | Right when |
|---|---|---|
| Goodie Core · $399 | Models and actions. Five models, no Gemini or Claude, and 10 fixes a month | Your buyers sit on ChatGPT, Perplexity and Google surfaces, and you have capacity for about ten changes a month anyway. |
| Goodie Pro · $999 | Actions, at 30 a month. Models stop at eight and Claude is not among them | You sell through retail assistants. Alexa and Sparky at self-serve exist nowhere else in this bracket. |
| Goodie Enterprise | Price, which is quoted rather than published | You need Claude, Meta AI, DeepSeek and Grok, multi-brand tracking, and a named strategist. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 a month | You need daily depth on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| Peec AI | Three of six engines on every self-serve tier, with Claude held for Enterprise | Your buyers cluster on three engines and you need unlimited seats. What Peec tracks covers the gating. |
| Conductor | Sampling depth. The AI allowance is annual, not monthly | You want AI visibility read beside ten years of your own organic data. The annual credit is priced out. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| Conductor Growth | Sampling depth. 2,500 credits a year refreshes 20 prompts on eight engines about every 23 days | You want AI visibility read beside ten years of your own organic data, and a monthly reading is enough. |
| Conductor Enterprise | Price, which is quoted rather than published, and the credit allowance is "2,500+" | The allowance is negotiable, you need APIs and SSO, and the platform is replacing two vendors rather than adding one. |
| Conductor Essentials | Scope. It carries no AI Search Credit line at all | Never, if AEO is why you are here. It is the SEO platform without the AI half. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 a month | You need daily depth on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| Rankscale | Credit balance, but scheduling runs from hourly to monthly | You need one converged reading rather than a standing dashboard. What Rankscale measures prices the depth. |
| Peec AI | Three of six engines on every self-serve tier, with Claude held for Enterprise | Your buyers cluster on three engines and you need unlimited seats. What Peec tracks covers the gating. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| Rankscale Pro | Credit balance. 1,200 credits is 4,800 answers, which is 20 prompts on eight engines daily and nothing spare | You want one converged reading a quarter and can keep the standing set narrow. |
| Rankscale Growth or Enterprise | Price, at $385 and $780. Cost per answer barely improves as you climb | You run many brands or client dashboards, or you want hourly running on a real prompt set rather than a sample. |
| Otterly | Engine mix. Claude, AI Mode and Gemini are paid add-ons on top of the four included | Cost per answer is the binding constraint. Our read on Otterly covers the multi-country angle. |
| Peec AI | Three of six engines on every self-serve tier, with Claude held back for Enterprise | Your buyers cluster on three engines and you need unlimited seats. What Peec actually tracks prices the fourth. |
| Profound | Price, and depth is negotiable only inside an Enterprise contract | Prompt-volume data from real user queries informs your roadmap, or commerce surfaces matter. |
| AthenaHQ | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 | Your buyers sit on engines the cheaper tools cannot reach. The credit arithmetic is worked out in full. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| AthenaHQ Starter | Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295 | Your buyers sit on engines the cheaper tools cannot reach, and you already know which questions matter. |
| AthenaHQ Enterprise | Price, and it is quoted rather than published | You need persona targeting, claim review or many markets, and the credit allowance is negotiable. |
| Peec AI | Three of six engines included on every self-serve tier, with Claude held back for Enterprise | Your buyers cluster on three engines and you need unlimited seats. |
| Ahrefs Brand Radar | Engine mix. No Claude at any tier, and an Ahrefs subscription sits underneath | Cost per answer binds and you want a demand-weighted prompt corpus. What Brand Radar actually tracks prices both halves. |
| Profound | Price. Depth is negotiable only inside an Enterprise contract | Engine breadth or prompt-volume data informs your roadmap. What Profound measures works through the sampling question. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Band | Monthly | What it realistically buys |
