LLM Audit: Method, KPIs and Deliverables to Manage Visibility

Running an LLM visibility audit has become a key priority for B2B marketing teams, both as an acquisition lever (being cited in AI-generated answers) and as a risk-management tool (hallucinations, outdated information, negative tone). The challenge often begins with a genuine ambiguity: are we talking about auditing a brand's visibility in generative engines (GEO), or auditing the AI model itself (quality, security, compliance)? This guide clarifies both meanings, then sets out a practical methodology to measure, fix and monitor your presence in AI-generated answers.

References and sources: GEO definitions and AI-visibility methodologies (https://www.maelzelie.com/blog/referencement/audit-geo-definition.html, https://www.natural-net.fr/blog-agence-web/2025/11/20/audit-visibilite-outils-ia-comment-mesurer-votre-presence-dans-les-reponses-generees-par-les-llm.html, https://www.soleil-digital.ch/blog/quest-ce-quun-audit-llm-et-pourquoi-votre-entreprise-en-a-besoin/, https://agence-wam.fr/geo-optimisez-votre-marque-pour-l-ia-search/). Usage and context data published by Incremys: https://www.incremys.com/en/resources/blog/chatgpt-statistics and the GEO/SEO resources listed further below.

Understanding an LLM Audit: Goals, Scope and Use Cases

What Is the Link Between GEO and Visibility in a Large Language Model?

GEO (Generative Engine Optimisation) aims to increase the likelihood that a brand is represented correctly in generated answers: being mentioned, recommended, cited as a source and described consistently. In simple terms, GEO looks at how an LLM constructs an answer (and which sources it uses), while SEO primarily concerns how a search engine crawls, indexes and ranks web pages.

In B2B contexts, this shift changes the unit you manage: you are no longer optimising only for a page that ranks, but for the set of elements that make your brand "quotable" — credible evidence, crisp definitions, documentation, entity pages and regular updates. An LLM presence audit becomes the control layer: what does the AI say, which sources does it rely on, and how accurate is it?

Two Approaches: an LLM Visibility Audit (GEO) vs Auditing the Model Itself

In practice, the term "LLM audit" covers two distinct types of work:

• LLM visibility audit (GEO): assessing whether your brand, offers and content are mentioned, recommended and cited (with sources where the interface provides them) in generated answers — for example in ChatGPT, Gemini, Perplexity, Claude or formats such as Google AI Overviews. Here, you are not managing "rank" in the SERP sense; you are managing contextual mentions, the quality of the brand narrative and the sources used.
• Auditing an LLM: evaluating the AI system itself (factual accuracy, robustness, bias, privacy, security and compliance, plus traceability). This sits more in governance and risk management, but it can affect marketing outcomes: an unreliable model can distort your positioning and amplify errors about your brand.

For marketing teams, the most common need is the GEO-focused audit: "What do generative engines say about us, and why?"

What Is the Difference Between an AI Security Audit and an AI Visibility Audit?

An AI visibility audit answers a marketing question: "Are we present and correctly represented when a prospect asks for a recommendation, a comparison or an opinion?" It measures what is observable in answers (presence, citations, tone, accuracy, freshness) and translates findings into actions on your content and evidence.

An AI security audit focuses on operational risk around the model's use: data leakage, PII exposure, prompts containing sensitive information, jailbreaks, compliance (including GDPR) and traceability mechanisms. The two converge in one area: weak security or governance can turn into a reputational risk if it results in inappropriate or incorrect answers about your brand.

Why SEO Remains Essential, but Is No Longer Enough to Appear in AI Answers

SEO remains the foundation because many AI answers still draw on web pages. At the same time, the search surface is evolving: users increasingly receive a synthesised answer (sometimes without clicking through), and value shifts towards being present within the answer and being cited as a source.

Two signals illustrate this shift:

• According to a study cited by Seer Interactive (November 2025), the presence of AI Overviews can reduce click-through rates by up to 61% on certain queries (secondary source referenced in https://www.natural-net.fr/…).
• The same referenced analysis suggests that between March 2024 and March 2025, average CTR for position 1 fell from 7.3% to 2.6% in contexts where AI Overviews appeared (secondary source referenced in https://www.natural-net.fr/…).

