Recro wrote this comparison, so we’re a party to it. We took Ahrefs’ features from its own product pages and press release. Figures on Ahrefs’ pages change as they update; details are current as of October 2026.

How does Ahrefs Brand Radar work?

Ahrefs built Brand Radar on a ready-made database. According to the company’s AI visibility index page, it holds more than 473 million prompts across six AI indexes, each modelled from real user searches in Ahrefs’ keyword database. Without writing a single prompt, a user can immediately see category-level visibility and historical trends for their own brand and competitors.

In January 2026 Ahrefs added custom prompts. The company’s press release describes the difference plainly: Brand Radar’s native tracking focuses on prompts derived from popular, high-volume topics, while custom prompts let teams monitor the long tail, their own buyer questions. Custom prompts come as an add-on, and each check consumes credits per platform, location and update.

Why doesn’t the B2B decision appear on volume lists?

A prompt database modelled from search data captures what people ask often. In consumer categories, that’s very close to a true picture of the market.

In B2B, the decision is made elsewhere. A marine supply firm’s technical manager asks AI: “Which class-approved suppliers can deliver spare parts to Mediterranean ports within 48 hours?” That sentence has no search volume; no volume database models it. Yet that month’s order is placed on the strength of the answer.

In Recro’s simulations, brands appeared in 33% of answers when asked about by name, and in 5.5% of decision questions that didn’t name them. There can be a wide gap between category-level visibility and visibility in the buyer’s decision question. We examined that gap in Found when asked by name, missing when asked by category.

Don’t custom prompts close that gap?

Partly. Custom prompts make it possible to track long-tail questions, and Ahrefs is building tools to help generate prompts. But the question still has to come from the team: who asks, under which conditions, at which stage of the buying journey?

At Recro, answering that is the first pillar of the work. Personas are built with a dedicated persona builder module grounded in an established buyer-insight model. Persona attributes and occasions become questions. The goal isn’t visibility in high-volume questions but presence everywhere a high-intent buyer looks.

Who sees what on the source side?

Ahrefs’ traditional strength is its web index: which site links to whom, where a brand is mentioned. That’s a valuable base for mapping the sites that feed AI answers at scale.

Recro reads sources in the context of the decision question. In one marine simulation, one of the sources that carried a competitor onto the shortlist was the competitor’s company profile on a careers site. In other simulations, catalogues uploaded years ago to document-sharing sites stood out. In link-profile analysis these sources may look trivial; they decide outcomes because they answer the buyer’s question. Details are in signals beyond your website.

Comparison table

Which one fits which situation?

For a team that wants the overall picture of its category, wants to follow competitors’ share of AI answers over time and manage it alongside SEO in one place, Brand Radar is a very strong starting point. The ready-made database delivers a first view without writing a prompt.

When the category picture doesn’t match what the sales team experiences, when the line is “we’re visible in the market but we don’t reach the proposal stage”, Recro comes in. Recro isn’t a software module but an agency service: the brand gets a representative who follows it, and findings are reviewed together in meetings.

How does Recro make this gap visible?

The Recro Insight Model starts where the volume list ends: with the questions the people who drive the brand’s revenue ask, under their own conditions.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

• The persona and question builders bring questions with no search volume, but which decide the outcome, into measurement.
• The report builder shows the source that moved a competitor ahead, in the context of the decision question.
• The action recommendation builder classifies the finding as a Communication, Content or Authority GAP and turns it into a sector-specific action.

Results aren’t templates; they’re specific, local findings for the brand’s sector and market. The infrastructure is optimised regularly for each brand. As GAPs close, progress is tracked in the dashboard and reviewed with the brand’s representative in regular meetings.

We asked the same question about other tools: Recro compared with Semrush AI Visibility Toolkit, Profound, Otterly.AI and Peec AI.

Executive summary

• Ahrefs Brand Radar builds a category-level AI market map from hundreds of millions of prompts modelled from search data.
• The questions that decide B2B deals often have no volume and don’t show up in volume databases.
• Recro generates those questions from persona, occasion and buying intent, and follows the cause and the action through with a representative.

Frequently asked questions

Where do Ahrefs Brand Radar’s prompts come from?

According to Ahrefs, each prompt in the native database is modelled from real user searches in its keyword database. Custom prompts are questions the user adds.

Is Brand Radar meaningful for B2B brands?

For category share and competitor trends, yes. To see zero-volume decision questions that decide the shortlist, you need custom prompts and a buyer map that builds those questions correctly.

Why doesn’t Recro start with high-volume questions?

Because the few decision-makers behind B2B revenue ask questions too specific to show up on volume lists. Recro’s focus isn’t volume but everywhere a high-intent buyer looks.

Why are Recro’s results “local”?

The same category produces different answers in different markets and languages. Recro produces findings for the brand’s target market, language and buying group; it doesn’t use template reports.

Can the two be used together?

Yes. Brand Radar provides the category picture and competitor trends; Recro provides the cause and the action for questions close to the sale.

You show up on the market map, but are you in the answer when the person making the buying decision asks? Recro shows it with personas and decision questions specific to your brand.

Request a demo insight report →

Sources

• Ahrefs · AI visibility index
• Ahrefs · Brand Radar
• Business Wire · Ahrefs launches custom AI prompt tracking for brand visibility (2026)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

Recro wrote this comparison. Information about Peec AI comes from the company’s own announcement and funding coverage; sources are listed at the end. Details are current as of October 2026.

What does Peec AI do?

Berlin-based Peec AI launched its product in February 2025 and raised a $21 million Series A led by Singular in November 2025. According to the company, more than 1,300 brands and agencies adopted the platform within ten months. That fast adoption reflects a simple, clear product.

Peec’s logic is straightforward: the team enters the prompts it wants to track, the platform runs them in ChatGPT, Gemini, Claude, Perplexity and other engines and stores the answers over time. Reporting rests on three measures: visibility (how often the brand appears), position (its ranking) and sentiment (how it’s described). The platform also shows which sources drive impact, and data can be exported via API or into BI dashboards.

The real variable: who writes the prompt?

Every prompt-based tool carries a hidden assumption: the tracked question is the question the buyer asks. In practice, prompts are often written by the marketing team, and the team describes the category in its own language.

We saw the cost of this in Recro’s simulations. A coffee supply brand was mentioned in 8 out of 8 runs for the questions of a tourism and restaurant group’s F&B director persona. The same brand wasn’t mentioned at all in the questions of hotel, café and restaurant chain procurement directors. Had the team written its prompts in the F&B director’s language, the dashboard would have shown “excellent visibility”. The bids the sales team was losing were behind the other door.

In short, the prompt set determines the measurement. If it isn’t clear which person asks, at which stage, under which conditions, the number on the dashboard is an average whose owner is unknown. We unpacked this in Persona GAP Analysis and What is a decision question?

Persona, occasion and buying intent

Recro produces questions from three layers. First, the persona: each role in the buying group is built separately with a dedicated persona builder module, grounded in an established buyer-insight model. Second, the occasion: the same person asks one question during a planned supplier change and another during an urgent need after an audit. Third, buying intent: the goal isn’t to show up in high-volume questions but to be everywhere a buyer close to purchase looks.

