How to Measure AI Search Visibility (Tools and Metrics)

The short answer

You measure AI search visibility by running a fixed set of buyer prompts across ChatGPT, Perplexity, Gemini, Copilot, and Google's AI answers on a schedule, then recording whether you are recommended, cited, and described accurately. You confirm the commercial effect through branded search lift, AI-referred sessions in GA4, and AI-sourced SQLs in your CRM.

That is the whole system in one paragraph. The rest of this guide is the detail: which metrics a B2B software or tech company should track, how to build the prompt set, how to track it by hand in a spreadsheet, what the commercial tools cost and cover as of their own pricing pages, how to stop AI traffic from hiding inside direct in GA4, and how to attribute the channel to pipeline instead of reporting mentions nobody in the boardroom cares about.

We wrote it for the people who own this number at a software company: the founder or CEO who asked "does ChatGPT recommend us?" and got a shrug, the head of marketing who needs to defend an AI search budget with something more than screenshots, and the RevOps lead who has to make the CRM tell the truth about where deals came from. If you are still deciding whether the channel deserves the effort, our primer on AI search optimization for B2B tech covers the why. This article is the how of measurement.

Why is AI search visibility harder to measure than SEO?

It is harder because none of the instruments you rely on in SEO exist here. There is no Search Console for ChatGPT, no impression count, no average position. The assistant writes a fresh answer each time, the answer changes between runs, and most of the value is delivered without a click.

In Google, a ranking is a stable, observable fact. Position four for "Salesforce implementation partner" today is very likely position three to five tomorrow, Search Console tells you how many people saw it, and the click lands in GA4 with a clean source. In an AI assistant, the equivalent buyer prompt produces a generated paragraph that names three to six vendors, and the same prompt asked an hour later may name a different set. Ahrefs, studying 15,000 prompts in August 2025, found that only 12% of the links cited by ChatGPT, Gemini, and Copilot also appeared in Google's top 10 for the same prompt. Your rank tracker is measuring a different universe.

The second problem is that the outcome you want, being named as the recommended vendor, often produces no traffic at all. A CTO who asks Perplexity for nearshore data engineering firms, sees your name, and later types your domain into the address bar shows up in GA4 as direct. Nothing in that session says "AI". So the channel that put you on the shortlist gets no credit, and the last-click model quietly defunds it.

The third problem is that the stakes have moved. Ahrefs' 300,000-keyword study found that the presence of a Google AI Overview correlated with a 34.5% lower click-through rate for the top-ranking page, and a re-run on December 2025 data put that gap at 58%. If a large share of the clicks you used to earn now stay inside the answer, then the answer itself is the surface you have to measure. Our guide to Google AI Overviews for B2B goes deeper on that specific surface.

None of this makes the channel unmeasurable. It means you need a metric stack built for how assistants behave: sampled, prompt-based, and connected to the CRM. That is what the next section defines.

What metrics should a B2B tech company track for AI search visibility?

Track seven metrics in three layers. Presence metrics tell you whether the assistants name you. Quality metrics tell you how they describe you. Commercial metrics tell you whether any of it turns into pipeline. Most teams stop at the first layer, which is why most AI search reports get ignored.

1. Recommendation rate (prompt-set share of voice)

Recommendation rate is the share of your tracked prompts, across all engines and runs, in which your company is named in the answer. If you track 40 prompts on 4 engines weekly, that is 160 answers a week, and being named in 48 of them is a 30% recommendation rate. This is your headline presence metric, the closest thing AI search has to a ranking.

Compute it per engine as well as in aggregate, because engines disagree. A Salesforce consultancy might be recommended in 60% of ChatGPT answers and 15% of Gemini answers for the same prompts, and those two numbers demand different work. Share of voice is the competitive version of the same measure: your mentions divided by all vendor mentions across the prompt set. It tells you whether you are gaining ground on the three competitors who keep appearing next to you.

2. Citation rate

Citation rate is the share of answers in which one of your URLs is linked as a source. It is a separate metric from recommendation rate because assistants regularly name a company without citing it, and cite a page without recommending the company. Ahrefs' Brand Radar draws this exact line: a mention means the platform named your brand in its response, while a citation means it linked to your website as a source.

