AI Visibility Baseline
Find out what AI systems say before buyers reach your sales team
Your buyers are already asking ChatGPT, Perplexity, Gemini, Claude, and search-backed answer surfaces which vendors, tools, agencies, and products deserve their attention.
Those answers can shape a shortlist before anyone clicks your site, fills in a form, or speaks to sales.
The AI Visibility Baseline is a fixed-scope diagnostic for B2B teams that need to know:
- whether AI systems mention and cite your brand for commercially important questions;
- which competitors, publishers, marketplaces, or review sites are being used as sources of truth;
- whether answer quality helps your positioning or quietly undermines it;
- whether your technical foundation makes your evidence easy to retrieve, parse, and trust;
- which fixes should happen first.
You get an executive-ready report, a retained evidence pack, and a ranked action plan. You leave knowing whether to fix content, technical source quality, competitive positioning, or measurement first. No guaranteed rankings. No promised citations. No black-box theatre.
Book a baseline call and find your visibility gaps
Why it matters
AI answers now sit between your buyers and your website, shaping which vendors get noticed before anyone clicks.
A buyer can ask:
- "Who are the best agencies for Generative Engine Optimization?"
- "Which AI search visibility tools should a B2B SaaS team consider?"
- "What are the alternatives to our current SEO agency?"
- "How do I get my brand cited by ChatGPT and Perplexity?"
The response may cite your site, cite a competitor, summarise stale positioning, lift a third-party listicle, or omit you completely.
By the time that buyer reaches a vendor site, the shortlist may already be shaped by sources you do not own and claims you have not corrected.
Traditional SEO data is still useful, but it does not answer the whole commercial question. Ranking for a keyword is not the same as being named, cited, and accurately explained inside an AI-generated answer.
The baseline gives you the evidence to decide what to fix first before you spend on content, tooling, technical hardening, or a broader GEO programme.
What we measure
Visibility
We test whether your brand appears for the buyer questions that matter: category discovery, comparison, evaluation, problem-aware, and source-trust prompts.
The output separates simple mentions from stronger cited appearances, so you can see where the market can find you and where you are absent.
Citations and source paths
When an answer displays sources, we record the URLs and domains used to support the response.
This shows whether answer engines are relying on your owned pages, competitor pages, publishers, marketplaces, documentation, review sites, or other intermediaries.
Competitor presence
We map which competitors, alternatives, tools, agencies, publishers, or directories appear across the prompt set.
That reveals where competitors are being recommended, which sources support them, and which buyer questions carry the highest commercial risk.
Answer quality
Visibility is not enough if the answer is vague, stale, inaccurate, or missing the proof a buyer needs.
We review whether the answer represents your positioning correctly, whether claims are supported, and whether the cited sources actually back up the recommendation.
Technical retrievability
AI visibility is not just copywriting.
If your best evidence is hard to crawl, parse, attribute, or trust, it is less likely to appear in useful answers. We inspect the technical layer behind your source material, including machine-readable surfaces, metadata, semantic structure, canonical signals, schema where present, and answer-ready pages.
Technical hygiene does not guarantee citations. It improves the odds that the right evidence can be found and reused.
What you get
Executive report
A clear readout of where your brand is visible, cited, absent, misrepresented, or displaced by competitors.
Evidence pack
A retained set of prompts, answer captures, displayed sources, timestamps, and practical limitations. You can see what was observed instead of being asked to trust a dashboard score.
Competitor and source map
A view of who appears, who gets cited, which sources recur, and where third-party assets are shaping the answer before your owned content does.
Answer-quality review
Human assessment of accuracy, positioning, missing proof, weak citations, stale claims, and model/platform variance.
Technical GEO hygiene review
A practical inspection of the infrastructure that supports retrieval and citation eligibility: crawl access, machine-readable files, semantic structure, metadata, canonicals, schema where available, and source pages that directly support buyer questions.