|---|---|---|
| Productized | $500 to $2,500 | Schema, some content restructuring, monitoring on one engine. Usually Google only. |
| Mid-market managed | $4,000 to $12,000 | Multi-engine measurement, ongoing passage work, structured data at scale, citation tracking. |
| Enterprise | $15,000 and up | Dedicated team, digital PR for off-page citations, entity architecture, pipeline attribution. |
| Option | What it constrains | Right when |
|---|---|---|
| Self-serve tracking tool | Fixes nothing. You get a reading and a queue you still have to work | Someone in-house owns the weekly loop and only needs instrumentation. |
| Consultant or fixed project | Ends when the invoice clears, before the first drift | You need a baseline and a plan, and you have a team to execute it. |
| Managed AEO agency | Costs a retainer. Cannot promise a citation count | Nobody owns the loop, and your source pool sits mostly off your domain. |
| Your existing SEO agency | Usually one engine and a rankings frame | They can show you a citation baseline. Ask before you assume. |
| Option | What it constrains | Right when |
|---|---|---|
| Brand Radar AI Index | Prompt control. You cannot add a prompt or change a cadence at any price | You want to find the demand-weighted questions your own prompt list never contained, and Ahrefs is already paid for. |
| Brand Radar Custom Prompts | Cadence, at daily. Cost per answer is the lowest in the bracket at $10.00 on Scale | You know your prompts, you want seven engines and multiple locations, and you want to allocate checks yourself. |
| Peec AI | Three of six engines included on every self-serve tier | Your buyers cluster on three engines and you need unlimited seats. What Peec actually tracks prices the fourth. |
| Profound | Price. Depth is negotiable only inside an Enterprise contract | You need more than four engines or the prompt-volume index informs your roadmap. |
| Otterly | Engine mix. Claude, AI Mode and Gemini are paid extras | Cost per answer binds and multi-country coverage matters. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| HubSpot AEO | Engine coverage, at three. Answers are capped at 2,500 or 5,000 a month whatever the prompt count | Marketing Hub Pro is already paid for and step 2 found your buyers on ChatGPT, Gemini or Perplexity. Our read on the launch covers what shipped in April. |
| Peec AI | Three of six engines included on every self-serve tier | You want a fourth engine and will price the add-on. What Peec actually tracks has the arithmetic. |
| Otterly | Engine mix. Claude, AI Mode and Gemini are paid extras | Cost per answer is the binding constraint and multi-country coverage matters. |
| Profound | Price. Depth is negotiable only inside an Enterprise contract | You need more than four engines or the prompt-volume index informs your roadmap. |
| Scrunch | Unit definition. Published prompt counts disagree by a factor of ten | You want seven platforms on an entry tier and will pin the unit down on the call. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Option | What it constrains | Right when |
|---|---|---|
| Stay on Profound | Cost per answer, at $44.33 per thousand on Growth. Depth is negotiable only at Enterprise | You need more than four engines, the prompt-volume index informs your roadmap, or commerce surfaces matter. |
| Otterly | Engine mix. Claude, AI Mode and Gemini are paid add-ons on top of the four included | Cost per answer is the binding constraint and your buyers sit on ChatGPT, Perplexity, Copilot or AI Overviews. Our read on Otterly covers the multi-country angle. |
| Peec AI | Three engines included from six on offer, on every self-serve tier | Your buyers cluster on three engines and you need unlimited seats. What Peec actually tracks prices the fourth engine. |
| Semrush AI Visibility | Prompt count. 25 daily prompts is one buying conversation | One domain, one core topic, and Semrush already in the stack. |
| Scrunch | Unit definition. Published prompt counts disagree by a factor of ten | You want seven platforms on an entry tier and will pin the unit down on the call. Our teardown covers the Sitecore split. |
| Ahrefs Brand Radar | Nothing, structurally. It is the only vendor pricing the unit that matters, at $0.020 per check | You want to buy depth on a narrow prompt set rather than breadth on a wide one, and you already pay for Ahrefs. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| Situation | Verdict | Why |
|---|---|---|
| You need broad platform coverage on a self-serve budget | Strong fit | Seven platforms including Meta on a $250 entry tier is the widest coverage in this bracket, and it does not move as you climb tiers. |