For a B2B services or software company, the mention usually carries more commercial weight, since it is what puts you on the shortlist. The citation matters because it sends verifying traffic, it compounds the model's trust in your domain, and it is the only one of the two that leaves a footprint in analytics. Track both, and record which URL got cited, because that tells you which pages the models find quotable.

3. Position in the answer

Position is where you appear in the list of vendors the assistant names: first, third, or last. Buyers read AI answers the way they read anything else, top down, and a vendor named first in a five-company shortlist gets a different level of attention than one tacked on at the end. Record position as an integer per answer and report the average across the prompt set. Most of the commercial tools call this rank or answer position; Peec AI and Goodie both track it explicitly.

4. Sentiment and accuracy of what the model says

Being named is not enough if the description is wrong. Assistants routinely attach stale positioning, an old headquarters, a service line you dropped two years ago, or a lukewarm caveat. Score each mention on two axes: sentiment (positive, neutral, negative) and accuracy (does the description match your current positioning, industries, and proof). Peec AI's brand perception feature, for example, fact-checks AI claims about your brand against statements you set, and flags recurring objections the models raise.

Accuracy is where a lot of the early work lives for tech companies. If ChatGPT describes a DevOps consultancy as "a web design studio" because that was the pitch in 2019, no amount of new content fixes it until the entity data across Crunchbase, LinkedIn, Clutch, and your own site tells the same current story. Our guide on how to get recommended by ChatGPT covers that consistency work.

5. Branded search lift

Branded search lift is the change in Google searches for your company name and its variants over the period you have been visible in AI answers. It is the first commercial signal you will see, and it appears before the traffic does, because a buyer who meets you in an assistant goes to Google to verify you. Pull it from Search Console (queries containing your brand) and from Google Ads impression data on brand terms if you run them. Compare against a pre-visibility baseline, and note the date you started earning recommendations for each prompt cluster.

6. AI-referred sessions

AI-referred sessions are GA4 sessions whose source is an assistant domain: chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, claude.ai, and the rest. This is the metric most teams already glance at, and it is the one that most understates the channel. We cover the GA4 setup and the direct-traffic leak in a dedicated section below. Treat this number as a floor, never as the size of the channel.

The sessions you do capture are worth watching closely. Semrush's July 2025 study, built on more than 500 digital marketing and SEO topics, found the average AI search visitor was worth 4.4 times a traditional organic visitor, measured by conversion rate. Adobe's AI traffic report put AI-referred visits to US retail sites up 62% year over year in July 2026, with those visitors converting at a rate 60% higher than non-AI traffic, and its September 2026 holiday forecast put August growth at 127%. That is retail data, not B2B, but the pattern of small volume and high intent matches what we see in software companies' CRMs.

7. Assisted conversions and CRM-tracked SQLs

The metric that ends the budget argument is the count of sales-qualified leads and closed revenue where an AI assistant was the first touch or a named influence. You get it from three places: the AI channel in GA4 conversions, the "How did you hear about us?" field on your demo and contact forms, and the lead source field in the CRM as qualified by sales. We treat this as the primary metric on every AI search engagement at XQL, and we explain the mechanics in the attribution section further down.

How do you build the prompt set you will measure?

Build the prompt set from the questions your buyers ask when they are choosing a vendor, not from your keyword list. Thirty to sixty prompts is enough for most B2B tech companies. Group them by intent, fix them in writing, and do not edit them mid-quarter, or your trend line is meaningless.

Start with the commercial prompts, because they are the ones that decide deals: "best Salesforce consulting partner for a mid-market manufacturer", "top nearshore software development companies for fintech", "who should we hire for a Kubernetes migration". Add comparison prompts ("X vs Y", "alternatives to X"), category prompts ("what is a fractional CMO for a SaaS company") where you have content that can be cited, and a handful of branded prompts ("what does Company do", "is Company a good vendor") to monitor accuracy. Write them the way a buyer types, with context and constraints, not as two-word keywords.

Then narrow ruthlessly. A ten-person DevOps consultancy will not win "best DevOps company" against global firms, and tracking it only produces a depressing zero. Track the specific prompt you can own, "DevOps consultancy for fintech on AWS", win it, and expand outward. Opsworks, a DevOps company we work with, was positioned for one important commercial keyword and recommended by the major assistants within a single month, precisely because the target was that specific. The B2B SaaS AI search playbook walks through prompt selection for product companies in more detail.

