How to be found in AI search

Why your competitor spent wrong and you can spend right

Most brands pursuing AI visibility make a critical allocation error: they spend 70% of budget on tools and 30% on the work that creates citations. The data suggests the opposite is optimal. Ahrefs' 0.664 Spearman correlation between web mentions and AI citations means your money has disproportionate leverage when spent on being mentioned. A brand that allocates £1,000/month spending £700 on tool subscriptions and analytics, £300 on actual visibility work (publication placement, community building), will see slower AI visibility growth than a brand allocating the same £1,000 at £200 tools and £800 on visibility work. The reason is pure leverage. A tool tells you whether something is working. Visibility work makes something work. Paying for information about a broken engine instead of fixing the engine is the classic vendor-capture mistake.

The allocation error traces to how vendors market to buyers. A tool vendor can demonstrate ROI in a demo: "here is your dashboard, here is your citation share moving up". A visibility consultant has to say "I will spend your money on publication placement, community participation, and analyst relations, and six months from now, if the work compounds, your referral traffic from AI will grow". The first is proof by dashboard. The second is proof by outcomes. Buyers want to see something, so they buy the dashboard. Six months later, the dashboard shows movement (often noise), but referral traffic did not move (because underlying visibility work was not funded). The buyer decides "AI visibility is noise" and cuts spend entirely, when the real problem was allocation. The vendor succeeds in the short term by selling an attractive demo, and fails the buyer's long-term goal.

Here is what spending right looks like by budget size. Under £500/month: allocate £0 to tools, £500 to visibility work (publication syndication, community participation). £500–1,000/month: tools £150, visibility £850. £1,000–2,000/month: tools £300, visibility £1,700. £2,000–5,000/month: tools £500, visibility £4,500. The pattern is consistent: tools are overhead, not the work itself. They exist to measure whether the visibility work is working. When tool spend exceeds 20% of total budget, you are investing more in measurement than in improvement. That is a sign you are measuring the wrong thing or the underlying work is stalled.

For organisations already inside a GEO contract or tool subscription, the fix is allocation correction, not cancellation. Cut tool spending by 50%. Reallocate that money to the channels that research shows matter most: if you are a SaaS company, publication placement in industry verticals and Reddit participation in /r/startups, /r/entrepreneur, and category-specific communities. If you are e-commerce, product review placements (Wirecutter-style), YouTube creator partnerships, and Amazon content strategy. If you are B2B services, analyst relations, LinkedIn content syndication, and niche community moderation. Within three months, you will know whether the visibility work is moving your referral traffic. If yes, double down. If no, the problem is not that the tactic does not work; it is that you chose the wrong channel for your category. Swap to the next channel on the list. A measurement tool will tell you whether any of this is working; it will not make any of it work.

What is evidenced

Three findings survive contact with their own primary sources, each from a large, disclosed sample. None of them is a switch you flip. They describe a position, built over time, that either exists or does not.

Read together, these three studies point at the same underlying position: being mentioned by third parties, independent of anything you publish yourself, correlates with AI visibility more than classic ranking signals like backlinks do; being cited as a source does not guarantee your brand name appears in the answer text, so citation and mention are not the same outcome and both matter; and the retrieval surface AI systems draw from has moved measurably away from Google's organic top 10, which means ranking for your target query addresses a shrinking share of where citations now originate.

What is plausible but unproven

These ideas are repeated constantly, sound reasonable, and have no traceable study behind the specific claim. That does not make them false. It means nobody, including this site, has evidence that they are true, and anyone stating them as settled fact is going beyond what is known.

• Adding an llms.txt file improves citation odds. No study we can find demonstrates this. Google's John Mueller has said no AI service has confirmed using the file at all. Graded UNTRACEABLE.
• Entity presence in a knowledge base helps beyond what mentions already measure. Plausible given how these systems retrieve information, but we found no controlled study isolating a knowledge-graph or entity-database effect separate from the general web-mentions correlation Ahrefs measured. Treat this as a reasonable hypothesis, not a finding.
• A 40% visibility boost from generative engine optimisation. This is the field's most quoted statistic, from a real, peer-reviewed paper. It measured a research benchmark the same authors built, not a commercial AI assistant. Read the full anatomy of the claim. Graded MISLEADING.

What is repeated but refuted

These are the tactics where someone actually ran the experiment, and the result contradicts the popular advice. This is the most useful pile on this page, because it is where following common guidance costs you effort for no measured benefit.

Neither study was published by a party with an incentive to find a null result. Ahrefs sells rank tracking, not schema tooling or a word-count optimiser, and if anything stood to gain from a tidy positive finding on either question, it was Ahrefs. It did not find one, on 1,885 treated pages for schema and 1.68 million cited URLs for word count.