The practical takeaway: SEO still drives discoverability, but it no longer guarantees your brand will be retained in the conversational answer. An LLM visibility audit complements an SEO audit rather than replacing it.

What You Can Reliably Assess (and What Changes With Model Versions)

An LLM visibility audit can measure reproducible, observable elements such as:

• Appearance of your brand and offers across a representative question set.
• Mention prominence (in the opening, in a shortlist, in a footnote, or absent altogether).
• Cited sources (clickable links when the interface provides them) and the type of sources (owned site, media, community platforms, documentation, etc.).
• Accuracy (pricing, guarantees, features, coverage, compliance, etc.).
• Freshness (up-to-date vs outdated information).
• Tone and reputation signals (positive, neutral, negative).

Other factors vary significantly by context: model, version, localisation, persona, conversation history and whether web browsing is enabled. That is why a sampling protocol, re-tests and ongoing tracking are essential — a single snapshot is rarely sufficient.

Why Audit Your Brand in Conversational AI?

When Does an Audit Become Essential?

An LLM visibility audit becomes particularly valuable when AI answers can influence demand directly, or when the risk of misinformation is high. Common scenarios include:

• A launch or repositioning (new offer, pricing change, scope change): models may continue to repeat older information.
• A complex offer (SaaS product, regulated services, option-heavy catalogues): the more nuance involved, the higher the risk of inaccurate simplification.
• Intense competition: "top 5" answers create shortlist effects, making AI share of voice a genuine stake.
• Sensitive topics (YMYL, security, compliance): even minor errors can create reputational and commercial friction.
• Fragmented sources (docs, blog, product pages, PDFs, third-party mentions): the audit helps identify which pages carry authority and which remain invisible.

In short, you carry out this type of analysis because it serves both as an acquisition lever (being recommended) and as a control mechanism (reducing the gap between your official messaging and what AI systems repeat).

Acquisition: Being Cited, Recommended and Short-Listed on High-Intent Prompts

The most direct benefit is access to a channel where users ask for a shortlist rather than a list of links. A typical example: "Which software should we choose for [need] in an SME, and why?" In that scenario, the AI lists two to five options, and the user will often visit only those sites.

Adoption signals reinforce the point: according to Backlinko (2026), ChatGPT reportedly has 900 million weekly active users (source: https://www.incremys.com/en/resources/blog/chatgpt-statistics).

Risk Reduction: Errors, Outdated Information, Offer Inconsistencies and Reputation

A brand audit within AI answers also helps to limit risks: hallucinations, confusion with a competitor, failure to reflect a new offer, or the repetition of outdated information (pricing, terms, integrations, geographic coverage, etc.). The issue is not only that an error occurs — it is that it can be repeated and amplified, as generative engines tend to reinforce sources they have already used.

Security and Trust: Limiting Data Leakage and Risky Answers

Visibility should never come at the expense of confidentiality. A trust-focused audit approach typically covers personal data handling (PII), internal usage policies and traceability of interactions (see the call-out below on logs and audit trails). The goal is not legal advice, but reducing operational and reputational exposure.

Sector Priorities: B2B SaaS, Services, E-commerce, Long Cycles and Sensitive Topics

The most exposed sectors are those where AI intervenes early in the decision journey: B2B SaaS, professional services, highly competitive e-commerce, and long-cycle offers where prospects compare, seek evidence and then validate. Sensitive domains — health, finance, education and, more broadly, YMYL topics — combine a stronger need for precision with higher reputational risk.

A Practical Methodology for an LLM Visibility Audit

How Does an LLM Presence Assessment Work?

An LLM presence assessment works like a test protocol: define a scope (brand, offers, country/language), run a representative library of prompts, then score the answers using a stable grid. The aim is not to find a single "truth", but to produce a reproducible diagnosis that can be compared over time and translated into concrete actions on your source pages and editorial strategy.

To stay rigorous, always record your collection conditions: date, language, persona, web-browsing mode (on/off) and the interface used. That traceability prevents you from mistaking model noise for genuine progress — or drift — in your brand representation.

What Are the Steps in a Full LLM Audit?

A complete LLM audit typically includes: (1) scoping, (2) semantic mapping based on conversational intents, (3) prompt set design, (4) multi-criteria scoring, (5) benchmarking and AI share of voice, (6) source identification, (7) a prioritised action plan, and (8) ongoing tracking. The sections below break each step down with examples and KPIs.