| You sell to two or more distinct buyer types | Strong fit | Personas are a first-class object with 3 on Starter and 5 on Growth, which most competitors handle as a filter or not at all. |
| You are already a Sitecore customer | Watch this closely | The DXP tie-in is the reason the acquisition happened, and the integration path will be better here than for anyone outside that stack. |
| Your cited-source pool is mostly communities and publishers | Wrong first purchase | Neither half reaches those domains. Fund earned placement first and come back when owned pages are the binding constraint. |
| You want AXP specifically | Price Enterprise, not Starter | It is on no self-serve tier. The plan you can buy today is the tracker, and the quote is a different conversation. |
| Your pages already render server-side | Skip the delivery layer | Token-light markup solves a problem you fixed in your own stack, without a second version of the site to keep current. |
| Option | What it constrains | Right when |
|---|---|---|
| Stay on BrightEdge | Three AI surfaces, bundled into a suite you cannot unbundle, at a price nobody publishes | Your estate is large, the crawl and template work is load-bearing, and the team opens it weekly. |
| Conductor | Four named engines, tied back to the page and topic that earned the citation | You want the enterprise workflow but need the citation data joined to content performance rather than sitting in a tab. |
| seoClarity ArcAI | Four engines, enterprise pricing, no self-serve entry | You are already on seoClarity and the switch is a module decision rather than a migration. |
| Semrush AI Visibility | Topics, not prompts. 25 daily prompts on the entry tier is one buying conversation | One domain, one core topic, and Semrush already in the stack. Our full teardown runs the prompt-to-topic maths. |
| Peec AI | Three engines included from six on offer, on every self-serve tier | Your buyers cluster on three engines and you need unlimited seats. What Peec actually tracks prices the fourth engine. |
| Profound | Sampling depth per prompt, not engine breadth | Engine coverage decides your reporting. What Profound measures works through answers per score. |
| A managed program | Nothing, if scoped right. It costs a retainer | The measurement was never the bottleneck and nobody owns the weekly loop. |
| What your own classification shows | Call | Why |
|---|---|---|
| Listicles are over half your cited URLs | Hold the placement budget, add the product-page work | B2B tech services ran at 61% listicle in the DeltaV data. Your category has not crossed over yet. |
| Product pages already lead your citations | Fund depth on the pages you have | You are on the rising format. The marginal return is in pricing, limits and constraint text, not in more pages. |
| Your product pages are cited but a competitor's are quoted | Fix the extractable passage, not the page count | Being crawled and being quoted are different outcomes. The quoted page carries the specific fact. |
| Homepages are taking most of your citations | Build the per-product layer | Homepage citations mean the engine could not find a page answering the specific query. |
| You have no format classification at all | Classify first, decide second | Every number in this post is a panel average. Panel averages are a prior, not a plan. |
| Your situation | What licensing means for you | Where the effort goes |
|---|---|---|
| B2B SaaS with no publishable archive | Not available at any price | Earned placement in the buying guides and review platforms already cited for your category |
| News publisher weighing an OpenAI deal | Real lift on ChatGPT, nothing elsewhere | Negotiate it, then keep measuring, because the deal is not a floor |
| Publisher already licensed to Google or Perplexity | Parity or slightly worse on that platform | Treat the deal as revenue, not as a visibility strategy |
| Brand blocking AI crawlers today | Blocking removes the only route you have | Open crawl access first, since you have no contract carrying content in |
| Anyone quoting the 48% in a business case | One month, one snapshot, two partners | Attach the date and the partnership disclosure before it reaches a slide |
| Source category | Aug 1-7 | Aug 14-18 | Change |
|---|---|---|---|
| Institutional, government and .org | 17.1% | 29.6% | +73% |
| Brand and commercial sites | 68.8% | 67.3% | Flat |
| Forums and other UGC | 3.2% | 0.9% | −70% |
| Press and media | 3.6% | 0.6% | −82% |
| Directories | 2.3% | 0.2% | −93% |
| 3.9% | 0.08% | −98% |