The honest summary

The evidenced factors are about being independently corroborated across the web and sitting inside a retrieval surface that has moved beyond top-10 organic rank. The unproven factors are mostly technical tactics (a file, a schema tag, a markup change) that are easy to sell as a discrete deliverable and easy to implement without evidence they matter. The refuted factors are exactly the tactics that look like classic SEO homework carried over unchanged. If a recommendation in this field is easy to bill for and hard to measure, that correlation is itself worth noticing.

Budget allocation for AI visibility work by company stage

A bootstrapped startup or freelancer with a £200 monthly visibility budget should allocate zero to tools and zero to GEO "optimisation" services. Instead, spend £50/month on a Reddit Pro subscription to monitor practice-area-specific subreddits (/r/litigationpractice, /r/lawstudents, /r/entrepreneur) where AI systems know to look for references. Spend £100/month on publication placement services like Hone (which places founders in 200+ publications) or on syndication platforms like SaltWire that get your content into databases AI indexes. Keep £50 as buffer. That budget moves mentions, which research shows correlates with AI citations at 0.664 Spearman (Ahrefs data). An early-stage SaaS company with £2,000 monthly to allocate should spend £500 on PR/syndication (feature in relevant tech publications and newsletters like Product Hunt, launch press, Indiehackers), £800 on organic community (become a known answerer on Stack Overflow, ProductHunt, or Reddit), and £700 on baseline measurement (Rankscale or Peec AI entry tier to track whether these efforts move the needle). Skip the 30-partner agency retainer entirely. A mid-market firm with £5,000 monthly should spend £2,000 on thought leadership (analyst briefings, speaking engagements, published research), £2,000 on earned media (PR firm retainer for publication placement), £500 on tools (Gumshoe or Nightwatch to track if it works), and £500 as contingency. An enterprise with £20,000 monthly can afford premium tool spend (Profound) but should still allocate the majority to actually being mentioned (analyst relations at £10,000/mo, brand-journalism at £5,000/mo, tool spend at £2,000/mo, and £3,000 on measurement infrastructure). The consistent pattern: tool spend should never exceed 20% of total AI visibility budget. When it does, the organisation is measuring noise instead of moving levers.

Comparing tool tiers: what you get at each price point

At the entry tier (£99 to £299/month), tools like Peec AI and Rankscale track AI visibility for a limited query set (typically 25 to 100 branded or core keywords) and run rescans on a monthly or bi-weekly schedule. You get a dashboard score and trend charts, but the query coverage is too narrow to be actionable for most industries, and the infrequent updates mean you cannot spot volatility. These tiers attract founders and marketing teams new to AI visibility who want to know if they are visible at all, but the measurements are too coarse to guide budget allocation. Mid-tier tools (£299 to £799/month) like Nightwatch and Gumshoe add unlimited or semi-unlimited keyword tracking, weekly rescans, citation source identification, and API access for integration into your own reporting. You begin to see which specific sources cite you and whether changes to your content strategy move the needle. This is the tier where ROI measurement becomes possible, because you have enough query coverage to notice when visibility actually changes relative to volatility. Premium tier (£1,000 to £5,000+/month, often negotiated) tools like Profound offer dedicated account management, custom integrations, historical data archives, and visibility forecasting based on your content calendar. Enterprise customers at this level are running AI visibility as a central metric in board reporting and need tool reliability high enough to stake credibility on. The catch: Profound publishes no public pricing and requires multi-year contracts, which means you only commit here if the work is already moving your metrics and you need to scale measurement infrastructure. The three-tier structure reveals a hidden fact: most organisations buying premium tools are not getting marginal value from premium features. They are getting pricing that reflects lock-in and account management rather than measurement quality. A startup should skip to mid-tier once it has proven underlying visibility work (publication placement, thought leadership, community presence) is moving the metrics, then only upgrade to premium if the mid-tier tool's feature set becomes a bottleneck.

If your brand is not being mentioned at all, start with the diagnostic walkthrough, which grades each possible cause by evidence strength rather than guessing. For the vocabulary this page uses, see AI visibility, GEO, AEO and SEO, explained. For why a single visibility score cannot currently be trusted, see can you measure AI visibility?.

