LLM Visibility Tools: Track Your Brand in ChatGPT and Claude

A buyer's shortlist often forms inside an AI answer before anyone clicks a result. LLM visibility tools track whether AI assistants such as ChatGPT and Claude mention, cite and recommend your brand when buyers ask about your category, and they show which sources shape those answers. A team that already runs SEO and paid search needs this data for that reason. As of October 2026, the major trackers all claim coverage of ChatGPT, Claude, Perplexity, Gemini, Copilot, Google AI Overviews and AI Mode. They differ in how they collect answers, how often they refresh, and whether they do anything about what they find. This guide compares the leading llm visibility tools on those points, separates monitoring from execution, and gives you a test protocol to run before you sign.

What LLM visibility tools measure, and why ChatGPT and Claude need separate treatment

The core metrics are mentions, citations, share of voice, sentiment and position, tracked across a fixed set of prompts over time. Together they describe a brand's presence inside AI-generated answers. For a wider view of the category, see our guide to platforms for tracking your brand in AI answers. Four terms recur throughout this guide:

• AEO / GEO: answer engine optimization and generative engine optimization, the work of getting a brand quoted in AI answers.
• Citation source: the URL an assistant links to as evidence, such as a review site, a Reddit thread or your own comparison page.
• Retrieval: the live web search an assistant runs before answering, as opposed to answering from training data alone.
• Share of voice: the percentage of tracked answers that name you, compared with your competitors.

Monitoring, recommendations and execution are different jobs

The biggest difference between tools is what happens after the data arrives. Most products in the category only monitor. Fewer recommend changes, and fewer still produce them.

Attribution features cut across these layers. Profound reports AI referral traffic, and Scrunch AI reports AI referral and bot traffic. Semrush does not measure AI referral traffic directly, according to reviews of its toolkit.

How ChatGPT and Claude answers differ for tracking

ChatGPT and Claude both answer from training data by default and from live web results when search is triggered or switched on. That split changes how fast your work shows up:

• Search-backed answers cite URLs and can shift within weeks of a new page being indexed or a Reddit thread gaining traction.
• Training-data answers reflect what the model learned before its cutoff, so they move slowly and give no citations to audit.
• API versus consumer app: an API call carries no memory, custom instructions or consumer system prompt, and it may not search when the app would. API results are a consistent proxy for what a buyer sees in chatgpt.com or claude.ai, but they will not match it exactly.

Ask every vendor which mode it queries for each engine. A tool that queries Claude without web search is measuring model memory. A tool that queries ChatGPT with search on is measuring retrieval. Both are useful, but the two figures cannot be compared.

Collection method and data freshness

Collection method decides how far you can trust the numbers, and each method has a cost:

• API queries: cheap, repeatable and scalable, but they can drift from the consumer experience. Semrush relies mainly on this method.
• Browser sessions: closer to what users see, including rendered citations, but brittle and rate-limited. Some Reddit users have also raised legal and reliability questions about scraping-based approaches such as Profound's.
• Panel data: reflects real user prompts but depends on the sample. Profound and Scrunch AI combine panels with API and browser collection.
• Search-data pipelines: Tellr records Google organic results, AI Overviews and the discussions block each week, with the domains each one cites.

Refresh cadence varies just as much. Semrush refreshes prompt rankings daily and brand data weekly. Profound updates its index weekly. Scrunch AI describes near-real-time monitoring, but some reviewers report weekly updates. Our piece on monitoring citations at scale covers what cadence each use case needs.

How we evaluated LLM visibility tools

We scored each tool against criteria drawn from vendor documentation and published third-party reviews, and we flag where those sources conflict. We did not run paid trials of every product. Instead, the protocol below lets your team test a shortlist on your own category in about two weeks.

• Engine coverage: ChatGPT, Claude, Gemini, Perplexity, Copilot, AI Overviews and AI Mode, and which mode each engine is queried in.
• Collection method: API, browser sessions, panel data or a search pipeline.
• Refresh cadence: daily, weekly or unstated.
• Metric depth: mentions, citations, share of voice, sentiment, position and referral traffic.
• Execution: whether the tool produces briefs, pages, outreach or creative.
• Enterprise controls: SSO, roles, approvals, audit trail, multi-region workspaces and SOC 2.
• Pricing model: per domain, per prompt tier, custom or per engagement.

A test protocol to run before you sign

Manual checks stop working after a few dozen prompts. As SANS notes, AI can analyze volumes of telemetry at a scale that no human team could process, and answer tracking is a telemetry problem. Use manual runs to check the vendor's numbers. They cannot do the vendor's job.