These layers surface the questions teams struggle to write on their own. A finance lead’s “what are the cost items beyond the fixed fee for this service?” is one example. In Recro’s reports, the researched brand appeared in only 12% of answers to price and total-cost questions like this. Details are in What does AI say when buyers ask about price?

Comparison table

Which one fits which situation?

For a team that knows its buyers well, has already mapped its decision questions and wants to combine AI visibility with other marketing data in its own BI environment, Peec is a clean, fast and scalable choice.

For a brand without that map, the order reverses. First you need to find out who asks what, on which occasion, and why the brand gets eliminated. Recro does this as an agency: the brand gets a representative, findings are reviewed together in meetings, and progress is tracked in a dashboard as GAPs close. The question map Recro produces can later feed any monitoring tool as well.

How does Recro make this gap visible?

The Recro Insight Model doesn’t leave the weakest link of measurement, the question, to the team’s guesswork. Questions are derived from the brand’s real buyers.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

• The persona builder builds each role in the buying group separately, so each door is measured on its own rather than as one “target audience” average.
• The question builder turns persona attributes and occasions into decision questions that don’t name the brand.
• The report and action recommendation builders tie each finding to a GAP type and a prioritised action.

Outputs aren’t templates; they are specific, local results for the brand’s sector, market and buying group. The infrastructure is optimised regularly for each brand, and the brand’s representative tracks progress with you.

We asked the same question about other tools: Recro compared with Semrush AI Visibility Toolkit, Profound, Otterly.AI and Ahrefs Brand Radar.

Executive summary

• Peec AI is a fast-growing analytics platform that tracks the prompts teams enter across many AI engines.
• Prompt-based measurement is only as good as the prompt set; questions written in the wrong person’s language can look misleadingly good.
• Recro generates questions from persona, occasion and buying intent, turns findings into action and follows through with a representative.

Frequently asked questions

Which metrics does Peec AI report?

According to the company: visibility, position and sentiment, plus which sources drive impact. Data can be exported through API and BI integrations.

Can’t you enter persona-based prompts in Peec?

You can; a role and conditions can be written into the prompt. The hard part is knowing which persona asks which question on which occasion. In Recro, that is the work itself.

Can Recro’s question map be used in another tool?

The decision questions Recro produces are a strategic asset for the brand; it can use them in its content plan and its own measurement tools.

Why is Recro an agency service rather than software?

Because the value shows up in interpreting the measurement and turning it into action. Each brand gets a representative, findings are reviewed in meetings and progress is tracked in a dashboard.

Which suits a small B2B brand better?

If you know your decision questions and have time to read a dashboard, Peec. If you don’t know why you drop off the shortlist, Recro first.

Are the prompts you track the questions your buyers actually ask? Recro builds the questions from personas and occasions specific to your brand.

Request a demo insight report →

Sources

• Peec AI · We raised $21M Series A to help brands win in AI search (2025)
• Tech.eu · Peec AI raises $21M Series A (2025)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

Recro wrote this comparison; we’re a party to it. We took Otterly.AI’s features from the company’s help centre and independent software directories. Details are current as of October 2026.

A marketing team’s first step into AI visibility is often a monitoring tool. That makes sense: you can’t manage what you don’t measure. Three months later, the question is usually the same: “The dashboard says our visibility is low. Now what?”

What does Otterly.AI do?

According to Otterly.AI’s help centre, the tool tracks brand and content visibility across six AI search engines: Google AI Overviews, ChatGPT, Perplexity, Google AI Mode, Gemini and Microsoft Copilot. Users choose the prompts to track, and a prompt research feature turns keywords into trackable prompts.

Reports cover brand mentions, citation links to the site, average position, sentiment and share of voice against competitors. Country-level monitoring and a content/GEO audit module are also available; the audit scores how accessible a page is to AI crawlers and how ready its content is. The tool is listed in Semrush’s app centre too. Pricing tiers start at a level small teams can afford, which is one of Otterly’s real strengths.

The dashboard shows the data. Who does the interpretation?

A monitoring tool shows what’s in the answer. Explaining why the answer looks that way, and what to do about it, is left to the team. For a large SEO team, that’s fine. In B2B, AI visibility often gets squeezed in among the other ten jobs of a single marketing manager.

That’s why Recro’s operating model is different. Recro isn’t software but an agency service: each brand gets a representative who follows it, and findings are reviewed together in regular meetings. There is a dashboard too, a tracking system that follows progress once GAPs are found. The difference is that the dashboard is the end of the work, not the start.

Is what AI says actually true?

Monitoring tools record the answer. Checking whether it’s accurate is a separate job. A 2025 study by the European Broadcasting Union and the BBC found at least one significant issue in 45% of AI assistants’ news answers.

In B2B the problem is subtler. In Recro’s simulations, the model said “no public information is available” in 27 answers and immediately filled the gap with a sector generalisation: “large manufacturers may be more competitive”, “boutique firms may be more flexible”. A dashboard logs that answer as “brand not mentioned”. The real finding is that, at the buyer’s most critical question, the model’s guess replaces the brand’s own word. We examined this pattern in “No public information available”.

Comparison table

Is a GEO audit the same as a GAP analysis?

No. A GEO audit looks at a page’s technical readiness: crawler access, content structure, readability. It’s a necessary hygiene check; a page with technical barriers struggles to get into AI answers. We explained the details in How do LLMs read your website?

A technically flawless site can still drop off the shortlist. The most frequent gaps in Recro’s reports weren’t technical: price logic never published, proof living only on the brand’s own site, the brand missing from sector lists where competitors appear. That’s why GAP analysis takes the buyer’s decision as its reference, not the website.

Which one fits which situation?

For a team measuring AI visibility for the first time, able to build its own prompt list, read a weekly dashboard and turn it into tasks for the content team, Otterly is a low-risk start. On a tight budget, a more sensible first step is hard to find.

Recro is for brands that want to go beyond the dashboard: clarity on who the buyer is, which question they ask on which occasion and why the brand gets eliminated; prioritised actions; and someone who follows through. It’s not a software licence but a team that watches your brand.

How does Recro make this gap visible?

The Recro Insight Model doesn’t start with monitoring; it starts by building the brand’s buyer. The dashboard exists to track progress after the findings and actions.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

• The persona and question builders don’t leave the prompt list to the team’s guesswork; they produce the questions that personas, built with a dedicated module grounded in an established buyer-insight model, ask on their occasions.
• The report builder writes down the reason behind a “not mentioned” entry and puts key claims through human review.
• The action recommendation builder classifies the finding as a Communication, Content or Authority GAP; every action has a clear target.

Results aren’t templates; they’re specific, local findings for the brand’s sector and market. The infrastructure is optimised regularly for each brand. As GAPs close, progress is tracked in the dashboard and the brand’s representative reviews results with you in meetings.

