Rikard Jonsson
Rikard Jonsson is Founder & CEO of Hey Sid and a five-time entrepreneur with a background in B2B SaaS, sales, and brand building. He believes B2B marketing is overcomplicated and writes about going back to basics: visibility, positioning, and consistent presence among the accounts that matter.
AI Visibility: See If B2B Buyers Find You
Quick answer: AI visibility tells you whether buyers find, trust and shortlist your B2B company inside answers from ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews. To measure it, test real buyer questions, record mentions and citations, score recommendation quality, compare against competitors, then connect the findings to content, sales enablement and pipeline reporting.
What you will learn
By the end of this guide, your team should be able to:
Build a repeatable test set: Turn sales questions, category searches and comparison prompts into a monthly audit.
Separate mentions from real demand: See the difference between being named and being recommended.
Find content gaps: Identify which pages AI assistants cite, ignore or misunderstand.
Report clearly: Give leadership a simple view of category presence, competitor position and buyer-stage risk.
What is AI visibility?
AI visibility is your company’s presence inside AI-generated answers that buyers use before they speak to sales. It covers whether an assistant names you, describes you correctly, cites your own pages or trusted third-party sources, and recommends you for the right use case.
Traditional search engine optimization asks, “Do we rank on Google for this query?” AI answer measurement asks a different question: “If a buyer asks an AI assistant who to consider, do we appear in the answer, and are we framed accurately?”
That distinction matters because AI assistants compress research. A buyer might ask one question such as, “Which B2B firms help industrial technology companies increase visibility in the UK and Europe?” The answer may include a short list, a summary, and sources. If you are missing, miscategorised or cited through weak pages, you may lose shortlist consideration before your sales team knows the deal exists.
LLM SEO is the practical work that helps large language models understand, retrieve and cite your company. It overlaps with ai search optimization, content strategy, digital PR, technical clarity and authority building. It does not replace normal SEO. It adds a new layer: making your expertise easy for answer engines to find, quote and compare.
Why AI visibility matters for B2B buyers
Senior B2B buyers increasingly use AI assistants as research partners. They ask for vendor lists, category definitions, procurement risks, comparison criteria and questions to ask sales teams. That means your public content can shape the buying process before a form fill, event scan or outbound reply.
For UK B2B teams, the risk is not only lost traffic. It is lost framing. If ChatGPT or Perplexity describes your company using outdated positioning, cites an old page, or recommends a better-known competitor for a use case you serve, your sales team starts behind.
Across the 150+ B2B companies we work with, reporting pressure keeps increasing. The approved first-party insight for this guide is simple: when teams ask for better reporting, they usually mean one of three different things: performance broken out by channel, reports they can hand straight to their boss, and a clear line from activity to pipeline. AI answer measurement needs all three.
Approach | What it tells you | Best use | Main limit |
Google rank tracking | Where your pages appear in search results | Traditional SEO reporting | Does not show AI answer wording |
Manual AI answer audit | Whether assistants name, cite and recommend you | Early visibility diagnosis | Needs tight prompts and repeat testing |
GEO monitoring platform | Ongoing share-of-answer tracking | Larger teams with internal owners | Reports the issue more than it fixes it |
Done-for-you service | Diagnosis plus content and authority execution | Lean B2B teams needing action | Less suited to teams wanting full manual control |
This is why ai visibility should sit beside pipeline, account engagement and sales feedback. It is not a vanity metric. It shows whether your market narrative appears where buyers now ask questions.
The Answer Presence Scorecard
The Answer Presence Scorecard is a simple audit framework for B2B teams. It helps you score whether AI assistants can find you, describe you, trust you and recommend you for the right buyer situation.
Use it once as a baseline, then repeat monthly. Score each prompt from 0 to 2 in each category. A score of 0 means absent or wrong. A score of 1 means present but weak. A score of 2 means clear, accurate and useful. Run the audit with your own prompts and target markets. Do not treat the score as a universal benchmark.
Scorecard category | 0 score | 1 score | 2 score | What to fix first |
Mention presence | Not named | Named after competitors | Named in the main answer | Create clearer category pages |
Citation quality | No citation | Weak or outdated citation | Strong page or trusted third-party citation | Improve source pages and evidence |
Description accuracy | Wrong category or market | Partly correct | Clear ICP, offer and geography | Tighten positioning language |
Recommendation fit | Not recommended | Recommended for broad fit | Recommended for your best-fit use case | Add use-case evidence |
Competitor context | Competitors dominate | Mixed presence | Fair comparison with your strengths | Build comparison-ready content |
Commercial usefulness | No next step | Generic next step | Clear evaluation criteria for buyers | Add procurement and sales enablement content |
A practical scoring rule works well:
0-4 points: You are mostly invisible or misread.
5-8 points: You appear, but your evidence is thin.
9-12 points: You have usable AI answer presence and can improve priority gaps.
This framework supports llm seo because it turns a vague topic into specific fixes. Instead of asking “How do we get into ChatGPT?” you can ask “Which citations are missing for UK buyer prompts about our category?” That question leads to better pages, better proof and clearer reporting.