1. Build a 60-prompt set using the mix below, phrased the way buyers type rather than as keywords.
2. Query ChatGPT and Claude with web search both on and off, plus Google AI Overviews.
3. Fix the locale for each market and write the prompts in the local language rather than translating English keywords.
4. Run each prompt five times per engine, on at least three days in the same week.
5. Count a mention as present only if it appears in at least three of the five runs. Record position (named first, listed, or footnoted) and every cited URL.
6. Compare vendor output with your own manual runs on 10 prompts. Log false positives, such as a namesake company counted as you, and false negatives, such as a product name mentioned without your company name and missed.
7. Open every cited URL to confirm it exists and supports the claim attributed to it.

Designing the prompt set

Your tool can only see what your prompts ask. Weight the set toward the questions buyers ask before they shortlist. Our guide on how to measure whether AI answers mention you goes deeper on scoring. The examples below use a cloud security brand.

Report six dimensions beyond raw mention counts:

• Sentiment quality: whether you are recommended, merely listed or caveated.
• Citation authority: whether the cited sources are your pages, review sites, Reddit or competitors.
• Position prominence: first-named or buried.
• Mention confidence: how many runs out of five include you.
• Answer consistency: how stable the answer stays across days.
• Geography variation: how answers shift between markets and languages.

Best LLM visibility tools

For tracking a brand in ChatGPT and Claude, the strongest picks are Tellr for managed execution, Profound for enterprise tracking with content workflows, Scrunch AI for governed self-serve monitoring, and Semrush for teams that want AI tracking inside an existing SEO suite.

Enterprise controls separate these tools as much as coverage does. Ask every vendor how queries reach the models. The Cloud Security Alliance warns that LLM API proxy routers occupy a man-in-the-middle position able to read, rewrite, retain or fabricate prompts. A tracker that routes queries through a third-party router can skew results without you knowing.

Tellr: best for enterprises that want the answers changed, not just measured

Overview: Tellr is a premium earned-visibility agency running on its own platform. A senior team runs one governed program covering Reddit, content built for AI search, answer visibility tracking and paid-media creative.

Engine coverage: Tellr tracks Google organic results, AI Overviews and the discussions block each week. Its content is written to be quoted by ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews.

Methodology: Tellr collects data through its own search-data pipeline rather than a consumer panel. It reports weekly citation share for each query, labeled as your site, a competitor, Reddit, social, review sites or references, with week-over-week movement. The measurement is there to steer the work. It is not a prompt-by-prompt dashboard.

• Comparison pages, reviews and answer-shaped articles built from your knowledge base and real user reviews, published straight to WordPress
• Subreddit mapping, a daily thread radar and guideline-checked replies behind an approval gate, with live status for each reply
• "Tellr for Claude", an MCP server for asking about the program in plain language, plus API access
• Runs without GA4 or Search Console access

Pricing: Priced per engagement, not per seat or per prompt. Pricing is not published and there is no free trial, because the work starts with a category map built for your brand.

Best for: Marketing teams spending $10k+ a month at companies worth $500M+ or with 200+ employees.

Not ideal for: Startups and small teams, for whom a managed program is overkill and a self-serve tracker fits better.

Profound: best for enterprise tracking with built-in content workflows

Overview: Profound is an enterprise AI search visibility platform that tracks citations, sentiment, competitor share of voice and AI crawler activity.

Engine coverage: All seven surfaces: ChatGPT, Perplexity, Gemini, Claude, Copilot, AI Overviews and AI Mode.

Methodology: Profound combines API, browser-session and panel data. Its index updates weekly.

• Measures mentions, citations, share of voice, sentiment, position and AI referral traffic
• Produces content briefs, drafts, page rewrites, outreach materials and ad creative, according to its own site
• SSO, user roles, approval workflows, audit trail, multi-brand workspaces and SOC 2 Type II

Weaknesses: Reviewers cite high, complicated pricing, a steep learning curve and lower tiers that restrict history and API access. Some users report data overload without clear next steps. Reddit users have also questioned how reliable and legally sound its scraping is.

Pricing: Custom enterprise quotes, plus a 7-day trial with 50 prompts a day across ChatGPT, Gemini and AI Overviews.

Best for: Enterprise teams with analysts who can act on the data.

Not ideal for: Small teams, or teams that need off-site placements such as Reddit replies.

Scrunch AI: best for governed self-serve monitoring

Overview: Scrunch AI is a self-serve AI visibility platform. One source reports that Sitecore acquired Scrunch in June 2026.

Engine coverage: All seven surfaces, including Claude and Google AI Mode.