We asked the same question about other tools: Recro compared with Semrush AI Visibility Toolkit, Profound, Peec AI and Ahrefs Brand Radar.

Executive summary

• Otterly.AI is an affordable self-serve tool that tracks mentions and citations across six AI search engines.
• Recro is an agency service that simulates buyers by persona and occasion, verifies findings, turns them into action and follows through with a representative.
• The deciding question: do you have the capacity to read the dashboard and turn it into decisions?

Frequently asked questions

Which AI tools does Otterly.AI monitor?

According to its help centre: Google AI Overviews, ChatGPT, Perplexity, Google AI Mode, Gemini and Microsoft Copilot. Coverage may change, so check Otterly’s website for the current list.

Does Recro have a dashboard too?

Yes. Once GAPs are found, a tracking system and dashboard follow progress. The dashboard measures improvement after the simulation and action plan.

Is a GEO audit enough for AI visibility?

Technical readiness is necessary but not sufficient. A technically sound site can still drop off the shortlist because price information or third-party proof is missing.

Does it make sense to start with Otterly and move to Recro?

It can. Monitoring data can serve as starting context for a Recro engagement. Recro’s measurement, however, is rebuilt from the brand’s own personas and decision questions.

What does Recro expect from our team?

A counterpart who knows the market, the buyers and the sales process. Recro runs the simulation, reporting and tracking; the brand’s representative reviews findings with you in regular meetings.

What’s behind the low visibility on your dashboard, and which gap should close first? Recro shows it with personas and decision questions specific to your brand.

Request a demo insight report →

Sources

• Otterly.AI Help Centre · Which AI searches does OtterlyAI support?
• Semrush Knowledge Base · Otterly – AI Search Monitoring
• GetApp · Otterly.AI
• EBU · News Integrity in AI Assistants (EBU & BBC, 2025)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

We’re Recro, so we’re a party to this comparison. Information about Profound comes from the company’s own announcements and funding coverage; sources are listed at the end. The product is evolving quickly; details are current as of October 2026.

What is Profound, and who is it built for?

Profound is the most heavily funded company in AI search visibility. It raised a $20 million Series A led by Kleiner Perkins in June 2025 and a $35 million Series B led by Sequoia in August 2025. In September 2026 it reached a $1.8 billion valuation with a $180 million Series D co-led by Sequoia and Kleiner Perkins. The company says it serves more than 1,000 enterprise brands.

Its 2025 announcements described three core products. Answer Engine Insights shows how chatbots describe a brand, its products and its industry. Agent Analytics reveals how AI bots crawl and interpret a website. Conversation Explorer surfaces rising topics and question volume in AI search. In 2026 the company is expanding into an agent layer, “AI Marketer”, that researches, writes content and reports on results.

That picture tells a clear positioning story: Profound is built for companies with large marketing teams, continuous content production and a need to show up across thousands of queries.

Scale or depth?

Conversation Explorer’s value comes from volume: what do people ask AI most? For a consumer brand, that’s the compass for the content calendar.

Volume works differently in B2B. An industrial water treatment firm’s annual revenue may depend on the environmental managers, procurement managers and finance leads of perhaps 40 factories. Their questions are too few to show up on a volume list. Each one, though, is a contract.

That’s Recro’s starting point: not the noise of the market, but everywhere a high-intent buyer looks. Persona attributes, decision stage and occasions are modelled together. A hotel chain switching suppliers before a new season and a factory looking for an urgent fix after an audit are different occasions, and they make people ask AI different questions.

Why can’t you trust a single answer?

Both approaches accept one fact: AI answers vary. In a January 2026 study by SparkToro and Gumshoe.ai, two answers to the same question contained the same list of brands less than once in 100 times.

Recro’s simulations showed the same: in the coffee supply category, 236 runs produced 50 different “dominant competitor” names. Profound manages this variance with large samples. Recro reads it by persona and occasion: which competitor is persistent in which person’s questions, and which appears by chance? For a sales team, the second distinction is the one that matters.

Comparison table

Why is content production the second step?

Profound’s move into an agent layer shows where the sector is heading: the tool that measures visibility also wants to produce the content. For speed, that’s appealing.

In B2B, the problem is often not too little content but content in the wrong place. None of Recro’s 73 reports found a price or ROI signal for the brand being researched. In many reports, the brand’s proof lived only on its own website, while what carried the competitor onto the shortlist was a sector directory, a careers-site profile or a press story. More blog posts don’t close that gap. First you need to know which gap, in which person’s question, in which source. We covered this in signals beyond your website.

Which one fits which situation?

For a company selling across hundreds of product categories, in multiple countries, to a broad audience, with a large marketing team that will manage AI visibility alongside content operations, Profound is a strong platform.

For a B2B brand whose buying group is a handful of people, whose sales cycle runs for months and which wants to know why each lost bid was lost, Recro is the sharper choice. With Recro you don’t buy a software licence; you get an agency service run by a representative who follows your brand, with regular meetings.

How does Recro make this gap visible?

The Recro Insight Model doesn’t simulate the broad market. It simulates the few critical decision-makers who drive the brand’s revenue, all year round, and shows where the brand loses in their decisions.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

• The persona builder is a dedicated module grounded in an established buyer-insight model; it builds each role in the buying group separately.
• The question builder generates the questions that never appear on volume lists but decide the contract, from persona attributes and occasions.
• The report and action recommendation builders say which gap closes in which source, instead of “more content”: a Communication, Content or Authority GAP.

Results aren’t templates but specific findings for the brand’s sector and market. The infrastructure is optimised regularly for each brand; as GAPs close, progress is tracked in a dashboard and reviewed with the brand’s representative in regular meetings.

We asked the same question about other tools: Recro compared with Semrush AI Visibility Toolkit, Otterly.AI, Peec AI and Ahrefs Brand Radar.

Executive summary

• Profound is a well-funded enterprise platform built for market-scale visibility and content operations.
• Recro is an agency service that simulates the few decision-makers behind a B2B brand’s revenue, by persona and occasion.
• For brands selling to broad audiences, scale decides; for brands selling to small buying groups, depth does.

Frequently asked questions

What is the core difference between Profound and Recro?

Profound is an enterprise software platform; Recro is an insight agency. Profound tracks visibility at market scale; Recro simulates the people making the buying decision and explains why the decision was lost.

Isn’t Profound suitable for B2B brands?

It can be, especially for large B2B companies with big marketing teams. What matters is whether the brand’s question is about volume or about the behaviour of specific decision-makers.

Do you buy a software licence with Recro?

No. Recro works as an agency service. The brand gets a representative, results are reviewed together in meetings and progress is tracked in a dashboard.

Why should occasions be part of measurement?

The same persona asks different questions in different situations. An urgent need after an audit and a planned supplier change are different decision questions; if one isn’t measured, the brand’s visibility in that moment is unknown.

Can the two approaches be used together?

Yes. A large company can use Profound for broad visibility and Recro for the decision questions of key accounts. They answer different leadership questions.