Ranked action plan
A concise backlog of what to fix now, what to test next, and what to park. Recommendations are tied to buyer impact, visibility risk, evidence strength, and implementation effort.
How it works
1. Define the commercial prompt set
We agree the priority offers, buyer questions, target markets, competitors, and proof assets that should shape the measurement.
2. Capture current AI visibility
We run the agreed prompts across relevant answer surfaces and record what appears: mentions, citations, competitors, source paths, and answer-quality issues.
3. Inspect the technical evidence layer
We review whether the pages and assets that should support your claims are accessible, structured, machine-readable, and useful as source material.
4. Prioritise the next moves
You receive the report, evidence pack, and action plan: immediate fixes, deeper tests, and longer-term GEO work if the baseline justifies it.
Who it is for
The AI Visibility Baseline is a strong fit for:
- B2B SaaS, agencies, consultancies, and technical services with complex buyer journeys;
- category creators whose buyers ask education and comparison questions before sales calls;
- CMOs and founders who need evidence before funding GEO or AI visibility work;
- teams whose competitors already appear in AI answers;
- brands with strong proof assets that may not be easy for answer engines to find;
- organisations that care about technical implementation, not just content briefs.
It is not for teams seeking guaranteed ChatGPT, Perplexity, Gemini, or Claude citations. Any agency promising deterministic control over answer-engine outputs is overclaiming.
Methodology note
The baseline is evidence-led and limitation-aware.
We do not infer AI visibility from rankings alone. Material findings are tied to retained answer captures, displayed sources, live URL checks, or source inspection for technical claims.
AI systems vary by platform, model, interface, geography, account state, freshness, and sampling. The report labels those limitations instead of hiding them. Weak, absent, and inconclusive results stay in the evidence pack because they are often the most useful part of the diagnosis.
The aim is not to produce a universal ranking score. The aim is to establish a credible measurement window, identify the highest-value gaps, and decide what to improve next.
FAQ
Is this an SEO audit?
No. SEO inputs matter, but the baseline measures answer-engine visibility, citations, source patterns, competitor presence, answer quality, and technical retrievability. Search rankings can support the analysis; they do not prove AI visibility by themselves.
Can you guarantee we will be cited by ChatGPT, Perplexity, Gemini, or Claude?
No. We measure current visibility, identify likely limiting factors, and recommend evidence-led interventions. We will not promise rankings or citations that no agency can control.
Why not just use an AI visibility dashboard?
Dashboards can be useful for monitoring. The baseline is designed for diagnosis and decision-making: the prompt set, evidence pack, answer-quality review, competitor/source map, and technical inspection are tied to the commercial questions your buyers ask.
What if our brand is absent?
That is useful evidence. Absence shows which prompts, competitors, sources, and technical or content gaps need attention before money is spent on broad rewrites or tools.
How do you handle model variance?
We record the tested surface, prompt, time window, displayed sources, and practical limitations where relevant. Variance is treated as a finding, not cleaned out of the report.
Do technical checks prove why an AI system did or did not cite us?
Usually not on their own. Technical checks show whether source material is accessible, parseable, and well structured. They can identify gaps that may be limiting retrieval, but causality should only be stated when the evidence supports it.
What do we need to provide?
Start with your domain, priority offer, target buyers, and a few competitors or buyer questions. If you have sales notes, Search Console data, proof assets, or analytics, they can improve the baseline — but they are not required for the first call.
What happens after the baseline?
You get a ranked action plan. Follow-on work may include technical hardening, source-backed content, proof assets, answer-ready pages, remeasurement, monitoring, or agentic workflows to keep the evidence current.
Start with the evidence
Before you rewrite the site, buy another platform, or brief a content team, find out what AI systems can already see.
The AI Visibility Baseline gives you the measurement layer first: prompts, platforms, citations, competitors, technical hygiene, retained evidence, and a ranked action plan. You leave knowing whether content, technical source quality, competitive positioning, or measurement needs to move first.