Mentions and positions
Whether your brand appears and where it sits in a relevant answer.
Expert-reviewed audit service
AI Visibility Audit Service for B2B SaaS Teams.
Already collecting AI visibility data but unsure what to do next? RecoProof reviews controlled recommendation, competitor, citation, and source evidence, then turns supported gaps into a prioritized diagnosis.
Before commissioning a diagnosis, inspect the formulas and worked audit example. If you first need to understand the output, the free audit example preview explains the question-to-evidence workflow and its limits.
Already tracking AI visibility but not acting on the data?
Your team may already have mention, share-of-voice, or prompt-monitoring data. The harder decision is which loss matters, why a competitor is winning the same buyer question, and what deserves attention first. Monitoring creates observations; the audit turns a bounded evidence set into a reviewable diagnosis and action order.
- 01
Monitoring data
Start with the prompt, visibility, share-of-voice, or mention observations your team already sees.
- 02
Expert review
Review a declared commercial question set and retain the answer and source evidence.
- 03
Visibility gaps
Separate absence, passing mentions, recommendations, citations, and accuracy concerns.
- 04
Competitor displacement
Identify commercially relevant questions where another vendor is recommended instead.
- 05
Evidence review
Inspect the owned, third-party, technical, positioning, or proof evidence surrounding the result.
- 06
Prioritized actions
Rank supported fixes by business value, confidence, and effort.
Definition
What is an AI visibility audit service?
An AI visibility audit service combines controlled measurement with expert review. It examines how a brand appears when AI answer engines respond to commercially meaningful market questions, then turns the retained evidence into a bounded diagnosis and prioritized actions. It looks beyond whether the brand name is present to assess whether the brand is recommended, which competitors are favored, which sources are cited, and whether the answer represents the company accurately.
RecoProof is built for English-language B2B SaaS teams that need query-level evidence rather than an unexplained universal score. Every finding stays tied to the questions, providers, models, country, language, repetitions, and measurement date used in the audit.
What RecoProof measures.
Recommendations
Whether the answer presents your product as a serious option, not merely a passing reference.
Citations
Which owned and third-party sources the answer uses as evidence.
Competitive visibility
Which competitors lead the same questions and what evidence supports their advantage.
Accuracy and confidence
Where claims are inconsistent, uncertain, unsupported, or sensitive to provider conditions.
See how these observations become comparable AI visibility metrics and KPIs, including their denominators and limitations.
Search and answer engines
Google rankings do not describe the whole AI answer.
Traditional rankings measure how a URL appears for a search query. AI answers can synthesize multiple sources, compare vendors inside a single response, mention a company without recommending it, or cite a third party instead of the company's own site. SEO remains an important foundation, but ranking position alone does not reveal those recommendation and evidence patterns.
Controlled, not personalized
RecoProof benchmarks specified provider and model conditions through Vercel AI Gateway. API results may differ from personalized consumer sessions in ChatGPT, Gemini, or Perplexity. The audit records its conditions so teams can interpret the evidence without treating one observation as a permanent ranking.
A mention, citation, and recommendation are not interchangeable.
Mention
The answer names the brand or product. This establishes presence, but not endorsement or shortlist position.
Citation
The answer links to or identifies a source. This shows what evidence informed the response, not that the source was recommended.
Recommendation
The answer presents the brand as an option for the buyer's stated need. This is closer to commercial consideration.
Which buyer questions are you losing—and what should you investigate first?
A visibility score cannot answer that management question by itself. The useful unit of analysis is the commercial question: the buyer need, the observed recommendation set, the competitors selected, the answer evidence, and the confidence that the gap is repeatable enough to act on.
Where are we losing?
Group the approved commercial questions by buyer intent, then identify the exact questions where the target brand is absent, merely mentioned, or present without a recommendation.
Who is winning instead?
Record which vendors are recommended for those same questions, including first-choice language and answer position where the response makes an order observable.