Step 1 — Scoping: Objectives, Scope, Country/Language, Models and Frequency

Start by making your scope explicit:

• Objectives: visibility (citations), acquisition (leads), reputation (tone), reducing errors (accuracy).
• Scope: brand, product lines, countries, languages, segments (SMEs vs mid-market, roles, etc.).
• Interfaces and models: chat assistants, "answer + sources" engines, and AI Overviews where relevant.
• Cadence: a baseline snapshot, then ongoing tracking. Some methodologies recommend monthly to quarterly cadences depending on volatility (source: https://www.natural-net.fr/…).

Crucially, record your test conditions (date, language, persona, web mode, protocol) so results remain comparable over time.

Step 2 — Semantic Mapping: Topics, Entities and Conversational Intents

Do not start from keywords alone; start from conversational intents: discovery, comparison, proof, decision-making, pricing, integrations and objections. The expected deliverable is a map from topics to entities (brand, products, categories, concepts) to real questions.

For prioritisation, combine business value (pipeline), the maturity of existing content and risk level (sensitive topics, compliance, security).

Step 3 — Building a Prompt Set: Discovery, Comparison, Decision and Reputation

Your prompt library is the heart of the method. It should be representative (multiple phrasings, multiple personae) and large enough to avoid fragile conclusions.

Discovery: Definitions, Trends and Guides

• "How would you define [category], and what are the key business use cases?"
• "What are the 2026 trends for [topic], and what do they mean for a marketing team?"

Comparison: Alternatives and "Best X for Y"

• "What are the best [category] solutions for a mid-sized company, and which criteria should we use to compare them?"
• "What alternatives to [approach] help achieve [goal] under constraints [X]?"

Decision: Pricing, Integrations, Reviews, Case Studies, Suppliers and Agencies

• "How should we budget for [category], and which cost items should we anticipate?"
• "Which criteria should we check before choosing a [category] provider (security, GDPR, integrations, evidence)?"

Reputation: Problems, Disputes, Compliance and Reliability

• "What are the common issues with [category], and how can they be avoided?"
• "What are the GDPR risks and governance best practices for [category]?"

Step 4 — Measurement and Scoring: Presence, Prominence, Sources, Accuracy, Freshness and Tone

To move from observation to action, apply a simple, stable scoring grid to each prompt:

• Visibility (presence/absence and mention prominence).
• Citation (sources cited and whether your site appears among them).
• Accuracy (faithfulness) for sensitive information.
• Brand alignment (positioning, promise, differentiation).
• Evidence (data, studies, customer cases, verifiable details).
• Freshness (recency and update status).

Note that some interfaces display links whilst others do not. Measure "citation" broadly — source mentions and brand mentions — rather than relying solely on click data.

Step 5 — Competitive Analysis: AI Share of Voice, Cited Entities and Dominant Sources

The key metric is AI share of voice (often referred to as share of model): how frequently you appear relative to other players across a set of strategic prompts. It tells you whether you are positioned as a leader, an alternative or simply absent.

In many pragmatic approaches, a panel of 20 to 50 strategic prompts repeated across several interfaces serves as a common starting point (source: https://www.soleil-digital.ch/…).

Step 6 — Identifying Which Pages Act as Sources for AI Answers

Once you have collected answers, identify which URLs are feeding the generative engines: which of your pages (or external ecosystem pages) are being used, and which are never referenced. This analysis often reveals gaps such as:

• missing evidence pages (numbers, methodology, concrete cases);
• weak entity pages (definitions, scope, integrations);
• content that is overly promotional and difficult to extract;
• outdated or undated content.

As a complementary practice, some teams use a dedicated file to help AI agents find canonical pages, whilst bearing in mind that this is not an official standard and its impact is not guaranteed. For more on this: our article on the llms.txt file.

Step 7 — Recommendations: Quick Wins, Foundational Work, Editorial Plan and Backlog

Recommendations should be prioritised by impact × effort × risk, and assigned to specific pages and owners. Common quick wins include:

• adding a targeted FAQ to a product page;
• rewriting a definition so it is self-contained and easy to quote;
• dating a page and documenting recent updates;
• adding factual comparison tables;
• strengthening evidence (sources, data, case studies).

