AI Quotes Your Pricing Wrong 71% of the Time

When a buyer asks ChatGPT or Perplexity what your product costs, the answer is stitched together from across the web, and it's wrong more often than not. Getting it right is the job of Generative Engine Optimization (GEO), which structures your information so AI engines cite it accurately.

To measure the gap, I ran 14,123 controlled queries across six AI platforms and 110 SaaS brands, validating every response against vendor-published prices. Here is what the data shows, and how GEO closes the gap so AI stops handing buyers the wrong number.

Finding 1 - AI Gets Your Pricing Fully Right Only 29% of the Time

There is a difference between AI quoting a price and AI quoting the correct price.

Across all the responses where AI committed to a direct price, I measured accuracy against the real vendor-published figures. The results:

AI gets your starting price right more often than your top tier price. Most errors happen at the top end. Starting prices are easy to find. Top tier prices are usually hidden behind a "contact sales" page or buried in formats AI crawlers struggle to read.

Most of these errors are not hallucinations in the technical sense. The model isn't fabricating numbers. It's accurately repeating wrong or outdated figures from third-party sources. The model is faithful to bad data, not inventing it. That distinction matters because the fix is different.

Finding 2 - AI Cites Your Own Website for Just 31% of All Pricing Questions

Every time AI answers a pricing question, it pulls from 4 to 8 different sources simultaneously. Here is where that information actually comes from:

The 69% that isn't your website breaks down into three buckets:

• Review sites: G2, Capterra, TrustRadius, SoftwareAdvice — user-generated reviews and ratings platforms where buyers compare software. Most let vendors claim and edit their profiles.
• Software directories: SoftwareFinder, SelectHub, ITQlick — curated listing sites that publish feature breakdowns and pricing. Many have not been updated in 12 to 36 months.
• Auto-generated comparison pages: CostBench, ProPicked, StackScored — pages built by scraping G2, Capterra, and your pricing page. No profile to claim. Often the last to reflect a price change.

Most of what AI LLMs tell buyers about your pricing comes from sources you do not control. Many of those sources are running outdated numbers. Your pricing page is one input among six, and it accounts for less than a third of what LLMs actually cite.

Finding 3 - When AI Gets Your Pricing Wrong, It Usually Gives Buyers a Number Lower Than What You Actually Charge

When AI gets your price wrong, it almost always errs in the same direction. Here is how those wrong answers break down:

The low quotes do the most damage. A buyer who has been told your product costs half of what it actually does shows up to a sales call with a locked-in expectation. Your team spends the first part of every conversation correcting the number before they can start selling.

The high quotes are less common but carry their own cost: buyers who see a number significantly above your actual price may not make it to the sales call at all.

Finding 4 - Pricing Accuracy Doesn't Vary Much Across AI Platforms

Accuracy varies across platforms, but not dramatically. No single platform stands out as significantly more reliable than the others.

Perplexity gets pricing right most often. Google AI Mode almost always gives a number, but that number is the most likely to be wrong. Google AI Overviews behaves differently from the rest: it refuses to answer the pricing question entirely 71% of the time.

Outside Google AI Mode and Google AIO, the gap between platforms is small. If your pricing is wrong in AI answers, the platform your buyer is using is probably not the reason why.

Finding 5 - Being a Larger Brand Does Not Mean AI Represents Your Pricing More Accurately

You would expect larger brands to have more control over how AI LLMs talk about their pricing. The data says the opposite.

For example, Workday is a $7+ billion company, yet when buyers ask AI about its pricing, AI fails to provide a clear answer 76% of the time.

A side-by-side comparison of an LLM's response versus the pricing information available on Workday's website makes this gap immediately visible.

Large brands attract far more third party coverage, and AI does not prioritize your official pricing page over anyone else. Your own website is just one voice in a very crowded room.

What You Can Do About It

The findings point to a clear split in where the problem lives. Some of it is on surfaces you fully own and can fix directly. The rest is spread across third-party sources you don't control but can still influence. I've broken the actions into those two buckets: What you can control and what you can influence.