| If your own data shows | Call | Why |
|---|---|---|
| Reddit was under 5% of your ChatGPT citations in July | Do nothing | You were never exposed. The event is news, not a signal about your program. |
| Reddit was a top-three source and is now absent | Hold, and set the September 15 checkpoint | Two of three prior collapses reversed. Cutting inside a trough is the expensive mistake. |
| Reddit held on Perplexity and Google but fell on ChatGPT | Reweight by engine, do not cut | This is a single-engine query-construction change, not a devaluation of community sources. |
| You have no pre-August baseline at all | Baseline first, decide second | Any action taken now is taken against a vendor's panel rather than your own category. |
| Institutional sources now dominate your category's answers | Open the institutional tier | That is where the 73% went, and almost nobody in B2B is competing there yet. |
| Situation | Verdict | Why |
|---|---|---|
| Large existing content library, thin or stale, one owner who can ship | Strong fit | Grids and Quill are the fastest route from a refresh list to shipped pages, and this is the case where owned content genuinely is the constraint. |
| You already have a tracker and stall at the "now what" step | Strong fit | The execution layer is what pure-play trackers deliberately do not build, and Page360 joins the citation data to GSC and GA4 natively. |
| Your cited-source pool is dominated by communities and publishers | Wrong first purchase | The meter cannot reach those domains. Fund earned placement first and buy the content engine once the owned gap is the binding one. |
| You sell to developers or enterprise Microsoft buyers | Check the tier before comparing | Copilot and Grok are outside the named engine list, and the entry tier is ChatGPT only. |
| Multi-region or multi-persona B2B | Price Enterprise, not Solo | One persona and one region on the self-serve tiers means the instrument is narrower than the business. |
| You want to know whether you are visible at all | Start free | The $0 Insights tier answers that question without a task budget or a contract. |
| Route to more prompts | Reported cost | Daily prompts | Topics it covers | What you are really buying |
|---|---|---|---|---|
| AI Visibility Base | $99/mo per domain | 25 | 1 | A complete read on one buying conversation |
| Base on a Guru or Business SEO plan | $99 plus the SEO plan | 50 | 1 to 2 | Prompts bundled with tools you may already pay for |
| 50-prompt add-on | $60/mo | +50 | +1 to 2 | The cheapest prompts per dollar in the lineup |
| Semrush One Pro+ | $299/mo | 100 | 2 to 4 | A focused B2B category, reported honestly |
| Semrush One Advanced | $549/mo | 200 | 5 to 8 | Multi-topic reporting plus API access |
| Second domain, any tier | +$99/mo | Separate allowance | Starts again at 1 | The multiplier that catches agencies |
| Situation | Verdict | Why |
|---|---|---|
| One domain, one core buying conversation, Semrush already in the stack | Strong fit | Daily refresh on a single topic clears the convergence band, and the SEO data sits beside it at no extra integration cost. |
| SEO team taking on AI visibility without new headcount | Strong fit | The workflow is familiar, which is the difference between a tool used weekly and a tab nobody opens. |
| Three or more product lines to report on separately | Price the add-ons first | Each line needs its own 25 to 40 prompts. Budget from the topic count, not the headline tier. |
| Agency or multi-region brand | Check the domain math | Every domain restarts the allowance at full price, which is where per-domain pricing stops competing. |
| You sell to developers or enterprise Microsoft buyers | Poor fit alone | Claude, Copilot and Grok are outside the product, and for these audiences that is the surface that matters. |
| Nobody has been named as the weekly owner | Wrong purchase entirely | The measurement was never the bottleneck. The follow-through is. |
| Engine | Cited-answer rate | Runs for 40 cited answers | Budget consequence |
|---|---|---|---|
| Google AI Mode | 97.4% | 42 | Cheapest engine to sample to convergence |
| ChatGPT (web search) | 92.5% | 44 | Roughly parity with AI Mode |
| Gemini | 79.1% | 51 | 21% more runs for the same evidence |
| Runs per topic-engine, per period | Margin of error at a 20% mention rate | What you can honestly claim |
|---|---|---|
| 50 | ±11.1pp | Presence or absence, nothing about trend |
| 100 | ±7.8pp | Direction, if the move is large |
| 200 | ±5.5pp | Meaningful quarter-over-quarter change |
| 400 | ±3.9pp | Board-grade reporting on a topic |
| 900 | ±2.6pp | Small lifts become defensible |