Foundational work — topic hubs, architecture refactors, update governance — is typically planned across a 30/60/90-day horizon.

Step 8 — Ongoing Tracking: Monitoring, Prompt Refresh and Lead/ROI Correlation

Because answers are dynamic, an LLM visibility audit should become a continuous process: re-tests, variation alerts and prompt library updates. Some sources mention observable improvements within 4 to 12 weeks of optimisation, with variability linked to model updates and the scale of changes made (source: https://www.maelzelie.com/…).

On the business side, the goal is to connect AI signals to MQLs, SQLs, pipeline and content ROI — before/after correlation and, where possible, analytics-based attribution.

Measurement and Reporting: What an Actionable Audit Report Looks Like

Which KPIs Should You Track in an AI Visibility Audit?

A useful dashboard blends three KPI families: (1) visibility (presence and citations), (2) quality (accuracy, brand alignment, evidence, freshness, sentiment), and (3) business impact (leads, conversions, ROI). The value lies in linking progress "inside the answer" to a specific action — for example, updating an evidence page — and then to an outcome, such as an increase in inbound enquiries.

Visibility KPIs: Appearance Rate, AI Share of Voice and Share of Citations

For visibility reporting, the core metrics typically include:

How Do You Measure Brand Sentiment in Model Answers?

Sentiment measurement means classifying the tone of passages that mention your brand (or your category) and aggregating findings across a sample. In practice, you can combine:

• a simple label per answer (positive / neutral / negative), already referenced in GEO methodologies (source: natural-net.fr);
• pattern spotting: recurring criticisms, objections and friction points (pricing, support, reliability, security);
• context checks: a negative mention may be acceptable if it is factual and balanced, but problematic if caused by confusion or outdated information.

The key is actionability: each negative mention should ideally be linked to a likely source (internal page, external mention, missing evidence) and to a corrective action (update, clarification, adding sources).

Quality KPIs: Brand Alignment, Accuracy, Evidence, Freshness and Compliance

A brand can "appear" and still be misrepresented. Quality KPIs typically include:

• Factual accuracy: aiming for correct information (one example target cited is 95% correct information, source: maelzelie.com).
• Brand consistency: alignment with your positioning.
• Evidence score: presence of numbers, sources, case studies and reference documents.
• Freshness: up-to-date, dated information.
• Sentiment: positive, neutral, negative (source: natural-net.fr).

Business KPIs: Attributable Traffic, Assisted Leads, Conversions and Content ROI

Where attribution is possible, isolate traffic coming from AI assistants in GA4 using a custom channel grouping and segments (approach referenced in https://www.natural-net.fr/…). Bear in mind that the impact is not only clicks: part of GEO influence shows up in assisted conversions, direct traffic or an increase in branded demand.

To contextualise changes in search metrics, you can also consult our SEO statistics and our GEO statistics.

Deliverables: Prompts×Topics Matrix, Benchmarks, Detected Errors and a 30/60/90-Day Roadmap

An actionable audit report looks less like a descriptive PDF and more like an execution pack:

• a prompts × topics/entities matrix with scores (visibility, citation, accuracy, etc.);
• a benchmark: AI share of voice, dominant sources and blind spots by topic;
• a list of errors (hallucinations, outdated content, inconsistencies) with source hypotheses;
• the top pages to strengthen (current source pages and missing pages);
• a 30/60/90-day roadmap with prioritisation, owners and validation criteria.

Scope and Budgeting: What Changes the Size of an Audit Engagement

There is no reliable "standard price" without inventing figures. However, the factors that drive scope are consistent:

• number of models and interfaces tested;
• number of topics, entities and personae;
• size of the prompt library (and re-test frequency);
• depth of source analysis (owned site + external ecosystems);
• deliverable expectations (simple diagnosis vs production-ready backlog + tracking).

On timing (not cost), some sources mention a duration of one to three weeks depending on complexity and scope (source: https://www.soleil-digital.ch/…).

Post-Audit Action Plan: Visibility, Reputation and Corrections

How Do You Fix Poor Brand Representation in AI Answers?

Fixing poor representation follows a straightforward loop: symptom → source → correction → re-test. In practice:

• identify inaccurate details (pricing, features, scope, compliance) and problematic wording;
• identify dominant sources (your pages, and third-party sources when visible);
• prioritise corrections to canonical pages (offers, documentation, evidence pages, FAQs) using factual, dated and verifiable language;
• rerun the same prompt protocol under the same conditions to validate the change.