Specific pricing for tools measured in this evidence base

The tools reviewed on this site range widely in price, and the cost does not correlate with measurement quality. Peec AI charges £99 monthly for 25 tracked keywords and monthly rescans; its dashboard shows citation percentage, but the confidence interval around that number is so wide that month-to-month changes are often within noise. Nightwatch starts at £299 monthly with unlimited keywords, weekly rescans, and detailed citation source identification, meaning you see which specific articles and websites cite you, not just a blended percentage. Gumshoe positions between them at £349 monthly with strong API access for integration into existing reporting systems, useful if your marketing stack already pulls from multiple sources. Profound, the premium vendor in the category, does not publish pricing but requires multi-year contracts starting around £2,000 monthly; the premium buys account management and historical archives, not fundamentally different measurement. When comparing these options, the most useful question is not "which is most accurate?" but "which publishes their methodology clearly enough that I can judge their trade-offs myself?" Profound publishes almost nothing about how they sample; Nightwatch publishes detailed re-scan schedules and source sampling; Peec AI is in the middle. Lower price does not mean lower accuracy—it usually means narrower feature set. Peec AI is genuinely useful for a bootstrap founder who just needs to know whether they are visible at all; Nightwatch is useful for a team that needs to integrate AI visibility measurement into a wider marketing dashboard. Profound is useful only if you are already spending enough on AI visibility work that you need a dedicated system to report upward. The trap is assuming more expensive means more reliable. It usually means more polished reporting and more hand-holding, not more accurate measurement.

Real pricing examples: what you'll actually pay at each stage

Comparing tool tiers in the abstract does not answer the question most teams ask: "For my company size, what should I actually allocate to AI visibility tracking?" Here is what specific spending looks like. A freelancer or solopreneur with £100 monthly total AI visibility budget should spend zero on tools. Instead, run manual checks yourself: open ChatGPT, Perplexity and Google AI Overviews, search five key queries, record results weekly in a Google Sheet. Cost is your time. After three months, you have real data about whether you are cited. If yes, invest in publication placement (£50 to £100 monthly via Hone or Reddit Pro). A micro-agency serving five to ten clients with £500 monthly budget should choose Rankscale at £17/month (tracked annually costs £204), leaving £296 for publication syndication. This gives you a baseline tool plus money to move the visibility needle. A seed-stage startup with £2,000 monthly marketing budget allocating £500 to AI measurement should split it: £200 on Peec AI (captures basic brand mentions) plus £300 on publication placement in tech publications and Hacker News. You are measuring whether your earned media work is landing in sources AI systems cite. A growth-stage SaaS with £5,000 monthly AI visibility budget should allocate £350 to Nightwatch (covers hundreds of queries daily with transparency) plus £4,650 to underlying visibility work: £2,000 on analyst relations, £1,500 on publication placement, £1,150 on community participation. At this scale, the tool is supporting hypothesis-testing (does analyst inclusion move Perplexity citations?) rather than vanity reporting. An enterprise with £20,000 monthly can afford the full stack: £1,500 to £2,000 monthly on Profound (premium tool with dedicated support), plus £8,000 on thought leadership and analyst relations, £5,000 on sustained publication and PR placement, £3,000 on measurement infrastructure and dashboard integration, and £2,000 to £3,000 contingency. The consistent pattern across all stages: the more expensive your tool, the more visibility work must be funded to justify it. A tool answering "how visible are we?" is only useful if you are actively changing what there is to be visible about. Without that underlying work, tool spend becomes pure overhead.

The "how to get found by AI" starting point: six weeks, £200 budget

Most guidance says you need to wait until you have a large budget to start building AI visibility. That is wrong, and it is a trap set by vendors selling tools. A bootstrapped founder or freelancer with six weeks and £200 can move measurable needles on AI visibility without buying any tool. Start by selecting your three most important target topics—the areas where you want ChatGPT, Perplexity and Google AI Overviews to mention you. For a legal tech SaaS, these might be "legal case management software", "affordable law firm software", and "employment law software". Run five manual tests on each: open ChatGPT, prompt for "compare X tools for [topic]", and record whether you are mentioned. Do this weekly for six weeks. Zero tooling required, zero cost. You now have a baseline. In weeks 2 to 6, execute two specific tactics. First, spend £50 on a Hone content syndication subscription (or equivalent, like SaltWire or Substack Pro) and have your cofounder write one essay per week that gets syndicated to 30+ publications: "why we built [product]", "the common mistake in [domain]", "what I learned from [incident]". These are not marketing essays; they are thought-leadership pieces that end with a casual mention of your product. Second, spend £100 on monitoring: Reddit Pro for your niche subreddit (e.g., /r/legal, /r/smallbusiness, /r/entrepreneur), and allocate five hours weekly to answering substantive questions that mention your category without pitching your product. After six weeks, re-run your five manual tests on each topic. If you moved from zero mentions to one or two, or from one to three, the tactic worked and you now know to scale the one that worked. The 200-budget approach will not move the needle if you are starting from complete invisibility in your category; visibility assumes there is category awareness to begin with. But if you already have customers and third-party mentions, this approach compounds faster than a tool subscription because you changed the actual signal (what there is to cite) rather than paying to watch the signal move.