What You Can Control: Your Own Site and Channels

These are the actions on surfaces you fully own, making them the most impactful part of any GEO program. Changes here are immediate, they don't require anyone else's approval, and they have the most direct impact on what AI retrieves and quotes about your pricing. Start with your own site. Full control, highest leverage, and the fixes there have a direct impact on every platform simultaneously.

Action 1 - Make Your Pricing Page Readable by AI

If AI cannot read your pricing page, it falls back on whatever third party has a price for you, and those numbers are often wrong. Many brands block AI web crawlers without realizing it, usually because of old security defaults that have never been reviewed.

• Let AI crawlers in: Open your robots.txt file (at yoursite.com/robots.txt) and explicitly allow each major AI web crawler: OAI-SearchBot, ClaudeBot, PerplexityBot, GPTBot, and Google-Extended. Then check Cloudflare, AWS WAF, or any anti-bot tool you use, since those often block unknown bots by default and need the same user agents whitelisted there too.
• Render your prices in the HTML: Open your pricing page, right-click, choose "View page source," and search the raw HTML for one of your plan prices. If you find it, you are fine. If you do not, your prices are being added by JavaScript after the page loads. This means AI web crawlers see an empty page. Ask your dev team to render the prices server-side or embed them as static HTML.
• Publish an llms.txt file: Create a plain text file listing your plan prices, add-on prices, billing terms, and any discount details. Format it as readable text or markdown, not JSON. Upload it to yoursite.com/llms.txt (the root of your domain). Cursor, Vercel, and other AI tools check this URL automatically. Here is a sample pricing llms.txt file you can download and use as a starting point for your own.

Doing these three things once is the easy part. Keeping them working as your team ships site updates and security changes is what trips most brands up.

That's what Crawl Assurance is built for. It audits which AI crawlers can actually reach your pricing page, flags what's blocked, and re-checks it every month. If AI can't read your page, nothing else in this report matters.

Book a demo to see Crawl Assurance in action.

Action 2 - Add Structured Data and a Last-Updated Date to Your Pricing Page

Structured data is hidden code that tells AI what each price on your page means. Without it, AI has to guess from the visual design, and it often guesses wrong.

• Tag each plan with its full pricing details: For every paid tier, mark up the price, the currency (use a 3-letter ISO code like USD or EUR), and a short description of what's included. This is the core of the markup, since it tells AI exactly which number belongs to which plan.
• Add a "last modified" date and a "price valid until" date: Use dateModified for the date you last updated the page, and priceValidUntil for when the listed price expires or needs review. AI uses these dates to decide whether your page is more trustworthy than a 2-year-old review site profile.
• Also show a "Last updated" line in plain text on the page: A visible note like "Last updated: June 2026" near your pricing table signals freshness to AI even when it can't parse the schema.org, and it builds trust with human buyers at the same time.
• Test your markup before publishing: Paste your pricing page URL into Google's free Rich Results Test. It will tell you if your structured data is valid, show you exactly what AI and search engines see, and flag any errors to fix.

You can download our sample JSON-LD snippet. It's a small block of code you paste into the <head> of your pricing page (or just before </body>). Replace the placeholder values with your actual plan names, prices, and descriptions, then save your changes. Run your pricing page through Google's free Rich Results Test to verify the markup is valid before publishing it live.

Action 3 - Mention Your Prices Across Multiple Pages

One pricing page is not enough. AI retrieval systems build confidence in a number when they see it repeated consistently across multiple pages on your domain. A single source is easy to doubt. The same price appearing on your pricing page, a blog post, a help article, and your homepage creates a signal that is much harder for the model to override with a third-party figure.

Mention your prices on at least three other pages on your own site. Pick from these:

• Write a blog post about your pricing that mentions each plan's actual price.
• Add prices to a help or support article by tying each price to a feature ("Pro users can do X at $Y/month").
• Make a separate page for each plan (for example yoursite.com/plans/starter, /plans/pro, /plans/enterprise).
• Build a comparison page with your pricing next to a competitor's (for example yoursite.com/compare/[competitor]).
• Add a price line to your homepage, like "Plans from $X/month".

A blog post with your specific prices in context is the highest-leverage move here. It creates a standalone, crawlable, dateable page that AI retrieval systems can find independently of your pricing page. Almost no SaaS brand does this.