This loop supports the core goal: reducing hallucinations and outdated information whilst increasing the likelihood that generative engines cite your official sources.

Align Content, Evidence and Positioning for Stronger Brand Consistency

Start with issues that directly hurt perception or conversion: promise mismatches, scope confusion, incorrect pricing or vague product pages. The objective is to increase the chance that AI systems extract factual, consistent and current language from your content.

Strengthen E-E-A-T: Authors, Expertise, Evidence, About Pages and Editorial Policies

Generative engines favour sources they perceive as trustworthy. On your site, this often means named authors, clearly stated experience and expertise, strong corporate pages (About, contact, legal notices) and, for higher-stakes topics, a published editorial policy.

Optimise Content for Citation: Clear Definitions, Short Formats, Tables, FAQs and Entity Pages

"Quotable" formats appear consistently in GEO recommendations: Q&A blocks, structured lists, comparison tables, crisp definitions and short sections (sources: natural-net.fr and agence-wam.fr). For GEO purposes, information density and extractability matter more than promotional copy.

To go further on GEO editorial strategy: our guide to GEO content strategy.

Schema.org Structured Data: Organization, Product, FAQPage, HowTo, Article, Review

Schema.org markup helps clarify page types, entities and evidence (FAQ, product, organisation). Common schemas in GEO/SEO action plans include Organization, Product or SoftwareApplication, FAQPage, HowTo, Article and, where relevant, Review.

Architecture and Internal Linking: Topic Hubs, Pillar Pages and Links to Evidence

A hub-based architecture (pillar page + supporting pages) clarifies scope and creates clear pathways to evidence. It also makes content planning more coherent: each cluster has reference pages, evidence pages and objection-handling pages.

Freshness Governance: Updates, Dating, Changelogs and Review Cycles

The risk is not only in publishing — it is in leaving strategic pages unmanaged. Dating pages, documenting changes (a changelog where relevant) and establishing a review governance cycle reduces the likelihood that AI engines repeat outdated information.

Reputation: Third-Party Mentions, Digital PR, Reference Pages and Entity Profiles

External mentions — media coverage, expert publications, reference platforms — contribute to perceived authority and may influence which sources generative engines select. An LLM visibility audit should therefore look beyond your own site: where is your brand described, how, and with what evidence?

An Iterative Approach: Re-Tests, Validation and Continuous Improvement

Treat the audit as a cycle: baseline, corrections, re-tests on the same prompt set, then expansion to new clusters. This avoids a one-off approach and makes it possible to demonstrate what has changed — or not — even as models evolve.

Content Strategy: Turning the Audit Into Measurable Growth

How Can an LLM Audit Improve Your Content Strategy?

An LLM visibility audit improves content strategy when it becomes a prioritisation tool: it reveals the themes where you are absent, the questions where answers lack evidence, and the pages that genuinely act as sources. That shifts you from volume-based production to production guided by:

• conversational intents (discovery → comparison → decision);
• expected evidence (numbers, methods, customer stories, documentation);
• high-impact pages (those that are cited, or ought to be).

This approach also streamlines updating: rather than refreshing an entire blog, you focus on content that feeds AI answers — and on content that is currently causing errors.

How Do You Identify the Queries Where Your Brand Should Appear in LLMs?

Start with decision moments where AI can influence a shortlist: "best tool for…", "comparison of…", "alternatives to…", "pricing for…", "integration with…", "reviews of…", "GDPR-compliant…", and so on. Then connect those formulations to your entities (brand, product, modules, integrations, use cases) and your canonical pages.

To prioritise without inventing figures, use three criteria: (1) business potential (pipeline, segments), (2) competitive intensity (themes with tight shortlists), and (3) reputational risk (pricing, compliance, security). The desired output is not merely a keyword list, but an "intent → prompt → source page → evidence to provide" matrix.

SEO Audit vs LLM-Focused Audit: The Practical Differences

What Is the Difference Between an SEO Audit and an LLM Audit?

An SEO audit evaluates your site's ability to be crawled, indexed, understood and ranked in the SERPs. An LLM audit evaluates what is said about your brand in generated answers: presence, citations, accuracy, consistency and tone. Even when both rely on web content, they do not optimise the same surface or the same KPIs.