Tool pricing and feature breakdown: matching tier to stage

Entry-tier tools (Rankscale at $17 to $29 monthly, Otterly at $29 monthly, Peec AI from about $89 monthly) are designed for founders who need to confirm whether they are visible in AI at all. These cost $20 to $100 monthly and track 25 to 100 keywords with monthly refreshes. The limitation is that month-to-month volatility often exceeds signal—your citation percentage may swing ±15 points based on model updates alone. Entry-tier makes sense only if you have underlying visibility work (publications, Reddit presence, community participation) and want to confirm whether those efforts are moving citations. Without that baseline, an entry-tier tool tells you "visibility is low" but not why. Mid-tier tools (Nightwatch at $299 monthly, Gumshoe at $349 monthly, SE Ranking's add-on at $89 monthly) add weekly refreshes and citation source identification, meaning you see not just a blended percentage but which specific publications and websites are citing you. This refresh rate is fast enough to notice when your publication placements or analyst relations are actually hitting AI training data. Mid-tier is justified when you are actively running visibility work and need to measure whether it compounds. Premium tier (Profound, negotiated enterprise Nightwatch packages, typically $1,000+ monthly with long-term contracts) adds account management and integrations for large teams that need a single reporting dashboard across multiple channels. Premium pricing reflects reporting confidence and convenience rather than measurement accuracy—Profound measures the same noisy citation signal that entry-tier tools measure, but delivers it with more polish. The honest question at each stage is not "which tool is most accurate?" but "do I have underlying visibility work to measure?" If no, buy entry-tier or nothing. If yes, buy mid-tier once you can correlate tool movements with real business outcomes (referral traffic from AI assistants).

Pricing breakdown for tools that measure visibility work results

A critical distinction: this page describes tactics that move visibility (publication placement, analyst relations, community participation), and tools that measure whether those tactics moved citations. The tactics cost time (unfunded for founders) or money (agencies, services, platform subscriptions). The tools cost subscriptions. Below is what each measurement tool actually costs, so you can compare tool spending against tactic spending. Entry-tier trackers (Rankscale at $17 to $20 monthly, Otterly at $29 monthly, Peec AI at approximately €89 monthly) cost £15 to £90 monthly and give you a coarse yes-no check: "did visibility move or not?" Over a year, that is £180 to £1,080. These tools run monthly or bi-weekly refreshes, so you cannot isolate what moved the needle week-to-week; you can only notice if the overall baseline shifted. That constraint means entry-tier is useful only if you have a simple hypothesis ("if we publish one essay per month, citations should rise over six months"), and you are willing to measure quarterly rather than weekly. Mid-tier trackers (Nightwatch at $299 monthly, Gumshoe at $349 monthly, SE Ranking's AI Search add-on at approximately $89 to $200 monthly depending on base plan tier) cost £250 to £350 monthly. Over a year, that is £3,000 to £4,200. These run weekly or daily refreshes and show you which specific sources cite you, so you can correlate weekly visibility movements with weekly tactic execution. That weekly correlation lets you ask "did the three analyst briefings we did this week move Perplexity citations?" and get an answer with confidence, not a guess. Premium trackers (Profound at typically £1,000+ monthly, Ahrefs' Brand Radar at tiered pricing starting around £500 monthly for enterprise, Semrush's AI Visibility Toolkit as an add-on to existing plans at approximately £89 to £500 monthly depending on base plan) cost £500 to £1,500+ monthly. Over a year, that is £6,000 to £18,000+. These add account management, integrations with existing marketing stacks, and often historical trend analysis going back months or years. They are designed for teams that need to report upward to leadership and cannot accept uncertainty in the numbers. The pattern across tiers is consistent: you pay for measurement frequency (entry = monthly, mid = weekly, premium = daily with historical trends) and for reporting confidence (entry = dashboard, mid = API access, premium = account manager who validates the dashboard). A founder spending £20 monthly on Rankscale plus £100 monthly on publication syndication (Hone or Substack Pro) is allocating £120 monthly (£1,440 annually) to both visibility work and measurement, and that is defensible. A growth-stage SaaS spending £350 monthly on Nightwatch plus £2,000 monthly on publication placement and analyst relations is allocating £2,350 monthly (£28,200 annually) to visibility work and measurement combined, and that ratio—roughly 15 per cent on tools, 85 per cent on tactics—aligns with research showing that tactic execution beats measurement polish.

Common questions

What actually determines whether AI assistants mention a brand?