• Commercial vendor prompts (50% of the set): "best", "top", "who should we hire", with industry, stack, and geography as constraints.
• Comparison prompts (20%): "X vs Y", "alternatives to X", "is X worth it".
• Category and educational prompts (20%): questions your pillar content answers, where a citation is the realistic win.
• Branded prompts (10%): "what does Company do", "Company reviews", to catch inaccurate descriptions early.

How do you track AI search visibility manually?

You track it manually by running each prompt in each engine on a fixed day every week, in a fresh session with no memory, and logging six fields per answer into a spreadsheet. Expect three to four hours a week for 40 prompts across four engines. It is tedious, but it is free, it keeps you close to the actual answers, and it is how we baseline every new client before we decide whether a tool is worth paying for.

The discipline matters more than the tooling. Use a logged-out or temporary session so personalization and chat history do not skew the answer. Run every prompt once per engine per week on the same weekday. Copy the full answer text into the sheet, because you will want to reread the exact phrasing later when a description changes. And keep one row per prompt per engine per run, never overwrite, so the history becomes the trend line.

• Prompt ID and text, and the intent group it belongs to.
• Engine and run date (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode or AI Overview).
• Named: yes or no. If yes, position in the list of vendors.
• Cited: yes or no. If yes, which URL of yours.
• Competitors named, in order, so share of voice can be computed.
• Sentiment and accuracy score, plus a note on anything the model got wrong.

Once the sheet has four or five weeks of rows, a pivot gives you recommendation rate by engine, citation rate by page, average position, and share of voice against each competitor. That is a complete presence dashboard for the price of a spreadsheet. The moment it stops being enough is when the prompt set grows past 60 to 80, when you need daily rather than weekly resolution, or when you want to track multiple markets and languages. That is when the tools below pay for themselves.

One caveat on the manual method: a single run per prompt per week is a small sample of a non-deterministic system. The trend across weeks is reliable; any single week's number is noisy. Report it as a rolling four-week average and you will avoid celebrating or panicking over one run.

Which tools track AI search visibility, and what do they cost?

The category has matured fast. The tools below all automate the prompt-tracking loop described above, and most now add sentiment, citation analysis, and some form of AI traffic reporting. Everything here is taken from each vendor's own website at the time of writing (October 2026). Prices change often, so check the pricing page before you buy, and treat the figures as a guide to the tier you will need rather than a quote.

Semrush AI Visibility Toolkit

Semrush sells its AI Visibility Toolkit as a standalone product at $99 per month, with no free trial, or bundled with its SEO toolkit as Semrush One from $199 per month. The standalone plan is billed per domain and includes 25 tracked prompts. It tracks your brand across ChatGPT, Google AI (including AI Mode), Gemini, and Perplexity, with prompt-level tracking, a brand performance view that benchmarks mentions and sentiment against competitors, and an AI Search Health check inside Site Audit for technical issues that block AI crawlers. It is the natural pick for teams already living in Semrush who want AI visibility in the same workspace as their organic reporting.

Ahrefs Brand Radar

Ahrefs' Brand Radar sells its AI Visibility Index at $199 per month, and every paid Ahrefs plan includes a small daily quota of custom prompts (5 a day on Lite, 10 on Standard, 20 on Advanced). Extra custom prompt tracking is an add-on from $50 per month. Its distinctive angle is scale: Ahrefs models AI visibility across a very large set of search-backed prompts derived from its keyword database, and calculates an AI share of voice from how often brands are mentioned or cited in ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, and AI Mode. It also separates mentions from citations explicitly, and shows the sources (including Reddit and YouTube) that influence the answers. For a tech company that already pays for Ahrefs, the included prompt quota is the cheapest way to start.

Profound

Profound is the enterprise end of the market. Its public pricing page lists a free trial (50 prompts run daily for seven days across ChatGPT, Gemini, and Google AI Overviews) and a custom-priced Enterprise plan that tracks up to nine answer engines, including Perplexity, Google AI Mode, Copilot, Claude, DeepSeek, and Exa Search, with daily tracking, API access, and SSO. Its Answer Engine Insights module tracks citations, sentiment, ranking, and competitive presence, and its Agent Analytics module tracks AI-sourced traffic through integrations with Google Analytics, Cloudflare, Vercel, and other hosting and CDN providers. Expect a sales conversation rather than a credit card checkout.