What do the few decision-makers behind your revenue ask AI, and why does your competitor appear in the answer? Recro simulates it with personas and occasions specific to your brand.

Request a demo insight report →

Sources

• Profound · Profound raises $180M Series D at $1.8B valuation (2026)
• Built In NYC · AI marketing insights platform Profound raises $20M Series A (2025)
• Pulse 2.0 · Profound: $35 million Series B (2025)
• Search Engine Land · AI recommendation lists repeat less than 1% of the time (SparkToro, Gumshoe.ai)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

Recro wrote this comparison, so let’s be clear that we’re a party to it. We took Semrush’s features from its own knowledge base and independent reviews, and list the sources at the end. Tool features change quickly; everything here reflects public sources as of October 2026.

Put the two products side by side and the first difference isn’t in the feature list. It’s on the first screen. Semrush asks: “Which prompts do you want to track?” Recro first asks: “Who makes the buying decision?”

What does the Semrush AI Visibility Toolkit do?

Semrush added an AI visibility module to its SEO suite in 2025 and folded it into Semrush One plans in October 2025. In April 2026 Adobe completed its acquisition of Semrush, so the product now sits alongside Adobe’s customer experience portfolio.

According to the Semrush knowledge base, the toolkit has two core parts. Prompt tracking follows a brand’s visibility in ChatGPT and Google AI Mode for a chosen set of high-value prompts, updated daily. Brand performance reports update weekly and show a visibility score, share of voice, mentions, average position, sentiment and cited sources. Independent reviews describe coverage that also includes Gemini, Google AI Overviews and Perplexity. The suite also offers an “AI readiness” audit that flags on-page issues blocking AI crawlers.

One reviewer notes that the reporting leans towards Google. That’s not surprising: the product is part of an ecosystem built on Google search data.

Where does the difference start? Where the prompt comes from

Semrush’s strength is keyword data. Tracked prompts naturally come from that world: topics with search volume, category phrases, clusters the brand already tracks for SEO. For a marketing team, that’s familiar and efficient.

In B2B buying, though, the questions that decide the outcome rarely have search volume. A quality manager asks AI: “Which packaging manufacturers are food-contact compliant, BRC certified and able to deliver in four weeks?” That sentence has no volume in a keyword tool. It’s also the sentence that builds the shortlist.

Recro’s simulations put a number on this gap. Across 460 question-answer runs for 6 brands between April and September 2026, brands appeared in 33% of answers when asked about by name, and in 5.5% of decision questions that didn’t mention them. A prompt set written in the brand’s or the category’s own language produces a more optimistic picture than the buyer actually sees. We shared this finding in detail in Before you buy AI visibility work.

Whose behaviour is being measured?

The Semrush report gives the brand’s category-level visibility: one number, one share of voice. Enterprise buying involves several people asking different questions. The procurement manager asks about price and lead time, the quality manager about certification, the finance lead about total cost.

In a simulation we built for a coffee supply brand, the brand was mentioned in 8 out of 8 runs for the F&B director’s questions, and in none for the hotel chain procurement director’s questions. Averaged, that reads as “moderate visibility”. In reality, one door was wide open and the other fully shut. We covered this in Persona GAP Analysis.

Comparison table

A point to check for markets outside the core list

According to the current list in the Semrush knowledge base, prompt tracking is offered for the US, UK, Canada, Australia, Spain and India. Brands selling into other markets, Turkey included, should confirm coverage before buying if they want to measure the answer a local buyer actually sees. We looked at why the same brand appears differently in different languages in Turkish vs English prompts.

Which one fits which situation?

For a team that already uses Semrush, wants SEO and AI visibility in one dashboard, and is asking “how well do our content clusters carry into AI answers?”, the toolkit is a sensible choice. No extra vendor, and it speaks the same language as existing keyword work.

When the question is “why did buyers eliminate us?”, the picture changes. A score doesn’t answer that; you need to show which person chose the competitor, under which condition, based on which source. Recro was built for that need. Its goal isn’t visibility on high-volume topics but presence everywhere a high-intent buyer looks.

There’s also a difference in operating model. Semrush is software; your team owns setup, the prompt list and interpretation. Recro is an agency service: the brand gets a dedicated representative who follows it, and findings are reviewed together in meetings.

The two aren’t mutually exclusive. Some teams use Semrush for category trends and Recro for questions close to the sales decision. What to avoid is presenting a category-level score to the board as “buyer behaviour”.

How does Recro make this gap visible?

The Recro Insight Model starts measurement with the key person who intends to buy, not with a prompt. The result isn’t a score but an insight report showing in which decision questions the brand falls behind and why, plus the action list to close the gap.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

• The persona and question builders replace Semrush’s prompt list with the buyer’s own sentence. Personas are built with a dedicated persona builder module grounded in an established buyer-insight model, and persona attributes and occasions carry into the questions. Questions with no search volume, but which decide the shortlist, enter the measurement.
• The report builder replaces a single category score with a separate view for each persona and decision stage, and shows which source moved the competitor ahead.
• The action recommendation builder classifies each finding as a Communication, Content or Authority GAP and turns it into a sector-specific action.

Outputs aren’t templates; they are specific, local results shaped by the brand’s sector, market and buying group. The simulation infrastructure is optimised regularly for each brand. Once GAPs are identified, progress is followed through a tracking system and dashboard, and the brand’s representative reviews results with you in regular meetings.

We asked the same question about other tools: Recro compared with Profound, Otterly.AI, Peec AI and Ahrefs Brand Radar.

Executive summary

• The Semrush AI Visibility Toolkit is the SEO ecosystem’s arm into AI: it tracks visibility for prompts aligned with keyword data.
• Recro explains why a brand is recommended or eliminated in buyers’ persona-based decision questions and turns that into action. It works as an agency: representative, meetings and a progress dashboard.
• Semrush for category trends, Recro for questions close to the sale; they don’t answer the same question.

Frequently asked questions

Which AI tools does the Semrush AI Visibility Toolkit cover?

The Semrush knowledge base lists ChatGPT and Google AI Mode for prompt tracking; independent reviews also mention Gemini, Google AI Overviews and Perplexity. Coverage changes, so check Semrush’s product page for the current list.

Does Recro replace Semrush?

No. Semrush is an SEO and visibility platform; Recro is a decision simulation and insight model. A brand that keeps Semrush for SEO can work with Recro on questions close to the buying decision.

Can you define personas in Semrush?

You can write a role and conditions into the prompt text. The difference is whether the persona sits at the centre of measurement or as a detail inside a prompt. In Recro, every finding is tied to a persona and a decision stage.

Which is better for non-English or local markets?

Check Semrush’s current country list for your market. Recro builds decision questions in the language and context of the brand’s target market, including Turkish and English.

Does it make sense to use both?

For most B2B brands, yes. Semrush shows broad category trends; Recro shows why the sales team loses specific questions. If the budget covers only one, choose based on the question leadership is asking.

Which questions do your buyers ask AI, and why does your competitor show up in the answer? Recro builds measurement on personas and decision questions specific to your brand.