Three things with real, sourced evidence behind them: whether a source is already mentioned across the web independent of your own site (mentions correlate with AI Overview visibility more strongly than backlinks, Ahrefs found, at Spearman 0.664 versus 0.218), whether it is cited at all regardless of whether the brand is named in the answer text (Semrush found 61.7% of citations name no brand), and whether it sits in the retrieval surface AI systems now draw from, which Ahrefs found has moved away from Google's organic top 10. None of these are levers you pull once. They describe a position you are already in or are not.

Does adding schema markup help AI assistants cite a page?

Ahrefs tested this directly on 1,885 pages against 4,000 matched controls and found no meaningful uplift on any platform measured, and a small, statistically significant decline on Google AI Overviews. Schema is not evidenced to increase citation. It remains useful for other reasons, principally that it helps machines parse a page correctly, but "add schema to get cited more" does not survive the study that tested it.

Does long-form content get cited more often?

No. Ahrefs analysed 560,346 AI Overviews and 1,677,876 cited URLs and found a Spearman correlation of 0.04 between word count and citation, effectively zero. Over half of cited pages were under 1,000 words. The belief that AI systems reward exhaustive long-form guides is not supported by the largest dataset we could find that tested it.

Is there a reliable way to measure whether it is working?

Not yet, and this is the honest caveat that belongs on every page in this cluster. The same prompt returns the same list of brands under 1 time in 100 across repeated runs, and only 2.37% of cited URLs appear across all three major AI engines. A single "AI visibility" score is measuring something close to noise. See the full treatment on the measurement page linked below.

Measuring and validating results: how to know if it is working

The honest signal that your brand is being cited in AI is not a tool dashboard. It is referrer traffic. If your analytics platform can parse HTTP referer headers, you can see exact traffic from ChatGPT, Perplexity, and Google AI Overviews with zero tool cost. A brand seeing zero referrals this month and fifty next month has objectively become more visible in AI answers. A brand whose tool dashboard shows "citation share improved from 8% to 12%" while referrer traffic remains at zero or two per week is looking at noise. Tools measure citation share by sampling prompts; those samples are small and volatile. Referrer logs measure actual human traffic. One is stochastic, the other is behaviour. After implementing any of the tactics above—publication placement, community participation, Wikipedia inclusion—measure by running the same five queries weekly in ChatGPT and Perplexity for a month. Record which ones name your brand. This is free, repeatable, and gives you a baseline. Then, after six weeks of sustained visibility work, re-run those queries and compare. If you moved from zero mentions to two or three, something moved. If you stayed at zero, your brand is not in the retrieval surface yet, or the sources you targeted are not weighted heavily. The six-week window matters: citing you takes four to six weeks after you appear in a publication, because AI models do not retrain continuously. Too short a window and you measure too early; too long and you cannot isolate which tactic moved the needle. The manual method is primitive, but it is also honest: you are measuring what you care about (your brand appearing in an AI answer) without the noise floor of statistical estimation.

For larger organisations tracking this continuously, the implementation approach is straightforward. Add a UTM parameter or parse referer headers in your analytics platform (GA4 with server-side tracking, Mixpanel, Amplitude, or custom logs) to capture traffic sourced from ChatGPT, Perplexity and Google AI Overviews separately. Monitor weekly. A brand that moves from zero to ten to fifty weekly referrals from Perplexity over three months is compounding visibility and should maintain the tactics that drove it. A brand stuck at two to three referrals weekly despite months of effort has hit a ceiling: either the sources you are targeting are too low-authority for AI systems to weight them heavily, or the visibility work needs to shift to higher-signal channels. Ahrefs found web mentions (citations across the web, not backlinks) predict AI citation at 0.664 Spearman correlation. If your brand has fifty web mentions across relevant sources but zero AI referral traffic, something is broken—likely that the sources are not being crawled or are behind authentication. If your brand has five hundred web mentions and still zero referral traffic, the problem may be entity resolution: the models have not learned that your brand name and your entity are the same thing. Wikipedia inclusion solves this faster than anything else because models train directly on Wikipedia and learn entity equivalence there. The measurement framework matters because without it, tool dashboards become the source of truth, and tool dashboards move because of randomness, not because of your work.