| Situation | Verdict | Why |
|---|---|---|
| You sell into three or more countries and suspect the answers differ | Strong fit | Per-country reporting across 50+ markets is the widest coverage in this price band, and ChatGPT and Perplexity both localize. |
| Agency running several brands on one account | Strong fit | Unlimited seats on every tier plus a bundled GEO audit, so per-brand cost falls as you add brands. |
| Single market, single language, first time measuring | Lite is enough, and you are paying for a feature you will not use | 15 prompts covers a first look. The market engine is the reason to buy Otterly, and you do not need it. |
| You want to test multi-country tracking on the $29 plan | Wrong tier | A 20-prompt buyer set does not fit in 15 prompts. Multi-country starts at Standard, not Lite. |
| Your buyers are on Gemini in Europe | Check availability first | Gemini is a paid add-on and is listed unavailable for country monitoring in 25+ markets. |
| You need click attribution from mention to pipeline | Wrong category | No prompt tracker closes that loop. That is a GA4 and CRM job. |
| Situation | Verdict | Why |
|---|---|---|
| Your buyers cluster on ChatGPT, Perplexity, and one Google surface | Strong fit | The three included engines are the three that matter, and daily refresh with unlimited seats is good value at $95. |
| Agency or in-house team running several brands | Strong fit | Prompts are shared across projects and seats are unlimited, so the per-brand cost falls as you add brands. |
| You sell to developers or researchers | Check Enterprise first | Claude is Enterprise-only and Grok is not offered, so the self-serve product may not see your buyers. |
| You need both Google surfaces plus Copilot | Price the add-ons before comparing | Five engines on Pro is $415 a month, which puts Peec into a different bracket of the market. |
| Nobody has been named as the weekly owner | Wrong purchase entirely | Daily data with no owner is a tab nobody opens. The measurement was never the bottleneck. |
| Question | Third-party AI overview tracker | Search Console generative AI report |
|---|---|---|
| Which surface | AI Overviews only, as the tool defines them | AI Overviews and AI Mode merged |
| Which queries | The set you chose | Every query Google served you on, unnamed |
| Whose session | The tool's crawl locale and profile | Real users, real locations, real accounts |
| What it counts | Appearance and citation on sampled runs | Impressions only |
| Competitors | Visible, with the full cited-source list | Invisible |
| Best use | Diagnosis and competitive source mapping | Reach, and a reality check on the sample |
| Question to ask | Why it decides the purchase |
|---|---|
| How many samples produce each data point? | Published research puts stable rankings at 33 to 94 answers. One weekly sample is not in that range. |
| Which locales and session states do you crawl from? | A single US anonymous profile cannot represent a multi-market buyer base. |
| Do you separate AI Overviews from AI Mode? | The two share 13.7% of citations. A blended number describes neither. |
| Do you report appearance and citation as separate lines? | Only the second one responds to your work. |
| Do you record which model generation produced each run? | The January 27 Gemini 3 default swap dropped AI Overview overlap with top-10 organic from 76% to 38%. |
| Can I export the full cited-source list per query? | Without it you have a scoreboard and no diagnosis. |
| Tier | Price | Engines | Prompts | Responses/mo | Answers per prompt, per engine, per month |
|---|---|---|---|---|---|
| Starter | $99/mo billed yearly | 1 (ChatGPT) | 50 | 1,500 | 30 |
| Growth | $399/mo billed yearly | 3 | 100 | 9,000 | 30 |
| Enterprise | Quoted | Up to 9 | Tailored | Tailored | Whatever you negotiate |
| Situation | Verdict | Why |
|---|---|---|
| Enterprise brand, dedicated owner, nine engines matter | Strong fit | Deepest prompt-volume dataset in the category, plus SSO, SOC 2, and API access at the quoted tier. |
| Consumer or commerce brand tracking ChatGPT Shopping | Strong fit | The shopping surface is barely instrumented anywhere else. |
| B2B team wanting one defensible number per quarter | Negotiate the response allowance | Engine coverage is not your constraint. Answers per prompt is. |
| Series A startup, one marketer, no analyst time | Poor fit | Starter's single engine and 30 answers per prompt will not resolve the differences you would act on. |
| Nobody has been named as the weekly owner | Wrong purchase entirely | The measurement is not the bottleneck. The follow-through is. |