Request a demo insight report →

Sources

• Semrush Knowledge Base · AI Visibility Toolkit
• Semrush · FAQ for customers: Adobe acquires Semrush
• TechRadar · Semrush review
• Exploding Topics · Semrush AI Visibility Toolkit vs SE Ranking AI Search Toolkit

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

Seeing a high number in AI visibility measurement is reassuring. For a corporate services brand in our simulations, total visibility measured between 63% and 81% across periods. The brand was strong in category questions, stood apart from competitors and its value proposition was described correctly.

The “gaps” section of the reports said something else. The brand didn’t appear in strategic analyst reports. It stayed silent in trust queries. Not enough proof was found for its expertise in one particular sector. And against a competitor’s editorial authority, its independent endorsement was weak.

Its own site was the most-cited domain

When we counted the answers with visible source links by domain, this brand’s own site came first by a wide margin: 36 citations across 10 runs. At first glance that’s a success signal. The site’s content is strong, and the model can read and quote it.

But the same picture also shows a dependency. The brand’s representation in AI rests largely on one source, and that source is the brand’s own word. The model knows what the brand says; it can’t find what others say about the brand.

What happens at the validation stage?

In B2B buying, the tone of questions changes once the shortlist forms. A buyer who asked “Who’s out there?” in discovery asks things like this in validation:

• “Is there independent proof that this firm works with large corporate clients?”
• “What are the risks of working with this firm?”
• “What do industry analysts or publications say about this firm?”
• “Has this firm worked in our sector before?”

The brand’s own site can’t answer these questions, because the answer’s value comes from its independence. Here the model looks for third-party sources; when it doesn’t find them, it either stays silent or turns to the competitor with more outside proof.

The key person asking changes at this stage too. A marketing director may ask in discovery; in validation, procurement, finance or information security step in. These people look at a brand’s independent endorsement more than its claims.

Three risks of one-legged visibility

1. Losses in validation questions. A place won in discovery can be lost in the final elimination. As the buyer narrows the shortlist, they choose the competitor with independent proof.

2. Fragility. Visibility can drop quickly when the site is redesigned, when crawler access is blocked without anyone noticing, or when the model gives more weight to independent sources. Representation that rests on one source is affected by everything that happens to that source.

3. No recognition by description. For the same brand, the model never described it by its attributes without its name. The brand’s own site carries its name, but others need to describe its place in the category. We covered this pattern in detail in found by name, missing by category.

How do you move proof beyond your site?

1. List the validation questions. Write the trust, risk and sector-experience questions a shortlisted buyer asks in the final elimination.
2. Target an independent source for each question. A case story in an industry publication, a review on a customer review platform, a talk at an association or event, a mention in an analyst or consulting report.
3. Take your strongest site content outside. A guide or case that works well on your site can become an independent voice as a guest article or interview in an industry publication.
4. Make expert voices visible. Regular visibility for founders and executives on LinkedIn, in industry publications and at events is the most readable proof outside the brand’s site. In Edelman and LinkedIn’s 2024 research, 73% of decision-makers said thought leadership is a more trustworthy basis for assessing an organization’s capabilities than its marketing materials.
5. Build sector-level proof. For every sector where you claim expertise, at least one independent source should carry a concrete example.

We covered which off-site sources carry signals in your website is no longer your only signal source, and the role of LinkedIn and the press in LinkedIn, press and third-party sources.

How does Recro make this gap visible?

The Recro Insight Model reads the source distribution behind a high visibility number and the brand’s standing in validation questions separately. A strong brand’s weakness becomes visible instead of disappearing inside the number.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

For this weakness, each pillar produces the following:

• The persona builder sets up the key people with a say in validation: procurement, finance and information security.
• The question builder produces the trust, risk and sector-experience questions that follow discovery, in the natural flow of the buying journey.
• The report builder reads the links that source the conversation by domain and shows how much of the brand’s representation rests on its own site.
• The action recommendation builder prioritizes which independent source to target for which validation question.

From a Recro simulation · anonymized

• Brand: Corporate services brand · Turkish
• Visibility: Total 63–81%; named in 75–88% of answers.
• Sources: Its own site was the most-cited domain in the dataset (36 citations across 10 runs).
• Gaps: Absent from analyst reports; silent in trust queries; insufficient proof of expertise in one sector.
• Class: Authority GAP
• Action: Independent sources that match validation questions: industry publications, review platforms, expert visibility.

Executive summary

• The most visible brand in Recro’s simulations was named in 75–88% of answers, yet went silent in trust and validation questions.
• The brand’s own site was the most-cited domain in the dataset; visibility rested on one source, the brand’s own word.
• One-legged visibility carries risks of losses in validation, fragility and no recognition by description. Proof should move to an independent source for each validation question.

Frequently asked questions

Is it bad that AI quotes our site so often?

No, it’s a strong foundation. The risk lies in representation resting on that source alone. Visibility not backed by independent sources stays weak in validation questions.

At which stage are validation questions asked?

After the shortlist forms, in the final elimination. In our simulations, these questions centered on trust, risk, analyst views and sector experience.

How do you get into analyst reports?

Alongside major analyst firms, market reports from industry associations, publications and consultancies are also used as sources. Offering verifiable data to the people who prepare them is the first step.

Why prioritize this when our visibility is already high?

Because a high number can hide that losses happen in the final elimination. For a brand that makes shortlists but doesn’t win, the fastest gains are at the validation stage.

What data are these findings based on?

Recro simulations for 6 brands between April and September 2026: 460 question-answer runs and 73 reports. The data is anonymized.

How many sources does your brand’s AI visibility rest on? Recro builds a sample simulation with validation-stage key people and shares the result in a demo report.

Request a demo insight report →

Sources

• Recro simulation data · April–September 2026 · 6 brands, 460 question-answer runs, 73 reports (internal, anonymized; run on a single AI model)
• Edelman and LinkedIn · 2024 B2B Thought Leadership Impact Report

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

Picture the marketing director of an IT and systems integration company. Organic growth has slowed, competitors are moving faster in B2B marketing. They want to work with a new agency, but a past agency switch went badly. The sales director is wary of outside agencies, and the finance team is strict about budget.

This person was one of the key people in a simulation Recro built for a marketing agency. None of the eight questions they asked AI was “Which is the most creative agency?”

Which questions did the marketing director ask?

When we summarized the questions of the simulation’s two marketing-leader personas by decision stage, this picture emerged:

Almost every question is about risk, proof and internal defense. Creative capability is taken as a precondition; what decides is how the agency reduces risk.

What did the simulation of the never-mentioned agency show?

The agency we researched wasn’t mentioned in any of 32 runs. The model recognized it neither by name nor by description. In the same period, four different agencies stood out as the dominant competitor on different days and for different personas. The reports’ shared diagnosis:

• Editorial lists decided discovery. Competitors appeared in lists prepared by industry magazines and agency directories. The source links included marketing publications, an international agency directory and an executive community site. Ahrefs’ research on ChatGPT sources shows the same pattern: “best X” lists make up 43.8% of cited pages, and 80.9% of those lists are third-party publications.
• Case studies with numbers won comparison. Competitors’ concrete success stories were quoted as proof of their “expertise” claims.
• Transparency won the decision. Agencies that clearly described their reporting model and cost structure pulled ahead in “transparent reporting” and “profitability model” questions.