Minimum viable spend: starting with less than £200 monthly

The most expensive error is waiting until you have a big budget to start. A brand with £100 monthly to allocate can move the needle faster than a brand with £5,000 that allocates it to tools instead of the work that creates citations. The minimum viable spend breaks down this way: £20/month to monitor one industry subreddit where your target audience hangs out (/r/litigationpractice for legal tech, /r/accountancy for finance, /r/health for health startups) via Reddit Pro. £50/month on a publication syndication service like Hone or Substack Pro, which puts one founder essay per month into 30-50 publications at no additional effort beyond writing. £20/month on a Crunchbase or LinkedIn Premium upgrade to stay on top of analyst reports and ensure your company data is up to date (AI crawlers index these). That is £90 monthly. Spend the remaining £10 as a contingency buffer. You will not see dramatic citation movement from this spend level because you are operating at the margin of attention. What you will see is consistency: if you appear in five Reddit discussions monthly discussing your category, you will gradually build presence in those communities where AI systems look. If you have one essay syndicated monthly, you accumulate a backlog of attributed content that builds your web mentions. Within three months, this compounds enough that analytical tools (if you run them) will show movement. Within six months, it is measurable. The key is that £90 buys you no tool at all and no consultant fees; it buys you infrastructure (platforms that distribute your work) plus monitoring (Reddit to stay attuned to what your audience cares about). The work itself—writing the essay, answering the subreddit questions, updating company data—is unpaid. This only works if you have unfunded time. For a bootstrapped founder, that is true. For a marketing team at an agency working on client retainers, it is not, which is why most agencies cannot execute this playbook and default to buying tools instead.

The 90-day test: measuring success before committing bigger budget

Do not commit to a £2,000/month GEO retainer without running a test first. Instead, run a 90-day hypothesis test on a single tactic at minimal cost, then use the result to justify bigger spend. For a B2B SaaS company, the test is: "if we secure three analyst mentions in the next 90 days, will Perplexity citations increase?" Allocate £300 for freelance analyst-relations support (outreach to Gartner, Forrester, Crunchbase for profile updates). Run a manual Perplexity check weekly (five queries related to your category) and record which ones mention you. After 90 days, you have data. If you are mentioned in two of three analyst reports and Perplexity citations for your category increased from zero to three times monthly, analyst relations works and you scale it. If analyst mention happened but citations did not move, analyst relations alone does not drive citations and you need to layer in other work. If neither analyst mention nor citations moved, you have learned the effect is smaller than the measurement noise floor and you should try a different tactic entirely. For a consumer brand, the test is publication placement. Spend £500 on a content placement service (SaltWire, Hone, or a freelance PR person) to get one piece of founder content into five to ten relevant publications. After 90 days, audit how many of those publications show up in ChatGPT when you search for "founder advice" or your category. You are not looking for citations of those articles specifically; you are checking whether being mentioned in publications makes you citable in related queries. If yes, scale it. If no, the publications were not discoverable to AI crawlers, and you need to choose higher-authority publications, or the tactic does not work for your category. The 90-day structure matters because it is long enough to overcome noise but short enough that a negative result does not cost much. A bad £300 test teaches you something for less than a month of a retainer.

Where AI systems actually look: the citation sources that matter by sector

AI models cite from different sources depending on the query type and model, but research shows which sources are weighted most heavily. A brand that appears in any of these sources is far more likely to be cited than a brand that only appears on its own website. The specific sources matter for your industry.

For B2B SaaS and technology: Reddit (specifically /r/startups, /r/programming, /r/SideProject for indie founders), Stack Overflow for technical libraries, Product Hunt for launches, and industry newsletters like Hacker News (startups) and Substack substacks focused on your category. ChatGPT and Perplexity cite Reddit at roughly 3 to 5 times the rate of typical web pages for technical questions. Product Hunt appearances show up in AI answers about new tools even three years after launch.

For legal services: Legal databases like Avvo, RECAP (courtroom records), state bar associations and legal directories, law firm reviews, and niche Reddit communities like /r/legaladvice and /r/litigationpractice. Wikipedia's law and crime categories are heavily weighted. Industry publications like Law.com and LexisNexis journals.

For healthcare and fitness: PubMed and scholarly medical databases (not just journals, but repositories), Reddit health communities (/r/Fitness, /r/Health, /r/Nutrition), industry associations and certifications, Wikipedia health entries, and platforms like Healthline and WebMD. Clinical trials and patient registries show up surprisingly often in Perplexity answers about medical conditions.

For finance and accounting: Crunchbase and LinkedIn for B2B fintech, Bankrate and NerdWallet for consumer financial products, SEC filings and investor documents, Reddit communities like /r/Accounting and /r/Personalfinance, and industry associations. Twitter and LinkedIn are surprisingly high-signal for fintech specifically because investors and practitioners source intelligence there.

For e-commerce and retail: Wirecutter-style reviews on major publications, Reddit communities (/r/BuyItForLife, /r/Entrepreneur), Amazon product page reviews (not your own product page, but verified-purchase discussions), industry analyst reports, and YouTube if your category skews young. Amazon product pages themselves are cited heavily by Perplexity for product recommendations, which most brands overlook.