How to measure AI visibility, step by step
Use this process as a monthly audit. Keep the same prompts, regions and recording rules each time so the trend means something.
Define buyer-stage prompts: Start with 20-30 questions your buyers ask. Include problem prompts, category prompts, vendor prompts, comparison prompts and procurement prompts. For a UK industrial technology firm, examples might include “best firms for B2B demand generation in the UK” or “how to compare account-based marketing partners for manufacturing companies.”
Why it matters: AI assistants answer the question asked. Weak prompts create weak findings.
Common mistake: Testing only your brand name. Branded prompts show recognition, not category demand.Set a controlled testing method: Run each prompt in the same assistants: ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews where available. Use a clean session where practical. Record date, market, prompt wording, answer summary, citations and competitors named.
Pro tip: Keep prompts stable for trend reporting, but add a small test group each month for new sales objections.Record mentions and position: Note whether your company appears in the main answer, a follow-up, a cited source, or not at all. Position matters less than framing. A brief but precise recommendation can be more valuable than a long generic mention.
Common mistake: Treating every mention as equal. A citation without a recommendation has less commercial value.Check citation sources: Perplexity and Google AI Overviews often show citations. ChatGPT and Claude vary by mode and setup. Capture which pages are cited: your site, review sites, media, documentation, partner pages or outdated assets.
Why it matters: Citations show what the model can defend. If your best proof is not cited, the content may be hard to retrieve or weakly connected to the query.Score answer quality: Use the Answer Presence Scorecard. Score each prompt and group the results by buyer stage. A low score on comp
arison prompts tells you something different from a low score on problem prompts.
Warning: Do not average everything into one headline number. Senior buyers do not ask one generic question. Segment the score by buying task.Compare against competitors and in-house alternatives: Track who is named instead of you. Include named competitors, large consultancies, internal build suggestions and “use your existing agency” style answers. This gives your team the real shortlist context.
Common mistake: Ignoring in-house answers. For lean B2B teams, “do it internally” is often the quiet competitor.Turn findings into content and proof work: Map each gap to an action. Missing category presence needs clearer educational content. Poor recommendation fit needs sharper ICP pages. Weak citations need stronger proof, case studies, expert pages or third-party mentions.
Why it matters: Measurement alone does not improve visibility. LLM SEO becomes useful only when it changes what answer engines can retrieve.Report to sales and leadership: Share three views: where you are named, where you are missing, and which content actions are underway. If your CRM is HubSpot, record campaign exposure and engagement where your process allows, so sales can see which companies have met the brand before outreach.
Suggested visual: Add a flowchart showing prompts, answer capture, scoring, content fixes and monthly reporting.
Requirements for a useful AI visibility audit
A useful audit needs consistency more than volume. Ten well-chosen prompts tested every month beat one hundred random prompts tested once.
Use these requirements before you start:
A clear ICP: Define industries, company size, buying committee and geography. For UK and European campaigns, separate UK prompts from EU or EEA prompts where regulation, availability or buyer wording differs.
Sales input: Ask sales which questions prospects ask before meetings. These often reveal the prompts buyers give AI assistants.
Stable recording rules: Decide how you score mentions, citations and recommendations before testing.
Competitor set: Include direct competitors, agencies, in-house options and large platforms buyers already know.
Compliance review: If your audit leads to tracking, advertising or remarketing changes, involve your compliance owner. UK GDPR, PECR and ICO guidance matter for UK programmes. EEA campaigns also need opt-in consent before tracking fires.
Content ownership: Assign each gap to a page type: category, comparison, use case, case study, integration, methodology or sales enablement.
Monthly cadence: AI answers shift. Monthly review is usually enough for B2B teams unless a major product, market or regulatory change occurs.
For related execution, see Hey Sid’s guide to ai search optimization for B2B and the more detailed resource on generative engine optimization for B2B SaaS.
Best practices for LLM SEO and answer visibility
LLM SEO works best when it makes your company easier to understand, not when it chases tricks. Use these practices to improve retrieval and recommendation quality:
Write category pages in plain terms: State who you serve, what problem you solve, where you operate and when you are not the right fit.
Use evidence close to claims: Put case studies, customer outcomes, certifications and methodology near the pages that explain your offer.
Create comparison-ready content: Buyers ask AI assistants for trade-offs. Give them fair criteria, not self-praise.
Keep old pages current: AI answers often surface dated pages if they remain the clearest source. Refresh positioning, markets and service names.
Support named entities: Make company, product, service and leadership information consistent across your site and trusted third-party profiles.
Cover procurement questions: Include risk, compliance, implementation, ownership and reporting guidance. These prompts appear late in the buying process.
Connect AI visibility to sales reality: Review the prompts with sales. If an answer sounds good but sales never hears that question, lower its priority.
This is also where a content calendar helps. A strong calendar should not only assign topics. It should assign buyer-stage gaps, target prompts, evidence sources and expected reporting outputs.