Does an LLM Visibility Audit Replace an SEO Audit?

No. It complements an SEO audit: it measures the outcome (brand representation inside the answer), whilst SEO provides the foundation (crawling, indexing, architecture, performance and content relevance). Without strong SEO, your pages are less likely to be discovered and reused. Without an LLM visibility audit, you cannot tell whether your messaging is correctly represented and cited.

Surfaces and Metrics: SERPs, Clicks, Citations, Recommendations and Answers

SEO auditing focuses on SERP performance (impressions, rankings, clicks, CTR) and technical ability to be crawled and indexed. An LLM audit focuses on presence within the answer, citations, narrative quality, sentiment and sources used. Different object, different KPIs.

For an SEO methodology refresher: our guide to SEO audits.

Time: Output Volatility, Sampling, Monitoring and Comparability

SEO rankings are relatively comparable over time (despite algorithm updates), whereas AI answers vary more with context and model versions. That is why you need (1) a test protocol, (2) a sufficiently large sample, (3) ongoing monitoring, and (4) a trend-based reading rather than a search for absolute truth.

Recommended Integration: Technical Foundations, Content and AI Visibility in One Management Framework

A practical integration looks like this:

• SEO foundations: indexation, performance, architecture, intent coverage.
• Content production: semantic coverage, evidence, quotable formats.
• GEO tracking: prompts, citations, sentiment, sources and AI share of voice.

To understand the shift from SEO to GEO and the differences in logic: our article on moving from SEO to GEO.

Call-Out: How Do You Evaluate an LLM Beyond Marketing?

Criteria: Accuracy, Robustness, Bias, Privacy, Security and Transparency

If your goal is to evaluate the model itself — rather than simply your visibility — common criteria include accuracy/faithfulness, robustness, bias/fairness, privacy, security, traceability and transparency.

Methods: Scenarios, Question Sets, Red Teaming and Source Checking

Methods typically include scenario-based testing (question sets), red teaming approaches, consistency checks (repeatability) and source verification when the model provides citations.

Brand Impact: Trust, Reliability and Perceived Credibility in Answers

These criteria shape trust: a model that varies heavily, rarely cites sources or distorts facts increases your reputational risk. Conversely, a strategy built on structured, verifiable content improves the chances of your brand being represented accurately.

Call-Out: Logs, Traceability and Security in Audit Programmes

Defining an Audit Trail: Prompts, Answers, Model Versions, Sources and Feedback

An audit trail is the traceability of prompts, answers, model versions, sources (where available) and feedback. It helps you understand who asked what, when and on what basis.

What Audit Logs Are for: Evidence, Control, Investigation and Incident Analysis

Audit logs support incident reproduction, documenting drift (for example a risky answer), proving test conditions, and continuous improvement — whether that means fixing a prompt, a source, a reference page or an internal policy.

Good Practice: Governance, Access, Retention, Personal Data and GDPR

Minimum good practice includes defining roles and access rights, setting retention policies, avoiding unnecessary logging of personal data, and applying GDPR requirements to your context (without confusing a marketing article with legal advice). The objective is risk reduction, not over-collection.

Tooling Overview: Choosing the Right Tools to Audit LLM Visibility

Which Tools Should You Use to Audit Your Presence in Conversational AI?

The right setup depends on your needs: a one-off diagnosis, ongoing monitoring, or management linked to a production backlog. In all cases, check that the solution can (1) replay a protocol, (2) export results, (3) identify sources, (4) maintain a history, and (5) connect findings to pages that need fixing.

For SEO and business measurement, the fundamentals remain Google Search Console and Google Analytics, ideally centralised to avoid fragmented analysis.

Coverage: Models, Languages, Sectors and Granularity (Prompts, Topics, Entities)

A strong setup should cover multiple models/interfaces, multiple languages where relevant, and enough granularity to move from "we do not appear" to "we lack evidence pages on this cluster".

Measurement: Scoring, Citations, Sentiment, Freshness, Exports and History

Key capabilities include multi-criteria scoring, source extraction, tone classification, before/after history, usable exports (tables) and the ability to rerun the protocol over time.