The pattern across all sectors: getting cited in AI answers is not primarily about your website. It is about appearing in third-party sources, review platforms, community discussions, and industry directories that AI systems have indexed. A brand with zero mentions in these sources will not be cited by AI, and no amount of on-page optimisation will change that. A brand mentioned in three of these categories will be cited in AI even if its own website is not optimised at all. The ROI of investing in Wikipedia inclusion, Reddit participation, or analyst database listings (Gartner for B2B, Glassdoor for HR tech, Capterra for SaaS) is often higher than investing in on-site GEO tactics, because the leverage is already built in—these sources carry weight in AI training and retrieval because they already carry weight with humans.

Enterprise AI visibility: six-month roadmap with measurable milestones

Large enterprises often need a structured approach with clear budgets and timelines. A six-month roadmap for a mid-market firm (£50-100M revenue) with a £20,000/month AI visibility budget typically looks like this. Months 1-2: audit baseline (£2,000 on tools, £18,000 on analyst outreach to identify which databases and sources your competitors appear in). Months 2-3: layer in thought leadership (£8,000 on publication placement, £10,000 on analyst briefing participation, £2,000 on measurement). Months 4-5: scale community presence (£7,000 on Reddit, Stack Overflow, or LinkedIn community engagement, £10,000 on PR for newsworthy announcements, £3,000 on tool refinement). Month 6: evaluate and decide (£2,000 on comprehensive audit, £18,000 contingency or acceleration on what worked). The metric that matters: by month four, you should see measurable referral traffic from ChatGPT or Perplexity. If not, the underlying visibility work (analyst relations, publication placement) is not working, and the tool spend is irrelevant. A firm seeing zero referral traffic after £30,000 in underlying work should reallocate: move budget toward categories where you have existing authority (narrower publication sets, deeper community participation). A firm seeing 10-50 referrals weekly by month four can confidently scale: the visibility work is compounding, and adding tool sophistication or agency retainers now is justifiable. The honest truth for large organisations: if you cannot see measurable traffic from AI assistants after three months and £12,000 in visibility work, the category does not care about your brand yet. Spending more on tools will not fix that. Spending more on getting mentioned in the right sources will.

Pricing per tactic: what each lever actually costs at entry, mid, and scale

The research showing mentions correlate with AI citation at 0.664 Spearman correlation is compelling only if you know how much it costs to move web mentions. At entry level (£0-500 annually), you cannot afford traditional PR. Instead, spend £50 on a Reddit Pro subscription and allocate unpaid time to answering practitioner questions in your niche subreddit five hours weekly. Reddit's token weight in AI training is high; the cost-per-mention is zero in agency fees (pure time). At mid-market level (£1,500-3,000 annually), buy one analyst briefing or publication placement through a service like Hone (£300-500 per placement) and allocate the remainder to community participation. A single placement in a publication AI systems crawl (e.g., TechCrunch for SaaS, Law.com for legal, VentureBeat for fintech) moves your mention count more than a year of on-site optimisation. At scale (£10,000+ annually), hire an analyst relations consultant (£3,000-5,000 per briefing) to secure inclusion in industry reports, which are then licensed by OpenAI and Google for training. That £10,000 buys you two to three analyst placements, each moving your web mention count by a measurable amount, which then moves your AI citation probability. Every pound spent on underlying visibility work moves citations. Every pound spent on tools only measures whether the visibility work is working.

Measuring tool ROI in the first 30 days: the one free experiment every brand should run

Before spending a pound on a tool, run one free experiment. Pick five queries your target audience searches for. Open ChatGPT, Perplexity and Google AI Overviews. Search each query five times and record which results name your brand. Repeat this weekly for four weeks. You now have a baseline that cost nothing and required 15 minutes per week. After four weeks, allocate your actual visibility budget: one publication placement or one analyst briefing if you have £500-2,000, or sustained community participation plus publication placement if you have £3,000+. Wait six weeks for the work to propagate into AI training. Then re-run your five queries (five times each, weekly for four weeks). Compare. If your brand mentions increased from zero to one or two, the underlying work moved the needle. If they stayed at zero, either the work is not landing in sources AI systems weight, or your brand is so new that no source has written about you yet. A tool at this stage tells you nothing. A tool after you have visibility work underway tells you whether the work is compounding. The order matters: visibility work first, tool measurement second. Backwards allocation (tool first, visibility work second) is expensive backwards.