Tools that help
You can start with a spreadsheet, fixed prompts and disciplined testing. That is enough for a baseline audit.
Specialist GEO monitoring platforms can help larger teams track more prompts across more assistants. They are useful when you have internal ownership, content capacity and a clear plan for acting on findings. They are less useful if the report sits in marketing with no execution path.
For broader tool selection, read Hey Sid’s guide to AI tools for B2B marketing. If your AI answer strategy also depends on executive visibility, the LinkedIn Marketing AI Visibility Playbook explains how social authority supports search and answer discovery.
Hey Sid sells done-for-you AI Search Optimization as a service, not a DIY tracking platform. That includes visibility analysis, high-intent content execution for traditional and AI search, monthly mention and citation monitoring, monthly AI share-of-voice reporting and pipeline reporting.
Where Hey Sid fits
This guide is published by Hey Sid, so the fit should be stated plainly.
Hey Sid fits mid-sized, sales-led B2B companies that need execution, not another reporting queue. The strongest fit is a 20-100 employee company with a clear ICP, a consultative sales process, and a lean marketing team that needs AI Search Optimization, ABM, authority building and outreach to work together.
Hey Sid is not right for every company. It is not a fit for B2C teams, very small budgets under $25K/year, early-stage companies without a defined target account list, organisations without a sales team, or teams expecting direct click-to-close attribution. It is also not for teams that need pipeline inside six weeks. The Influence Loop compounds over 60-90 days: Always On creates awareness, Authority Builder builds trust, and Precision Connect drives engagement with the same named individuals.
For RevOps teams using HubSpot, Hey Sid’s two-way integration writes ad impressions, clicks and engagement totals onto existing company records as Sid-prefixed properties. It also imports deals read-only each night and can push companies from the CRM into ad audiences, while leaving the CRM as the source of truth.
Common mistakes to avoid
Testing only branded prompts: Brand prompts show recognition, not whether new buyers find you during category research.
Changing prompts every month: If the test set keeps moving, the trend becomes noise.
Ignoring wrong descriptions: A mention that misstates your ICP, geography or offer can hurt more than absence.
Treating citations as endorsements: A cited page proves source use, not buyer preference.
Skipping compliance review: Tracking, consent and remarketing choices need UK GDPR, PECR and ICO review by your compliance owner.
Reporting one blended score: Split results by buyer stage, market and assistant to show where action is needed.
Measuring without execution: Monitoring does not create demand. Content, proof and authority work change the answer set over time.
Conclusion and next steps
AI answer engines now shape how B2B buyers research categories, compare firms and prepare for sales conversations. Your job is to know whether your company appears, whether the description is accurate, which sources are cited, and which gaps are costing shortlist presence.
Start with a fixed prompt set, run a monthly audit, score the answers, and turn the gaps into content and authority work. Keep traditional SEO, sales feedback and CRM reporting connected so leadership sees more than impressions or isolated rankings.
If you want the broader execution context, read Hey Sid’s guide to AI Search Optimization for B2B. If your content engine also needs a social authority layer, pair it with the LinkedIn Marketing AI Visibility Playbook.
Ready to see whether buyers find you in AI answers?
Hey Sid can audit your AI answer presence, identify the content and citation gaps, and execute the work needed to get your company named, cited and recommended in the right buyer conversations. The service is built for lean B2B teams that need action, not another report to maintain.
FAQ
How long does an AI visibility audit take?
A focused baseline audit usually takes a few working days once your ICP, competitor set and prompt list are ready. The longer work is not testing. It is agreeing which gaps matter commercially and assigning content, proof or authority actions. For B2B teams, repeat the audit monthly so the trend is visible.
What is the cost of improving AI visibility?
Costs vary by approach. A manual audit mainly costs internal time. A monitoring platform adds software spend and still needs people to act on the findings. A done-for-you service usually combines strategic analysis, content execution, monitoring and reporting. Hey Sid pricing is service fee plus ad spend, with a minimum commitment.
Can I measure ai visibility without a paid platform?
Yes. Start with a spreadsheet, fixed prompts and consistent scoring. Test ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews where available. Record mentions, citations, competitors and answer quality. A paid platform becomes more useful when you need larger prompt sets, trend reporting or multi-market coverage.
Does LLM SEO replace traditional SEO?
No. LLM SEO builds on normal search engine optimization, technical clarity, strong pages and trusted sources. AI assistants often draw from pages that are easy to understand and cite. Your Google visibility, content structure, third-party mentions and proof assets still matter. The difference is that the output is an answer, not only a ranked list.
How often should B2B teams test ChatGPT and Perplexity visibility?
Monthly is a practical cadence for most B2B teams. It is frequent enough to spot changes, but not so frequent that the team chases every answer variation. Run extra tests after a rebrand, major case study, new market launch, product change or competitor announcement.
Sources
Framework used: Answer Presence Scorecard. First-party insight used: reporting requests usually mean channel detail, board-ready reports and a clear line from activity to pipeline.