Management: Moving From the Audit to the Editorial Plan, Then ROI Tracking

The critical point is not only measurement: it is turning analysis into a backlog — content, structured data, evidence pages, update governance — and tracking the impact on visibility and then on leads.

How Incremys Helps You Audit, Plan and Track GEO/SEO Performance

From Semantic Mapping to Content Briefs: Turning Analysis Into an Actionable Backlog

Incremys is a B2B SaaS platform focused on GEO and SEO optimisation, designed to connect semantic mapping to content briefs, an editorial plan and assisted (or automated) production, whilst keeping performance-driven governance at the centre.

Unified Tracking: Google Search Console and Google Analytics Integrations

The platform integrates Google Search Console and Google Analytics via API to centralise SEO signals (impressions, clicks, pages) and support business tracking (engagement, conversions), alongside visibility indicators in generative engines.

Continuous Measurement: Dashboards, Prioritisation and Content ROI Management

The aim is to move from a snapshot to a cycle of diagnosis, prioritisation, execution and monitoring (dashboards), enabling you to estimate the ROI of content and structuring efforts across Google and conversational surfaces.

FAQ About LLM Visibility Audits

What Is an LLM Audit on the GEO Side vs the Governance Side?

On the GEO side, it measures your presence, citations, quality and tone in generated answers. On the governance side, it focuses on security, compliance, privacy and traceability (logs, audit trails) around AI usage.

Do You Need to Audit Each Model Separately (ChatGPT, Gemini, Perplexity, Claude)?

Yes, at least by sampling: interfaces, sources and how citations are displayed differ across models. In practice, you can reuse one prompt library and run a standardised protocol per model, with comparative reporting.

How Can You Tell Whether Your Brand Is Mentioned by ChatGPT or Perplexity?

The most reliable approach is to test a representative prompt set (by intent and topic), record brand mentions, and check any displayed sources when available. Regular tracking — same prompts, same conditions — makes variations measurable and highlights where you appear or disappear.

How Can You Tell Whether Your Brand Is Cited, and on Which Topics?

Start with a prompt set built around your key topics and intents, then track appearance rate, mention prominence and cited sources. Monitoring over time shows where you are improving and where you remain absent.

Which Sources Do Models Use to Generate Their Answers?

They may rely on web sources when citations are provided — owned pages, media, documentation, community platforms — and can also consolidate information from reference sources. The audit's role is precisely to identify, prompt by prompt, which URLs feed the answer and which are missing.

Which KPIs Should You Track to Manage Visibility, Citations and Quality?

At a minimum: appearance rate, AI share of voice, share of citations, accuracy score, brand-alignment score, evidence score, freshness score and sentiment. Add business KPIs (assisted leads, conversions) where attribution is possible.

What Should an LLM Audit Report Include for a Marketing Team and an Agency?

A prompts × topics scoring matrix, a benchmark (AI share of voice, dominant sources), a list of errors and outdated information, the pages to strengthen, and a 30/60/90-day roadmap with prioritisation, owners and validation criteria.

Can You Automate an LLM Audit and Monitor Results Over Time?

Partly. Collecting and consolidating answers can be automated, but interpretation — nuance, context and reputational risk — often requires human review. A hybrid approach is typically the most robust (source: maelzelie.com).

How Often Should You Run an LLM Audit?

Start with a baseline, then set a cadence aligned with your market: quarterly for stable scopes, more frequently in fast-moving sectors, with continuous monitoring for high-risk prompts (pricing, compliance, security) (source: natural-net.fr).

How Often Should You Rerun the Analysis and Refresh Prompts?

Prompt refresh follows the same logic: quarterly for stable scopes, monthly for fast-moving markets. Expand the library whenever you launch an offer, change positioning or encounter a reputational incident, so the protocol remains representative of your commercial reality.

How Much Does an LLM Audit Service Cost Depending on Scope and Models Covered?

Cost depends mainly on prompt volume, the number of models/interfaces, the number of languages/personae, the depth of source analysis and the level of deliverables expected (simple diagnosis vs production-ready backlog + tracking). Start by defining your objectives, scope and cadence, then size the work accordingly.

What Are Logs and Traceability for in a Compliance Approach?

They help reproduce outcomes, investigate incidents, document test conditions and demonstrate a minimum level of internal control (who did what, when and how). They also support personal-data risk reduction and alignment with GDPR requirements.