The distance between agency language and buyer language

Agency websites usually speak through portfolio, creative approach and service lists. The questions a marketing director asks AI use a different vocabulary: payback period, out-of-scope work, data liability, pilot project, service-level commitment, internal objection.

This is one of the clearest examples of the difference between a keyword and a key person. An agency ranking well for “B2B digital agency” may answer none of the eight questions of the marketing director behind that keyword. AI, meanwhile, surfaces the sources that can answer those eight questions.

How can agencies answer decision questions?

1. Publish sector-specific cases. Without the client’s name if needed: sector, company size, starting point, work done, measurable outcome and timeline.
2. Describe the engagement and cost model. State clearly what the retainer covers, what’s out of scope and how extra costs arise.
3. Offer a pilot option. Define its length, scope and success measure.
4. Write out your data and compliance process. Explain your role under data protection law, your CRM integration approach and your service-level commitments.
5. Get into editorial lists and directories. Map which lists are cited in discovery.
6. Give the marketing director material for internal defense. An in-house vs. agency comparison, a first-90-days plan, the impact on the sales team.

We covered how price and cost questions are answered in general in what AI says when buyers ask about price, and the shortlist that changes by person in from keywords to key people.

How does Recro make this gap visible?

The Recro Insight Model measures an agency’s visibility through the decision journey of the marketing leader who will choose it. It shows at which stage and in which question the agency drops, and which proof puts the competitor ahead.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

For agency selection, each pillar produces the following:

• The persona builder sets up the person who will choose the agency, with their past experience, internal pressures and sector.
• The question builder produces this person’s questions from problem to decision, in their language and natural flow.
• The report builder reads the agency’s standing at each stage, the dominant competitor and the links that source the conversation.
• The action recommendation builder sets out which equivalent content to produce against standout competitor content and which lists to enter.

From a Recro simulation · anonymized

• Persona: IT company marketing director and corporate deputy marketing GM · Turkish
• Question: An eight-stage agency-selection journey from problem to decision
• Inclusion: Not mentioned in any of 32 runs; four different competitors dominated.
• Competitors’ proof: Editorial agency lists, case studies with numbers, transparent reporting model.
• Class: Authority GAP + Content GAP
• Action: Sector-specific anonymized cases; cost and scope model; pilot definition; entry into agency lists.

Executive summary

• A marketing director choosing an agency asks AI about risk: the cost of a wrong choice, cases, payback, hidden costs, data compliance, pilots and internal defense.
• In a Recro simulation, an agency wasn’t mentioned in any of 32 answers; competitors were recommended through editorial lists, cases with numbers and a transparent model.
• Until the gap between agencies’ portfolio language and buyers’ risk language closes, ranking for a keyword doesn’t mean appearing in the decision question.

Frequently asked questions

Is the creative portfolio no longer important?

It matters, as a precondition. In our simulations, decision questions centered on risk, proof and transparency more than portfolio.

How does an agency that can’t name clients share cases?

Anonymized cases describing sector, company size, starting point, work done and measurable outcome are strong proof even without the client’s name.

Which agency lists matter?

The ones cited in discovery questions in your sector. In our simulation, marketing publications’ lists and international agency directories stood out; measurement sets the order.

Who asks questions besides the marketing director?

The sales director, finance and legal or IT. In the simulation, the marketing director’s questions already anticipated these people’s objections.

What data are these findings based on?

A Recro simulation built for a marketing agency, within 460 question-answer runs between April and September 2026. The data is anonymized.

When the marketing director who will choose you asks AI, is your agency in the answer? Recro builds a sample simulation with your buyer’s decision journey and shares the result in a demo report.

Request a demo insight report →

Sources

• Recro simulation data · April–September 2026 · 6 brands, 460 question-answer runs, 73 reports (internal, anonymized; run on a single AI model)
• Ahrefs · Do Self-Promotional “Best” Lists Boost ChatGPT Visibility?

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

For a coffee supplier’s marketing team, the target keywords are obvious: “corporate coffee supplier”, “HORECA coffee”, “wholesale coffee”. A site that ranks well for them looks successful in reports. But who is typing them?

A hotel chain’s F&B director thinks about guest experience, brand perception and barista training. A café chain’s procurement director looks at minimum orders, price stability and delivery frequency. A retail chain’s category director asks about private-label production capacity. All three are searching the same category. Each asks AI a different question, gets a different answer and sees a different shortlist.

How did the same brand change by person in our simulations?

In a simulation Recro built for a coffee supply brand, questions asked on behalf of different decision-makers produced this picture:

Same brand, same category, same period. Results range from 100% to 0%, and each person meets a different type of competitor. Across the simulation, 236 runs produced 50 different “dominant competitor” names.

The pattern repeated in other sectors. In a marine equipment simulation, the brand appeared in 25% of answers for some personas and 0% for others; the dominant competitor was a domestic manufacturer for one persona and a global player for another. In an enterprise software category, 112 runs produced 28 different dominant competitors.

What does a keyword show, and what does a key person show?

A keyword is a phrase typed into a search engine. It measures how well a site answers that phrase, and it still matters for SEO.

A key person is the decision-maker with buying intent: their role, sector, company size, the situation that triggers the question and their acceptance threshold. In the AI era, buyers don’t type keywords; they describe their situation: “We’re a café chain with a hundred branches, we want protection from price swings; which specialty roasters offer flexible minimum orders?” The model weighs each condition in that sentence and builds the shortlist for that person.

Same category, two key people
Hotel chain F&B director: “Which corporate coffee suppliers offer barista training and equipment support that would raise the guest experience?”
Café chain procurement director: “Which suppliers offer annual price locking against green-bean price swings and flexible minimum orders?”

Both questions sit behind the keyword “corporate coffee supplier”. In the first, proof of experience and service wins; in the second, proof of pricing policy and flexibility.

Why isn’t a single “target audience” enough?

In B2B, decisions aren’t made by one person. According to 6sense, the average buying group has 11 people. According to Gartner, buyers spend only 17% of their buying time meeting suppliers. Each member of the group asks their own question, and AI offers each of them a different shortlist.

A definition like “target audience: hotels, restaurants and cafés” erases this difference. The insight that a brand is strong with F&B directors and invisible to procurement directors only surfaces when each key person is measured separately. We explained how this spread is analyzed in Persona GAP analysis.

How do you build a key-person map?

1. List the deals you won and lost in the last two years. Who was in each decision?
2. Write four things for each role. The situation that triggers the question, the condition they look for in the answer, their acceptance threshold and their veto point.
3. Write each role’s question in their own words. Without your brand’s category names.
4. Measure each role separately. Brand mentions, the dominant competitor and the rationale should be read for each person.
5. Build content per person. Proof of experience for the F&B director, pricing policy for procurement, risk management for finance.