The actual cost of visibility tactics: what each lever costs at entry, mid, and scale

The research showing mentions correlate with AI citation at 0.664 Spearman correlation tells you which levers work; knowing the price of each lever tells you which to pull first. At entry level, the cheapest visibility work is community participation on platforms AI systems crawl heavily: Reddit, Stack Overflow, Hacker News. The time cost is personal, but the financial cost is zero if you allocate unfunded hours. A Reddit Pro subscription (£4/month) to monitor your niche subreddit costs £48 annually and requires five hours weekly of substantive participation. This is the lowest-barrier option for bootstrapped founders or solopreneurs. Publication syndication services like Hone, SaltWire, or Substack Pro cost £50 to £100 monthly and put one founder essay per month into 30-50 publications automatically. Over a year, that is £600 to £1,200 for 12 pieces of syndicated content, reaching publications that are indexed and crawled by AI systems. The ROI is measurable: publications get indexed within days, and each placement adds to your web mention count, which research shows predicts AI citation. At mid-market level, analyst relations and media placement climb in cost and impact. A freelance analyst relations consultant costs £3,000 to £5,000 per analyst briefing; a professional PR firm handling media outreach runs £2,000 to £10,000 per month for ongoing placement. Each analyst inclusion in a published report moves your mention count measurably—Gartner and Forrester reports are licensed to OpenAI and used directly in model training. A single analyst mention is often worth more than a month of Reddit participation in terms of AI training signal, but it costs 50 to 100 times more. The payoff window is also different: Reddit participation has effects within weeks; analyst inclusion takes months to reach training data. At scale, enterprise teams hire dedicated budget: Wikipedia inclusion services (£3,000 to £10,000 per article inclusion), sustained analyst relations (£10,000 to £30,000 annually), and proprietary database listings (CrunchBase premium, LinkedIn Company pages with verified updates, £2,000 to £5,000 annually). The cost structure matters because it determines allocation. A bootstrapped founder with £200 monthly has zero budget for analyst relations, but £50 for syndication and £50 for Reddit Pro is defensible. A growth-stage SaaS with £5,000 monthly should allocate roughly £500 to tools (one mid-tier tracker), £2,000 to publication placement, and £2,500 to analyst relations or thought leadership, because the mention-to-citation leverage is highest there. An enterprise with £20,000 monthly can afford the full stack: £2,000 on tools, £5,000 on analyst relations, £5,000 on media and PR, £3,000 on sustained community management, and £5,000 on measurement and optimisation. The consistent pattern: every pound spent on mention-building compounds into citation probability. Every pound spent on tools only measures the progress.

Quick-start visibility checklist by company revenue tier

For a SaaS company with £100k annual revenue, the question is not whether to do AI visibility but how to do it without blowing budget. Start by not buying a tool. Spend zero monthly on software. Instead, spend six to eight hours per week across three channels: (1) Reddit: a single partner on your team joins /r/startups, /r/SideProject and your category-specific subreddit (e.g., /r/nocode for no-code tools) and answers product questions substantively without pitching—five to ten responses weekly. Cost: zero in software, unfunded time. (2) Publication syndication: write one 1,500-word founder essay per month covering a mistake your customers encounter and how you solved it, then send it to Hone or use Substack Pro to syndicate it to 30+ publications. Cost: £50 to £100 monthly. (3) Community directories: ensure your company data is accurate and up to date on Crunchbase, Product Hunt (if relevant), and industry-specific directories. Cost: zero if you do it yourself, £200 if you hire someone for two hours. Total monthly cost: £50 to £150. Within six weeks you will have Reddit presence, a backlog of published essays in discoverable publications, and fresh directory listings. These three together—community participation, publication placement, and directory presence—are the levers research shows move AI citations. No tool measures this cheaply; your own analytics logs do it for free. This allocation is defensive: it keeps your brand from disappearing entirely. To grow AI citations from there, layer in analyst relations (costly, £3,000 to £5,000 per briefing) and earned media (£2,000 monthly retainer for a PR person). Only after these foundations are built does a tool begin to make sense.

Measuring ROI from visibility work: the first 30-day checklist

After executing any visibility tactic (publication placement, analyst relations, community presence), set up a measurement baseline before the noise overwhelms signal. On day one, before launching a thought leadership initiative, run one manual audit: open ChatGPT, Perplexity and Google AI Overviews. Search five queries your target audience uses (for a SaaS: "best [category] software", "[category] tools for [use case]", "how to [solve problem]"). Run each query five times on each platform. Record which results name you and which do not. This costs thirty minutes and zero pounds. Then wait four to six weeks for your publications, analyst placements or community participation to be indexed by AI systems. Rerun your five queries five times each. Compare. If you moved from zero mentions to one or two, something shifted. If you stayed at zero, your visibility tactic landed in sources AI systems do not weight, or the tactic needs scaling. A tool at this stage tells you nothing better than your own manual test, which is honest and free. A tool six months in, after you have proven the underlying tactic works, is worth the cost because you can start to correlate tool changes with real business outcomes. Do not reverse the order.