Peec AI

Peec AI, a Berlin-based platform, offers a 15% discount for annual billing; check its pricing page for current plan prices. Its brand plans are Starter (50 prompts, your choice of three models, daily tracking, one project), Pro (150 prompts, two projects), Advanced (350 prompts, five projects, multi-country, Looker Studio integration), and a custom Enterprise tier with up to 13 models, API access, and SSO. Self-serve plans choose their three models from ChatGPT, Google AI Mode, AI Overviews, Copilot, Gemini, and Naver AI, with Perplexity as a paid add-on and Claude, Grok, DeepSeek, Mistral, and others on Enterprise. Its measurement view reports visibility, position, sentiment, and share of voice daily against competitors, and its newer brand perception feature fact-checks what models claim about you against facts you set. It also reports AI referrals and audits which of 40+ AI crawlers your robots.txt allows.

Otterly.AI

Otterly.AI publishes the simplest pricing in the category. Lite is $29 per month for 15 prompts, Standard is $189 per month for 100 prompts, and Premium is $489 per month for 400 prompts, with a 15% discount for annual billing and a free trial. Every plan tracks four engines by default (ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot), and Claude, Google AI Mode, and Gemini are paid add-ons priced by tier. Standard and above include API and MCP access, a Looker Studio connector, and Agent Analytics for AI bot and referral traffic. For a small B2B software company that wants automated weekly tracking of 40 to 100 prompts without a sales call, this is the lowest-friction entry point.

Scrunch AI

Scrunch lists a Starter plan at $300 month to month ($250 per month billed annually) with 350 custom prompts, 1,000 industry prompts, three personas, and three user seats, and a Growth plan at $500 month to month ($417 annually) with 700 custom prompts and five seats. Enterprise is custom, with SAML SSO and a data API. Coverage spans ChatGPT, Claude, Gemini, Perplexity, Google AI Mode and AI Overviews, and Meta. Every plan includes citation tracking, page audits, and agent traffic monitoring to attribute AI-driven visits. It offers a seven-day free trial of Starter without a credit card.

Goodie

Goodie positions itself as a closed-loop AEO platform, combining monitoring, optimization recommendations, and analytics. Its published brand plans start at $399 per month for the Core tier and $999 per month for Pro, with a custom Enterprise tier that covers multi-brand tracking, up to 13 models, SSO, and API access. It measures citation frequency, ranking position, sentiment, and share of voice daily, and its analytics module is built to trace AI-sourced traffic through the conversion funnel and attribute revenue to platforms and prompt categories, which is the part most relevant to a RevOps reader.

Rankscale

Rankscale, based in Vienna, uses credit-based pricing: a query to most engines costs 0.25 credits, some engines such as Claude cost more, and plans include a monthly credit allocation. Essentials starts at $17 per month on annual billing, Pro is $99 per month with 1,200 credits and a seven-day free trial, Growth is $385 with 5,500 credits, and Enterprise is $780 with 12,000 credits. It monitors a wide engine list (ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, DeepSeek, Mistral, Claude, Grok, Copilot) with scheduling from hourly to monthly, and adds citation analysis, sentiment analysis, competitor benchmarking, and a query fan-out view showing the internal searches engines run while answering. The credit model suits teams that want to track many prompts on a weekly rather than daily cadence.

Which one should a B2B tech company pick? If you already pay for Ahrefs or Semrush, start with what you have. If you want to run this on a small budget with no sales call, Otterly or Rankscale will cover a 40-prompt set for well under $200 a month. If you have multiple brands, markets, or a RevOps team that wants attribution inside the tool, look at Peec, Scrunch, Goodie, or Profound. In every case, run the manual baseline first for a month so you know what you are buying.

How do you see AI-referred traffic in GA4, and why does it leak into direct?

You see it by building a custom channel group in GA4 with a rule that matches assistant domains in the session source, and by watching for the utm_source=chatgpt.com parameter that ChatGPT appends to many outbound links. Even done correctly, a meaningful share of AI-influenced visits will still land in direct, because the assistant hands over a name without a link, or strips the referrer.