| Week ending | All AI bot hits | Training | General / index | Answer-time | Answer-time share |
|---|---|---|---|---|---|
| Jun 15 | 23,224 | 13,895 | 4,725 | 4,604 | 19.8% |
| Jun 22 | 34,521 | 22,538 | 7,938 | 4,045 | 11.7% |
| Jun 29 | 201,161 | 152,308 | 42,857 | 5,996 | 3.0% |
| Jul 6 | 64,086 | 45,393 | 15,410 | 3,283 | 5.1% |
| Jul 13 | 59,194 | 48,001 | 8,828 | 2,365 | 4.0% |
| Jul 20 | 151,164 | 136,840 | 13,028 | 1,296 | 0.9% |
| Jul 27 | 228,535 | 207,296 | 19,873 | 1,366 | 0.6% |
| Total | 761,885 | 626,271 | 112,659 | 22,955 | 3.0% |
| User agent | Operator | Can it cite you? | What it is actually doing | Our default |
|---|---|---|---|---|
| ChatGPT-User | OpenAI | Yes, directly | Fetches a page the moment a user asks a question | Never block. Highest-value bot on the site. |
| OAI-SearchBot | OpenAI | Yes, via the index | Builds the pool ChatGPT search draws from | Allow. Keep money pages fast and clean. |
| PerplexityBot | Perplexity | Yes | Indexes and fetches for cited answers | Allow, and watch the volume trend. |
| Claude-User | Anthropic | Yes, in principle | User-triggered fetch for Claude web search | Allow. Negligible volume for us so far. |
| GPTBot | OpenAI | No | Training corpus for future models | Allow, with no citation expectation. |
| ClaudeBot | Anthropic | No | Training corpus for Claude | Allow. |
| Meta-ExternalAgent | Meta | No | Training for Llama and Meta AI | Rate-limit. 82% of our volume, zero return. |
| Amazonbot | Amazon | No | Training and general collection | Rate-limit if egress matters. |
| Applebot-Extended | Apple | No | Training opt-in for Apple Intelligence | Policy call, not a visibility call. |
| Google-Extended | No | Not a crawler. A robots.txt token controlling Gemini training use. | Leave alone unless you want out of training. |
| Element | Refresh priority | Why it matters for AI citation |
|---|---|---|
| Key statistics and dates | High | The first thing an engine downgrades when it looks outdated, and the easiest to verify against a rival. |
| Pricing, plans, and product claims | High | A mismatch with reality kills trust in the whole page, not just the wrong line. |
| The leading answer under each heading | Medium | Keeps the passage an engine extracts current and quotable. |
| Examples and screenshots | Medium | Dated examples signal an unmaintained page even when the argument holds. |
| URL and page structure | Low | Changing these breaks existing signals. Leave a working URL alone. |
| Step | What you ship | Why DeepSeek rewards it |
|---|---|---|
| 1. Training presence | Durable, referenced reference pages | Default mode answers from memory, not the live web |
| 2. Answer blocks | 40 to 60 word direct answers | Search mode has a clean passage to lift and quote |
| 3. Crawlable HTML | Clean pages a fetcher can read | Live retrieval can only cite what it fetches |
| 4. Corroboration | Reference-grade earned mentions | Independent sources build the trust DeepSeek weighs |
| 5. Measurement | Weekly DeepSeek-only citation share | You catch losses while they are still fixable |
| Step | What you ship | Why Copilot rewards it |
|---|---|---|
| 1. Bing indexing | Indexed, sitemap-submitted pages | Copilot can only cite what Bing has indexed |
| 2. Answer blocks | 40 to 60 word direct answers | The grounding layer has a clean passage to lift |
| 3. Crawlable HTML | Clean pages Bingbot can parse | The crawler reaches the page and indexes it |
| 4. Editorial citations | Reference-grade earned mentions | Corroboration builds the trust Copilot weighs |
| 5. Measurement | Weekly Copilot-only citation share | You catch losses while they are still fixable |
| Step | What you ship | Why Meta AI rewards it |
|---|---|---|
| 1. Crawler access | Robots.txt that allows Meta-WebIndexer | Meta AI can only cite what its crawler indexed |
| 2. Answer blocks | 40 to 60 word direct answers | The model has a clean passage to lift and attribute |
| 3. Entity clarity | Consistent name, category, and claims | The model resolves you to one trusted source |
| 4. Earned citations | Third-party mentions on trusted pages | Corroboration makes your claim safer to repeat |
| 5. Measurement | Weekly Meta AI-only citation share | You catch losses while they are still fixable |
| Dimension | Entity SEO | Knowledge graph SEO |
|---|---|---|
| Scope | The full practice of being understood as a thing | The specific work on graph records |
| Main asset | A clear, corroborated brand identity | A Wikidata item and a Google entity |
| Core levers | Naming, schema, corroboration, topical consistency | Knowledge-base nodes, sameAs links, fact accuracy |