We covered how decision questions are mapped in what is a decision question.

How does Recro make this gap visible?

The Recro Insight Model starts measurement with the key person with buying intent. Which people the brand is strong with, which it’s invisible to and which competitor each person meets appear in the same report.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

In the key-person approach, each pillar produces the following:

• The persona builder defines the brand’s real buyer structure by role, sector, scale and triggering situation. Single persona–many questions, many personas–single question and many personas–many questions setups cover the whole buying group.
• The question builder produces each person’s question with their conditions, in a natural buying-journey flow.
• The report builder reads brand mentions, the dominant competitor and the rationale per person, and assesses KPIs set against the brand’s own goals per person.
• The action recommendation builder ranks which proof and content matter most for the key people where the brand is weak.

From a Recro simulation · anonymized

• Brand: Coffee supply brand · HORECA and retail · Turkish
• Strong with: Tourism group F&B director and retail category director: 8/8.
• Invisible to: Hotel, café and restaurant chain procurement directors: 0.
• Pattern: 50 different dominant competitors across 236 runs; the competitor type changes by person.
• Class: Persona GAP
• Action: Produce proof content on the pricing policy, minimum order and delivery topics procurement directors ask about.

Executive summary

• The AI answer is shaped far more by the asker’s role and conditions than by the keyword in the question.
• In a Recro simulation, the same brand was mentioned 8/8 for two decision-makers and 0 for three; 236 runs produced 50 different dominant competitors.
• Measurement and content should be built around key people with buying intent: each role’s question, conditions and competitor read separately.

Frequently asked questions

Is keyword work unnecessary now?

No; it shows how a site performs in search engines. But it doesn’t show which decision-maker an AI answer recommends the brand to. Use both.

Is a key person the same as a persona?

A persona is the modeled form of a key person. The key person is the real decision-maker with buying intent; the persona makes their role, conditions and question measurable.

How many key people should we define?

At least one for every critical role in the buying group. In our simulations, when the role changed, both visibility and the dominant competitor changed.

Doesn’t such a volatile dominant competitor make measurement meaningless?

It does if you look at a single answer. With repeated measurement, which competitor type stands out for which person appears as a stable pattern.

What data are these findings based on?

Recro simulations for 6 brands between April and September 2026, with 460 question-answer runs. The data is anonymized and shows observed patterns.

Which key people is your brand strong with, and which is it invisible to? Recro builds a sample simulation for each role in your buying group and shares the result in a demo report.

Request a demo insight report →

Sources

• Recro simulation data · April–September 2026 · 6 brands, 460 question-answer runs, 73 reports (internal, anonymized; run on a single AI model)
• 6sense · The B2B Buyer Experience Report for 2024
• Gartner · The B2B Buying Journey

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

A café chain’s operations director is comparing three coffee suppliers on their shortlist. The question is operational: “How often, and on which routes, do these suppliers deliver within Istanbul?” In the Recro simulation, the model answered: “This information is generally a contract-specific service, so it isn’t available in detail in public sources.”

Then the model said what it knew: that large roasters have their own distribution networks and that some brands are “known” to deliver on fixed scheduled routes. The information that would actually affect the decision wasn’t on any brand’s website.

On which topics does the model say “no information”?

When we grouped the 27 answers in Recro simulations that contained “no public information” by topic, the picture pointed straight at the middle of the decision stage:

What these topics share: all of them are asked in the final elimination, after the shortlist forms. And each belongs to a different key person. A keyword approach never sees these questions, because a phrase like “coffee supplier API integration” has no search volume. But the IT manager asking it may hold a veto over the purchase.

How does missing information show up in the answer?

As generalization. The model fills the gap with a category pattern: “Boutique roasters may offer more flexible minimum orders.” Sometimes that works for the brand, sometimes against it; it’s never the brand’s own statement.

As assumption. Phrases like “likely has the technological infrastructure” or “its innovation capacity may depend on its flexibility” show that the model has no evidence but still has to say something. In the European Broadcasting Union and BBC’s 2025 research, 20% of AI answers contained serious accuracy errors, including fabricated or outdated information. The bigger the gap, the bigger that risk.

As a comparative minus. The most striking example came in a sustainability question. The model said of a global competitor that it “has global sustainability reports and commitments”, while for the domestic brand being studied it said “less public information is available about its policies”. What the brand actually did was unknown. Not having written it down was read as weakness in the buyer’s eyes.

Why is this gap an opportunity?

Because the sentence is often built for the whole category. “Most suppliers in Turkey are reluctant to lock prices long-term” or “no supplier offering API support could be identified” says competitors share the same gap. In such a space, the first brand to publish the information becomes the model’s only source for that question.

We covered this for pricing in detail in what AI says when buyers ask about price. The same logic applies to delivery, quality, integration and sustainability.

How do you build a “not publicly available” inventory?

1. List the buying group’s final elimination questions. Write the questions procurement, finance, operations, quality and IT would ask, separately.
2. Ask AI these questions and flag the “no information” answers. On which topics can’t the model find information about you or your competitors?
3. Separate what can be shared. Not everything gets published. But delivery models, quality-reporting practice, integration options and the factors behind price are usually not trade secrets.
4. Publish under headings that answer the question. “Our delivery model”, “Batch-level quality reporting”, “Order integration”: headings that match the buyer’s question.
5. Ask again. Repeat the same questions a few weeks later and see whether the answer has changed.

How does Recro make this gap visible?

The Recro Insight Model reports the points where the model finds no information together with the key person asking. A “no information” sentence becomes a finding that shows in which final-elimination question, and for which decision-maker, the brand loses.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

For this gap, each pillar produces the following:

• The persona builder sets up the roles with a say in final elimination: operations, quality, finance and IT managers alongside procurement.
• The question builder produces these people’s questions, which have no keyword volume but decide the purchase.
• The report builder separates answers where the model found no information and fell back on generalization, and shows how competitors are described on the same topics.
• The action recommendation builder prioritizes topics where the category is silent as the first content to publish.

Compiled from Recro simulations · anonymized

• Personas: Café and hotel chain procurement, operations and quality roles · Coffee supply · Turkish
• Question: Decision · Delivery frequency, quality reporting, order integration
• Answer: “No public information” on all three topics; the gap was filled with a sector generalization.
• Competitors: No information was found for competitors on the same topics either.
• Class: Content GAP · Category-level gap
• Action: Publish the delivery model, quality-reporting practice and integration options under headings that match the question.

Executive summary

• In Recro simulations, AI said “no public information” in 27 answers; topics were price, delivery, quality, integration, currency risk and sustainability.
• Missing information shows up in the answer as generalization, assumption or a minus when compared with a competitor.
• The sentence is often built for the whole category; on a silent topic, the first brand to publish becomes the reference for that question.

Frequently asked questions

Does “no public information” hurt the brand?

If competitors are in the same position, not directly, but it wastes the opportunity. If a competitor has published the information, it works against the brand in comparison.

What shouldn’t be published?