Out of the box, GA4 files visits from chatgpt.com, perplexity.ai, or gemini.google.com under the generic Referral channel, mixed in with every other referring site. Create a custom channel group (Admin, Data display, Channel groups) with a channel named AI Assistants, and a rule that matches source against a regular expression covering the domains you care about. Custom channel groups apply retroactively to your property's history, so you get a backfilled view the moment you save it. A working pattern looks like this:

Rule: session source matches regex .*(chatgpt|openai|perplexity|gemini\.google|copilot\.microsoft|bing\.com/chat|claude\.ai|anthropic|you\.com|mistral|deepseek|grok|meta\.ai).* and the channel is named AI Assistants. Extend the list as new assistants appear, and check the Referral report every month for domains you missed.

Two edge cases will still bite you. First, ChatGPT often appends utm_source=chatgpt.com to links, and when a UTM source is present without a matching medium, GA4 stops using the referrer and the session can fall into Unassigned rather than Referral. Add a rule that matches source equal to chatgpt.com regardless of medium so those sessions land in your AI channel. Second, some assistant surfaces and some browser configurations send no referrer at all, so the visit arrives as direct with nothing to match. There is no analytics fix for that; it is the reason the form field and the CRM matter.

Then there is the bigger leak, the one no configuration recovers: the buyer who reads your name in an answer and does not click anything. They search your brand on Google (which shows up as organic, brand), or type your domain (direct), or ask a colleague. We see this in every engagement. The AI-referred session count moves a little; the branded search count and the "heard about you from ChatGPT" form answers move a lot. Report AI-referred sessions as a floor and pair it with the other two signals, and you will get an honest picture rather than a flattering or a dismissive one.

What does an AI search visibility dashboard look like?

A useful dashboard fits on one page and reads top to bottom from presence to pipeline. Each row is a metric, each column is a period, and the last column says what changed and why. The sample below is the structure we report to clients monthly, with illustrative figures to show the shape; your numbers will differ.

Three notes on reading it. Recommendation rate and position should move first, within four to eight weeks of the on-site and off-site work landing. Branded search and form answers lag by a few weeks, because a buyer needs time between meeting you in an answer and raising a hand. SQLs lag furthest, and in a B2B tech sales cycle you may not see the first one for a quarter. Judge each layer on its own timeline, or you will kill the program at week six because the CRM is still empty.

How often should you measure AI search visibility?

Measure presence weekly, review quality monthly, and report commercial metrics monthly with a quarterly deep-dive. Daily tracking is useful for large prompt sets and for catching regressions after a model update, but for most B2B tech companies it adds noise without adding decisions.

• Weekly: run the prompt set (manually or via tool), log recommendation rate, citation rate, and position. Flag any prompt where you dropped out or a new competitor appeared.
• Monthly: review sentiment and accuracy across every mention, fix inaccurate descriptions at the source, update the branded search and GA4 AI channel numbers, and reconcile form answers with CRM lead source.
• Quarterly: re-examine the prompt set against sales conversations (which prompts produced real pipeline), retire the ones that never will, add the new questions buyers are asking, and reset the baseline for any prompts you change.
• After any major model release or engine update: re-run the full set once, out of cycle, and compare against the previous week. Model updates can reshuffle a category overnight.

Cadence is also where the manual and tool approaches diverge in cost. Weekly manual tracking of 40 prompts on four engines is a half-day job. Daily tracking of 150 prompts on six engines is 27,000 answers a month, and that is a tool's job. Match the frequency to the decisions you will make with the data, not to what the tool can do.

How do you attribute AI search visibility to revenue?

You attribute it by capturing the AI source at the three points where it can be captured (analytics, the form, and the sales conversation), writing it into a single lead source field in the CRM, and reporting SQLs and closed revenue by that field. Nothing about this is technically hard. It fails when nobody owns the field.

Step one is the form. Add a required "How did you hear about us?" question to the demo, contact, and intro call forms, with "AI assistant (ChatGPT, Perplexity, Gemini, etc.)" as an explicit option and a free-text follow-up for which one. Self-reported attribution is imperfect, but it is the only instrument that catches the buyer who never clicked. In our experience it is also where the AI channel first becomes visible to leadership, because a founder reads "ChatGPT recommended you" in a form submission and starts paying attention.