| Proof it worked | The engine describes your brand correctly | A knowledge panel and a reconciled Wikidata QID |
| Where it fails | Good content, ambiguous brand | Strong brand, missing or messy graph record |
| Spend area | Old allocation | AI-search allocation |
|---|---|---|
| Rank-and-click content and ads | ~80% | ~50% |
| Citation-earning content (answers, comparisons, data) | ~10% | ~25% |
| Earned authority (PR, reviews, expert placement) | ~5% | ~15% |
| AI-visibility measurement and tooling | ~5% | ~10% |
| Metric | What it answers | First-party benchmark |
|---|---|---|
| Share of voice vs competitors | Are we winning the category answer? | The #1 brand averages 76% share of voice in its category |
| Citation share by engine | Where are we strong or invisible? | ChatGPT cites a source in 87% of commercial answers; Gemini is the lowest of the three |
| Trend vs prior editions | Are we gaining or losing ground? | The category leader changes in 24% of consecutive editions |
| Source mix and gaps | Which off-site sources decide our visibility? | Reddit is cited in roughly 22% of answers, about 1 in 5 |
| Pipeline signal | Does any of this reach revenue? | Directional: AI-sourced demos and trials, segmented in analytics |
| Step | What you ship | Why Grok rewards it |
|---|---|---|
| 1. Crawler access | Fetchable, server-rendered pages | You enter Grok's web retrieval pool |
| 2. Live X presence | Active, credible X accounts | You feed Grok's real-time source no one else has |
| 3. Earned corroboration | Reddit and review-site presence | Grok's site: searches find you by default |
| 4. Quotable passages | 40 to 60 word answer blocks | Direct writing survives Grok's quotation style |
| 5. Measurement | Weekly Grok citation share | You catch losses while they are still fixable |
| Question | Standard analytics view | AI attribution view |
|---|---|---|
| Where did the demand start? | Last click before the session | The AI answer that named you |
| What is Direct traffic? | Bookmarks and typed URLs | Partly AI referrals with no referrer |
| How is AI performing? | Near zero, buried in noise | Citation share plus correlated visit lift |
| What proves ROI? | Tracked campaign conversions | Recommendation rate and share of model |
| Step | What you ship | Why Claude rewards it |
|---|---|---|
| 1. Retrieval | Crawlable, clean-HTML pages | Claude can fetch and parse you at all |
| 2. Answer blocks | 40 to 60 word direct answers | The model has a clean passage to quote |
| 3. Evergreen structure | Strong older explainers | Claude cites durable analysis over fresh posts |
| 4. Analytical citations | Reference-grade earned mentions | Corroboration builds the trust Claude weighs |
| 5. Measurement | Weekly Claude-only citation share | You catch losses while they are still fixable |
| Step | What you ship | Why Gemini rewards it |
|---|---|---|
| 1. Indexation | Crawlable, indexed pages | You enter the retrieval set Gemini grounds in |
| 2. Answer blocks | 40 to 60 word direct answers | The model has a clean passage to quote |
| 3. Structured data | FAQ and Article schema | Extraction gets more reliable |
| 4. Earned citations | Third-party mentions and data | Corroboration builds the trust Gemini weighs |
| 5. Measurement | Weekly citation share by surface | You catch losses while they are still fixable |
| Step | What you ship | Why ChatGPT rewards it |
|---|---|---|
| 1. Bing indexation | Crawlable, indexed pages in Bing | You enter the index ChatGPT retrieves from |
| 2. Answer blocks | 40 to 60 word direct answers | The model has a clean passage to quote |
| 3. Query-match language | Buyer-phrased headings and terms | You match the query ChatGPT rewrites toward |
| 4. Earned citations | Third-party mentions and data | Corroboration turns mentions into recommendations |
| 5. Measurement | Weekly citation share tracking | You catch losses while they are still fixable |
| Dimension | Topical authority for SEO | Topical authority for AI search |
|---|---|---|
| Unit of coverage | Keywords in a topic | Sub-questions in a prompt cluster |
| What gets rewarded | The pillar page ranking | Each passage a model can extract |
| Authority signal | Internal links and backlinks | Citations and consistent off-domain mentions |
| Success metric | Rankings and organic clicks | Citation rate and share of voice across prompts |
| Freshness weight | Moderate | High: coverage must be maintained, not archived |
| Tier | Source type | Share of citations | What you actually do about it |
|---|---|---|---|
| Full control | Your websites | 44% | Write clean, current, extractable pages |