Customer-specific prices, contract terms and confidential customer information. Engagement models, processes, standards and options can usually be shared.

Who asks these questions?

In our simulations, operations, quality, finance and IT roles asked the final-elimination questions alongside procurement. These people often hold a veto in the decision.

How soon does the answer change after publishing?

Change can show within a few weeks in search-grounded answers. Reflection in the model’s own memory waits for later versions.

What data are these findings based on?

Recro simulations for 6 brands between April and September 2026, with 460 question-answer runs. The data is anonymized and shows observed patterns.

Is AI saying “no information” about you in your buyer’s final-elimination questions? Recro builds a sample simulation with your buying group’s key people and shares the result in a demo report.

Request a demo insight report →

Sources

• Recro simulation data · April–September 2026 · 6 brands, 460 question-answer runs, 73 reports (internal, anonymized; run on a single AI model)
• EBU · AI assistants misrepresent news content 45% of the time (EBU and BBC, 2025)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.

AI visibility has quickly become a crowded market. Prompt-tracking tools, GEO agencies, “AI modules” added to SEO suites and consulting services make the same promise with different methods. For a marketing director or CMO, the hard part is finding a common yardstick to compare these offers.

This article builds that yardstick. For each question, we share a finding from Recro’s own simulations that shows why it matters. The findings are anonymized from 460 question-answer runs for 6 brands between April and September 2026.

1. On whose behalf is the measurement done?

In a simulation for a coffee supply brand, the brand was mentioned in 8 of 8 runs for the questions of a tourism and restaurant group’s F&B director persona. The same brand wasn’t mentioned at all in the questions of hotel chain, café chain and restaurant chain procurement directors. Had a single persona been chosen, the report would have said either “excellent” or “zero”.

Ask: Are personas derived from the brand’s real buyer structure, or is a generic “marketing manager” profile used? Are different roles in the buying group measured separately?

2. In whose language are the questions written?

In our simulations, brands appeared in 33% of answers when asked about by name and in 5.5% of decision questions that didn’t name them. Questions that include the brand’s name, or are written in the marketing team’s own category language, make results look better than they are.

Ask: Do the questions grow out of the buyer’s need and conditions? Are discovery and elimination questions without the brand’s name reported separately?

3. How many times, and over what period, is it measured?

In the coffee supply simulation, 236 runs produced 50 different “dominant competitor” names. In an enterprise software category, 112 runs produced 28. A single screenshot shows only one moment of that spread. SparkToro and Gumshoe.ai’s January 2026 research also found that the chance of two answers to the same question giving the same brand list is under 1 in 100.

Ask: How many times is each question run? Are results given as a rank, or as an inclusion rate and a time series?

4. Are the answer’s sources shown?

In a marine equipment simulation, one source that carried a competitor onto the shortlist was that competitor’s company profile on Kariyer.net, a job board. In other simulations, catalogs uploaded to document-sharing sites years earlier stood out as sources. An action plan made without seeing sources usually focuses only on the brand’s own website.

Ask: Does the report show the links that source the answer and which sources carry competitors?

5. Does it give the cause and the action alongside the number?

“Your visibility is 8%” is an observation, not a decision. The most common causes in our simulations were pricing and ROI information never being published (in all 73 reports), proof sitting only on the brand’s own site and absence from the category lists competitors appeared in. Each cause calls for a different action.

Ask: Are findings separated into gap types such as Communication, Content and Authority? Are actions prioritized and sector-specific?

6. Are AI’s claims verified?

When the model can’t find information, it fills the gap with generalizations. In our simulations, 27 answers followed “no public information available” directly with a sector generalization. In the European Broadcasting Union and BBC’s 2025 research, 45% of AI answers had at least one significant issue. An unverified AI output shouldn’t enter a report as a finding.

Ask: Are standout claims checked by a person against what exists online?

7. Are results retested?

Unless the same persona and question are measured again after an action is implemented, the work’s effect can’t be known. Model versions change too, so the measurement date and version should be recorded.

Ask: Is there a retest plan after actions? Are results reported with the model version?

Evaluation table

This table works for evaluating an in-house measurement as well as an outside service. We explain the core logic of measurement in how to measure AI visibility, and how decision questions are mapped in what is a decision question.

Keywords or key people: which are you buying?

The core difference between offers is this. Some track keywords and count how often a brand appears in prompts related to them; that’s valuable for following broad category trends. The other approach sets up the key person with buying intent and explains why the brand is present or absent in that person’s decision questions; that puts investment where sales decisions are made.

The two can complement each other. But the approach that brings the board an answer to “Why aren’t our buyers choosing us?” is the second one.

Where does Recro stand in this evaluation?

The Recro Insight Model was built to answer each of the seven questions structurally. Measurement starts with the key person with buying intent.

One simulation · 4 key pillars · 90+ steps

What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.

1. Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
2. Question builder: The brand-specific decision questions these people ask AI along their buying journey.
3. Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
4. Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.

Against the evaluation questions, each pillar maps as follows:

• The persona builder answers question one: measurement is done on behalf of decision-makers that represent the brand’s real buyers, specific to its sector and sales structure.
• The question builder answers question two: questions grow out of persona attributes and follow a natural buying-journey flow.
• The report builder answers questions three, four and six: daily outputs, the links that source the conversation, estimated visibility probability and a monthly reality check with human review.
• The action recommendation builder answers questions five and seven: a sector-specific signal list, prioritized actions and a retest after actions.

Executive summary

• In AI visibility work, the decisive question is “On whose behalf, with which questions?” more than “Which keywords?”
• In Recro simulations, the same brand showed 100% visibility for one persona and 0% for others; questions naming the brand inflated results up to six times.
• Strong work offers role-based personas, questions in the buyer’s language, repeated measurement, sources, causes and actions, human verification and retesting.

Frequently asked questions

Aren’t prompt-tracking tools enough?

They’re useful for following category-level trends. Explaining why a brand is missing from a specific buyer’s decision question takes brand-specific persona, question and source analysis.

How many personas are enough?

At least one for every critical role in the buying group. In our simulations, when the role changed within the same sector, both visibility and the dominant competitor changed.

When should we expect to see results?

Diagnosis comes with the first report. The effect of access and content actions is measured within weeks, authority actions within months.

Can we do this in-house?

Yes; the seven questions apply to an internal team too. The real challenge is building personas and questions independently of the brand’s own language and sustaining repeated measurement.

What drives the price?

The number of personas, the size of the question set, measurement frequency and language coverage. When comparing proposals, make sure these four variables are held equal.

Ask Recro these seven questions too. We’ll build a sample simulation with brand-specific personas and decision questions and share the result in a demo report.

Request a demo insight report →

Sources

• Recro simulation data · April–September 2026 · 6 brands, 460 question-answer runs, 73 reports (internal, anonymized; run on a single AI model)
• Search Engine Land · AI recommendation lists repeat less than 1% of the time (SparkToro, Gumshoe.ai)
• EBU · AI assistants misrepresent news content 45% of the time (EBU and BBC, 2025)

This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.