Action 4: Fix What G2, Capterra and Other Third-Party Sites Are Publishing About Your Pricing

Your pricing shows up in more places than your pricing page: review sites, comparison sites, old blog posts, partner pages, press releases. AI cites all of them, and many are running outdated numbers.

The G2 and Capterra problem is the most urgent. If any of your plans say "contact sales," G2 or Capterra is almost certainly publishing a number for you anyway, and AI treats it as the truth.

In this study, every contact-sales brand had G2 or Capterra publishing a price for them, often without their knowledge.

That number becomes what your buyer walks into the sales call believing.

Two ways to address it:

• Show your prices. Publish them on your pricing page, or at minimum a "Starting from $X/month" callout on your homepage. When your own page has a number, AI has a more authoritative source to pull from.
• Fix what others publish. Submit corrections through your profiles on Capterra, G2, SoftwareAdvice, and TrustRadius. You cannot delete an old Reddit thread, but you can fix the listings you own.

Also search site:yourbrand.com for every price you have ever published and update or remove outdated mentions. Do the same on partner pages and press releases. Free to fix, and worth doing.

Action 5 - Publish a Youtube Pricing Explainer Video

YouTube is one of the main places AI looks for information about your pricing. If you do not publish your own video, AI will use whatever a reviewer or random creator has put up instead.

• Publish a video on your brand's channel with a title like "[Your brand] pricing explained" or "Pro vs Enterprise: which plan is right for you."
• Walk through each plan with the actual price and explain who it is built for.
• Keep it up to date. Re-publish quarterly when prices change, or add a "last updated" note in the description.

The video does not need to go viral. It just needs to be accurate, recent, and easy to find.

What You Can Influence: Third-Party Sites

You don't own these surfaces, but you can shape what lives on them. The sources AI pulls your pricing from are not static, and most of them accept corrections, claims, or direct outreach.

Action 6: Find Out What AI Is Actually Saying About Your Pricing

You cannot fix what you do not know is broken. Before anything else on the third-party side, run a few pricing questions about your brand through every major AI platform and log what comes back. This is your baseline.

Run these on each of the six platforms (ChatGPT, Perplexity, Claude, Gemini, Google AI Mode, Google AI Overviews):

• "How much does [your brand] cost?"
• "What is [your brand] pricing?"
• "Does [your brand] have a free plan?"
• "[Your brand] vs [your top competitor]: which is better value?"

For each response, extract the sources AI cited, what price it quoted, and which competitors it mentioned even when you didn't ask about them.

Re-run the same set every month. Most marketers skip this and react to anecdotes. A monthly citation audit is the difference between guessing and knowing where your pricing story is actually coming from.

If you want this running automatically rather than manually, VisibilityStack's Trust Signal Engine tracks how AI cites your brand across all six platforms on a recurring basis, flags which sources are being pulled, and surfaces pricing discrepancies as they appear.

Book a baseline call →

Action 7: Claim and Update Your Profiles on Software Directories and Comparison Sites

After review sites, the next biggest sources AI pulls pricing from are two groups: software directories and auto-generated comparison sites.

Software directories

These have brand profile pages you can claim and edit. Start with our list of the most-cited directories in this Google Sheet. Make a copy, filter for your category, and work through it:

• Claim your profile on the top 10 directories in your category. Most have a "vendor signup" or "claim this profile" button on the brand page; some require a quick email to their editorial team.
• Update your pricing on each one with your current plan prices, currency, and what each plan includes. Many of these profiles have not been touched in 12 to 36 months.
• Pay for placement on the top 3 directories where the ROI makes sense. Most sell sponsored top-of-list slots or featured comparisons. Get their media kits and compare cost per citation.
• Re-check accuracy every quarter, and immediately after any pricing change.

Comparison sites

These auto-generate brand pages by scraping G2, Capterra, and your pricing page. Most have a vendor claim or listing page you can submit to directly.

• Claim your profile where available and submit your current pricing directly. Even a basic submission gives you one touchpoint to correct outdated numbers.
• Check your affiliate commission rate against your top competitors. A lower rate means a lower spot in their ranked lists.
• Most of these sites sell direct sponsorship. Ask for their media kits and compare cost per citation.
• Email the top 20 sites with your current pricing. Most editors will accept corrections without you paying.