| Controllable | Listings and profiles | 42% | Claim and complete every profile |
| Influenceable | Reviews and social | 8% | Prompt reviews, stay active, respond |
| Uncontrollable | News and forums | 6% | Earn mentions through real coverage |
| Component | What it does | How you measure it |
|---|---|---|
| Question map | Defines the buyer questions you intend to be the answer for | Question coverage versus your priority list |
| Answer engineering | Puts a clean, extractable answer at the top of each section | Answer blocks per page; extraction rate |
| Schema markup | Lets engines parse the answer without guessing structure | Valid FAQ, HowTo, and Article coverage |
| Entity clarity | Binds the answer to your brand, not a near-namesake | Entity resolution in answers and knowledge panels |
| Off-page corroboration | Gets your answer echoed in the trusted source pool | Mentions earned in the live source pool |
| Measurement loop | Catches drift and separates citation from mention | Answer share and mention share, week over week |
| Dimension | Keyword SEO | Entity SEO |
|---|---|---|
| Unit of optimization | The page and its target query | The brand, person, or product as a defined entity |
| Core signal | Keyword relevance and backlinks | Consistency, schema, and cross-source corroboration |
| What it earns | A ranking position on a results page | Recognition as a candidate the model can cite |
| Main failure mode | Right page, wrong keyword | Good content, ambiguous brand |
| Why AI cares | Matches the words in a query | Lets the model reason about which brand belongs in the answer |
| Content type | Why AI cites it | Role in the strategy |
|---|---|---|
| Comparison and "vs" pages | Match shortlist prompts and present options side by side | Win the "best option" and "X vs Y" prompts |
| Definition and "what is" pages | Answer the question directly in an extractable passage | Win the informational prompts at the top of the journey |
| Data and statistics pages | Carry numbers a model can quote with a source attached | Become the cited proof other answers lean on |
| How-to and process guides | Map cleanly to step-based answers and HowTo structure | Win the "how do I" prompts buyers ask before a purchase |
| Thin product and landing pages | Carry little extractable substance for a model to lift | Support conversion, but rarely earn the citation alone |
| Move | What it does | How you measure it |
|---|---|---|
| Citation baseline | Shows where AI cites you today versus competitors | Citation share per engine, per prompt |
| Passage engineering | Makes your pages extractable as clean answers | Answer blocks per page; extraction rate |
| Proof density | Gives the model a reason to quote the claim | Claims with a statistic, source, or quote attached |
| Off-page placement | Gets you onto the sources engines actually cite | Mentions earned in the live source pool |
| Technical retrieval | Lets crawlers fetch and parse your pages | Crawlability and render-parity pass rate |
| Weekly tracking | Catches drift before it becomes lost visibility | Week-over-week citation share delta |
| Engine | Answers that cite a source | What that means for your audit |
|---|---|---|
| Google AI Mode | 97.9% | Near-total citation. Almost every answer shows its sources, so your absence here is unambiguous. |
| ChatGPT | 87.4% | High but not total. Roughly one answer in eight gives no source to score against. |
| Gemini | 74.0% | Lowest transparency. One answer in four cites nothing, so mention rate matters more than citation rate here. |
| Component | What it does | How you measure it |
|---|---|---|
| Citation baseline | Establishes where AI cites you today versus competitors | Citation share per engine, per prompt |
| Passage engineering | Makes your pages extractable as clean answers | Answer blocks per page; extraction rate |
| Off-page placement | Gets you onto the sources engines actually cite | Mentions earned in the live source pool |
| Technical retrieval | Ensures crawlers can fetch and parse your pages | Crawlability and render-parity pass rate |
| Weekly tracking | Catches drift before it becomes a quarter of lost visibility | Week-over-week citation share delta |
| Engine | Answers that cite any source | Avg sources per answer | What this tells you |
|---|---|---|---|
| Google AI Mode | 97.9% | 5.2 | Retrieval-grounded by default. Almost always pulls live pages. |
| ChatGPT | 87.4% | 5.2 | Retrieves often, but answers a real share from memory. |
| Gemini | 74.0% | 4.4 | The least source-transparent of the three. One answer in four shows nothing. |