Step two is the CRM. Create a lead source value for AI search, and set a rule for how it gets populated: GA4 AI channel on the first session, or the form answer, or the sales rep's discovery call note, in that order of precedence. Then make sales confirm it at qualification. A rep asking "how did you find us?" on the first call, and recording the answer, is the most reliable attribution instrument in B2B, and it costs nothing. Report SQLs and closed-won by lead source monthly, and the AI row will either justify the investment or tell you to fix something.

Step three is the reconciliation. Once a quarter, put the prompt-level data next to the pipeline data. Which prompts did the AI-sourced SQLs actually come from? Sales will often know, because the buyer said "I asked ChatGPT for Salesforce partners with healthcare experience." Those prompts get more investment. The prompts you win but that never produce a conversation get cut from the set. This loop, visibility to prompt to SQL to prompt, is the whole point of measuring, and it is what turns AI search from a vanity dashboard into a channel with a cost per SQL you can compare against paid.

What do measured results look like for B2B tech companies?

This is what the stack above produces when the work behind it is done. We run AI search optimization for software and tech companies and track every engagement on the metrics in this article, from prompt-set placement to CRM-confirmed deals. Across the portfolio we hold an 80% success rate at getting a client recommended for a target commercial prompt.

The clearest revenue example is Computools, a software development company we positioned as the recommended Salesforce partner inside the major LLMs. Within a three-month engagement, two $1M enterprise deals closed from ChatGPT, $2M in total. That is a pipeline outcome, the bottom row of the stack, not a visibility score.

They operated with the discipline and initiative of an internal senior marketer. (Computools, COO)

Intelvision, a staff augmentation company, shows what the SQL row looks like at a smaller scale: two to four sales-qualified leads a month sourced from ChatGPT, alongside the Meta campaigns we run for them. Gapsy Studio, a design agency, is the cleanest AI-referred sessions example we have: traffic from AI assistants grew from 10 to 154 visits a month in three months, alongside a 70% increase in Google clicks, after a previous agency had delivered nothing in six months. Small absolute numbers, and the first SQLs from organic search arrived with them.

On the presence layer, Baytech Consulting, a software development company, reached a 100% placement rate across the AI-search prompts we targeted: recommended by every major assistant for three commercial keywords. Opsworks, a DevOps company, earned the top AI recommendation for one commercial keyword within a single month. Both are recommendation-rate outcomes, measured exactly the way this article describes, and both are on the path to the CRM row rather than the end of it.

What impressed us most was their deep specialization in working with software development companies. (Baytech Consulting, Partner)

Across 60+ B2B tech companies and 9+ years, we have tracked more than $30M in CRM-attributed, marketing-led revenue for clients. You can read the individual engagements, with the metrics behind them, in our case studies.

What mistakes make AI search visibility tracking useless?

Most broken AI visibility programs fail on measurement before they fail on execution. The same handful of mistakes appears in almost every audit we run, and each one is cheap to fix.

• Tracking prompts nobody types. A set built from your SEO keyword list measures a channel your buyers do not use. Build it from buyer questions and sales call notes.
• Changing the prompt set every month. Every edit resets the trend line. Fix the set for a quarter and add new prompts as a separate cohort.
• Running prompts in a logged-in, personalized session. Chat history and memory bias the answer toward you. Use a clean session every time.
• Reporting AI-referred sessions as the size of the channel. It is a floor. Pair it with branded search and form attribution or you will undercount by a wide margin.
• Counting mentions without checking accuracy. Being recommended with a wrong description sends the wrong buyers, or none.
• Owning no CRM field. If nobody is responsible for the lead source value, the revenue row stays empty and the budget conversation is lost by default.
• Judging the program at week six on SQLs. Presence moves in weeks, pipeline in quarters. Set expectations per layer before you start.

Where does XQL fit?

Measurement is built into every AI search engagement we run for software and tech companies. We baseline the prompt set before any work starts, track recommendation rate, citations, position, and accuracy across every major engine on a fixed cadence, configure the GA4 channel and the form attribution, and instrument the CRM so AI-sourced SQLs and revenue show up as a named row in your pipeline report. Then we do the work that moves the numbers: entity fixes, quotable pages, the authoritative listicle in your category, and the independent mentions the models trust. Our AI search optimization service page describes the full program.

If you want to know where you stand today, we can run the baseline for your top commercial prompts and show you the recommendation rate, who is winning instead, and what it would take to change it. Book a 30-minute intro call and bring the three prompts you most want to win.