What evidence accompanies the result?
Retain the raw answer and source URLs, then compare owned and third-party evidence. A source appearing beside an answer is evidence to inspect, not proof that it caused the recommendation.
What should we investigate first?
Prioritize a supported gap when the buyer question matters commercially, the observation survives review or repetition, and the evidence points to an owned, third-party, technical, positioning, or proof issue the team can act on.
If your brand already appears in the answer but another vendor receives the recommendation, use the focused guide to diagnose visibility without selection.
From commercial questions to a defensible action plan.
- 01
Define the commercial question set
Map the category, buyer context, competitors, and questions that influence discovery, comparison, pricing, and fit.
- 02
Run controlled provider benchmarks
Record the model, country, language, question, repetition, raw answer, and source URLs so results remain reviewable.
- 03
Extract the visibility evidence
Separate mentions, recommendations, positions, citations, competitor advantage, and accuracy risks from interpretation.
- 04
Explain the recommendation gap
Compare the observed answers with website and source evidence to identify technical, content, proof, pricing, and authority gaps.
- 05
Prioritize and publish
Rank a bounded action plan by evidence, business value, confidence, and effort; exceptions wait for human review.
Read the complete AI visibility audit methodology or see the shorter workflow overview.
B2B SaaS teams making considered-market decisions.
- Growth and SEO teams connecting search work to AI discovery.
- Product marketers responsible for category, positioning, and competitive accuracy.
- Content and communications teams deciding which evidence gaps to address first.
- Leaders who need a reviewable baseline before funding ongoing monitoring.
Evidence your team can inspect—and actions it can prioritize.
- Technical accessibility and crawl evidence.
- Query-level answers, mentions, positions, and citations.
- Competitor, owned-source, and accuracy gaps.
- Confidence-scored findings and a prioritized action plan.
- For the paid audit, a human-reviewed web report, PDF, and CSV.
Teams looking for a universal rank or guaranteed inclusion.
- Brands that cannot define a market, buyer, or commercial question set.
- Teams seeking a one-number substitute for query-level evidence.
- Campaigns that require a promise of future ranking, citation, or recommendation.
- Decisions that depend on undisclosed personalized consumer sessions.
Consequential interpretation stays reviewable.
Automation can collect pages, prepare bounded questions, run provider benchmarks, and extract observable answer signals. Human review is required when category fit is ambiguous, evidence conflicts, accuracy claims could affect positioning, or a recommendation does not clear the publication quality gate.
Example of What RecoProof Finds
This example comes directly from the public Northstar sample report, which uses development-fixture responses to demonstrate the deliverable.
“Best CRM software for marketing agencies”
Northstar recommended: No. Retained citations: 2.
Pricing information is not directly sourceable
The pricing page describes value but does not publish starting prices, plan boundaries, or an updated date.
Make pricing facts explicit and sourceable
Publish current starting prices, billing cadence, plan differences, exclusions, and a visible updated date on an indexable page.
AI Visibility Audit Service vs Monitoring Software
Monitoring answers “what changed?” An audit service answers “what does the evidence mean, what should we investigate, and what deserves action first?” If you already collect prompt-monitoring data but nobody owns interpretation, adding more observations can deepen the backlog without resolving the decision.
| Decision | Monitoring software | AI visibility audit service |
|---|---|---|
| Primary job | Observe the same prompts repeatedly and preserve dashboards or history. | Diagnose a bounded commercial question set and decide what the evidence supports doing next. |
| Evidence workflow | Collect recurring observations that still require someone inside the company to validate and interpret them. | Review retained answers, recommendation states, competitors, citations, source evidence, accuracy risks, and confidence together. |
| Competitive analysis | Show which brands appear or move across tracked prompts. | Identify competitor displacement on commercially relevant questions and inspect the evidence surrounding that result. |
| Output | A time series, dashboard, alert, or export for an internal operator to investigate. | A human-reviewed diagnosis with web, PDF, and CSV evidence plus a prioritized 90-day action plan. |
When monitoring alone is not enough
- The dashboard shows movement, but the team cannot separate a mention from a recommendation or a citation.