For the long tail of smaller sites, do not chase them one by one. Fix the sources they scrape from instead: structured data on your pricing page, your own /alternatives content, and accurate G2 and Capterra profiles.

The fastest way to find out exactly which directories and comparison sites AI is currently citing for your brand is to run a citation audit through VisibilityStack. It surfaces every source appearing in AI answers for your pricing queries, so you know which profiles to prioritize instead of working through a generic list.

Run your citation audit

Action 8: Engage Reddit and YouTube Creators in Your Category

Reddit and YouTube together account for 16% of what AI cites about your pricing. Reddit in particular gets cited far more often than its share of the internet would predict. The full mechanics behind that are in our companion study - The Anatomy of a Cited Reddit Thread.

Reddit

Search your brand name across r/SaaS, r/SaaSdeals, and subreddits in your category. Look for three types of threads:

• Buyers asking what your product costs.
• Comparisons between you and a competitor.
• Threads requesting feedback or recommendations in your category.

Reply from a clearly labeled brand account with accurate, specific pricing. Never post anonymously or pretend to be a customer. These are the threads AI is already pulling from. Getting your accurate pricing into them is the fastest way to influence what AI quotes.

VisibilityStack's Trust Signal Engine identifies which Reddit threads are currently being cited for your brand's pricing queries, tracks which new contributions AI starts to cite, and measures how your citation share shifts over time.

See how it works →

YouTube creators

You can't claim someone else's channel, but YouTube is one of the main places AI pulls pricing from, so what creators say about you is worth shaping. There are two moves here, and you can run them together.

• Fix the coverage that already exists. Find the videos reviewing you and your competitors. If a creator has your pricing outdated or wrong, reach out and ask them to correct it, or sponsor a fresh comparison that gets the numbers right.
• Get more coverage. Reach out to creators in your category and offer to be featured in a pricing or comparison video that quotes your actual plan prices. This is about citations, not reach, so the creator does not need a big following. A small channel with an accurate, recent video naming your prices is worth more to AI than a viral one that never mentions a number.

Where VisibilityStack Comes In

Managing AI pricing accuracy is not a one-time fix. It is a recurring problem across a dozen surfaces: your own site, review profiles, software directories, comparison pages, Reddit threads, and YouTube creators. Some need technical fixes. Others need ongoing relationships with editors, reviewers, and community members. All of them need to be checked every month.

That is what VisibilityStack is built for. The Trust Signal Engine tracks how AI cites your brand across all six platforms and surfaces which sources are driving wrong quotes. The Embedded Team handles the execution: fixing profiles, engaging the right threads, and tracking how your citation share changes over time.

AI and third parties are already writing your pricing story. The data in this report shows what that looks like when you leave it to them.

Book a baseline call

Methodology

I ran 14,123 controlled queries across six AI search platforms, tagged every response against a frozen schema.org, and validated the results against vendor-published prices. The full analysis plan was locked two days before any query was fired.

Brand panel

110 SaaS brands across CRM, HR, Project Management, Email Marketing, and Help Desk. Selected across four revenue sizes, from household names down to small niche players.

Platforms queried

ChatGPT, Perplexity, Claude, Gemini, Google AI Mode, and Google AI Overviews. Each platform's direct API was used where possible, with DataForSEO as a backup.

Pricing questions asked

Eight ways buyers ask about pricing: "How much does X cost?", "What is X pricing?", "What are X's pricing plans?", plus four variations and two additional questions about free plans and pricing perception.

How I tagged the responses

Every response was sorted into 16 source-type categories by GPT-4o, then cross-checked against Claude Sonnet 4.5. Agreement was strong: κ = 0.94 for vendor citations, κ = 0.89 for price mentions.

How I got the real prices

Real prices were pulled two ways. Claude Sonnet 4.5 with web search scraped each vendor's pricing page. Those numbers were then cross-checked against the prices AI platforms most often quoted across the dataset.

Pre-registration

The full analysis plan was locked two days before running any queries. Six measurements, one main statistical model, and a defined list of analyses committed to in advance. Full methodology and raw data are in the open repository at github.com/VisibilityStack/am-research.