- Competitors appear more often, but nobody has reviewed the displaced buyer questions and surrounding evidence.
- Prompt, provider, or run-to-run disagreement makes the apparent trend too uncertain to prioritize.
- Exports accumulate without a defensible link from evidence to an owned action.
How RecoProof turns evidence into action
RecoProof does not relabel an external dashboard score as its own finding. The service runs a declared commercial question set, retains its own answer and source evidence, reviews ambiguous or consequential findings, and prioritizes supported technical, content, proof, pricing, positioning, or third-party authority gaps.
Compare the complete audit-versus-monitoring decision.
Fix one supported evidence gap, then retest it.
Assign the highest-value supported action, define an owner and acceptance criteria, make the change, and repeat the relevant questions under recorded conditions. Not every gap needs a content page; the right action may involve product evidence, pricing clarity, technical access, positioning, or third-party proof. Ongoing monitoring is an optional next step only when repeated measurement will change a funded decision.
Free checker, paid audit, or continuous monitoring?
Free visibility snapshot
A ten-page crawl, five commercial questions, and one controlled Sonar Pro pass. Best for confirming whether a first evidence gap is worth investigating.
Start with the free checker →Recommendation Audit
Up to 100 public pages, 30 approved questions, three models, adaptive repeats, a three-competitor benchmark, and human-reviewed web, PDF, CSV, and 90-day action plan deliverables.
Review the complete scope →Managed Monitoring
Monthly repeat measurement for audit customers who need to track material recommendation, citation, competitor, and accuracy changes over time.
Compare audit and monitoring →Current offer
Buy the Audit — $499 How much does an AI visibility audit cost?
RecoProof's founding price is $499 for the first 5 customers; the standard price is $750. The audit is delivered within five business days and includes the human-reviewed evidence package and prioritized action plan. Review the current scope beside the free and monitoring offers on the AI visibility audit pricing page.
AI visibility audit questions before purchase.
How much does the RecoProof AI visibility audit cost?
The founding price is $499 for the first 5 customers; the standard price is $750. The current offer and scope are shown on the pricing page before checkout.
Which AI systems are included in the paid audit?
The current Recommendation Audit uses GPT-5.6 Terra, Gemini 3.5 Flash, and Sonar Pro through Vercel AI Gateway across 30 approved commercial questions, with an adaptive second pass for selected high-value or conflicting questions.
What is delivered?
The paid audit includes a human-reviewed web report, PDF, CSV, three-competitor benchmark, and a prioritized 90-day action plan. Delivery is within five business days under the current offer.
Does the audit guarantee ChatGPT or Google visibility?
No. The audit records point-in-time evidence under defined provider conditions. It does not guarantee future rankings, citations, inclusion, or recommendations in any AI or search product.
Do I need continuous monitoring after the audit?
Not necessarily. The audit establishes a baseline and prioritized plan. Monitoring is useful only when repeated measurement of the same commercial question set is worth the ongoing cost.
Planning a manual review first? Use the public AI visibility audit template or read the B2B SaaS audit guide.
Evaluating an ongoing AI visibility platform instead? Read the factual RecoProof vs Otterly, RecoProof vs Peec AI, or RecoProof vs Profound comparison.
Comparing expert help before choosing RecoProof? Use the AI visibility service provider buyer guide to compare bounded audits, advisory programs, and full-service agencies by fit.
Start with a bounded check
Ready for the full evidence-backed audit?
Buy the Recommendation Audit for the complete three-model benchmark, human-reviewed deliverables, and prioritized 90-day action plan.
Choose with context
Compare the free snapshot, full Recommendation Audit, and managed monitoring without a ranking or inclusion guarantee.
Review AI audit pricing