Methodology: Scrunch AI uses API, browser sessions and panel data. It does not publish an exact refresh interval.

• Tracks mentions, citation share by domain, share of voice, sentiment, position, and AI referral and bot traffic
• SAML and OIDC SSO, role-based access, audit trail, multi-region deployment and SOC 2 Type II
• White-glove setup and Slack support for enterprise customers

Weaknesses: Users say its insights are hard to act on and its exports and reports are thin. Prompt credits run out fast because each engine counts separately. A recent review says it does not produce briefs, articles or page fixes, although an older source disagrees.

Pricing: Starter $300, Growth $500 and Pro $1,000 a month, custom enterprise pricing, and a 7-day trial on Starter.

Best for: Compliance-heavy teams that want to run monitoring in-house.

Not ideal for: Teams without the staff to turn the data into content.

Semrush: best for adding AI tracking to an existing SEO stack

Overview: Semrush's AI Visibility Toolkit adds AI answer tracking to its SEO suite. Customer Review Rating: 4.5/5 on G2 from 3,945 reviews as of October 2026. Reviewers single out its AI Overview and AEO tracking as increasingly valuable.

Engine coverage: ChatGPT, Perplexity, Gemini, Claude, Copilot, AI Overviews and AI Mode.

Methodology: Semrush collects mainly through the API, with some sources describing browser-based collection. It refreshes prompt rankings daily and brand data weekly.

• Tracks mentions, citations, share of voice and position
• Integrates with GA4, Search Console, WordPress, an API and an MCP server
• Supports multi-brand and multi-region reporting in Semrush One

Weaknesses: Reviewers report limited regional and language support and some inconsistent results. Costs scale quickly with add-ons, there is no free trial, and caps apply to prompts and exports. Sentiment reporting is inconsistently documented, and the toolkit gives insights but no fixes.

Pricing: $99 a month per domain, billed annually. Extra users cost $45 a month and prompt packs cost $60 a month per 50 prompts. Semrush One bundles cost $199, $299 or $549 a month.

Best for: SEO teams already on Semrush.

Not ideal for: Multilingual, multi-market programs.

Other tools worth shortlisting

• xSeek: tracks ChatGPT, Claude, Gemini, Perplexity and AI Overviews with screenshots, timestamps, source URLs and change alerts, and includes an AI agent that rewrites content.
• Scalenut: pairs visibility tracking with SEO research, content creation and GEO recommendations.
• Dageno AI: links demand, competitors, citations and URLs to help prioritize fixes.
• Peec AI: runs recurring prompt-level tracking of visibility, position, sentiment and sources.
• OtterlyAI and AIclicks: lighter options that track how answers and citations change over time.
• SE Visible (SE Ranking): adds AI monitoring inside an existing SEO workflow.

Free LLM visibility tools

The free options are one-off reports or time-limited trials. None of them gives you ongoing tracking.

How to choose and roll out an LLM visibility tool

The right tool depends on your company type, your compliance needs, and whether your team can turn data into content each week.

Buyer matrix by company type

The first 30, 60 and 90 days

1. Days 1–30: Lock the 60-prompt set, run the vendor audit from the test protocol, and record baseline share of voice and cited domains for ChatGPT, Claude and AI Overviews.
2. Days 31–60: Rank the top 10 gaps by intent. Publish or refresh the comparison and use-case pages that competitors win with, and join the Reddit threads that AI answers cite.
3. Days 61–90: Re-measure with the same prompts and locales. Retire prompts that never move, add post-purchase prompts, and report changes to leadership alongside the attribution signals below.

Limits of the whole category

• Model volatility: the same prompt can return different brands from one day to the next.
• Personalization: memory and location in consumer apps mean no tracker sees exactly what a given buyer sees.
• Rate limits and retrieval inconsistency: these cap sample sizes and add noise, especially for browser collection.
• Causal impact: a visibility gain shows correlation and cannot prove that the gain created pipeline.

Connecting visibility to pipeline

Combine three signals. Segment AI referral sessions in GA4 by referrer, such as chatgpt.com, claude.ai and perplexity.ai. Watch branded search impressions in Search Console. And add an AI-assistant option to the "how did you hear about us" field on demo forms. For example, if share of voice on comparison prompts rises from 20% to 35% over a quarter while branded impressions climb 15% and demo forms citing AI double, you have a defensible influenced-pipeline story without claiming last-click credit.

Where Tellr fits in an LLM visibility program

Tellr fits after the tracker has done its job. It turns citation gaps into the replies, pages and creative that change the answer. Many enterprise teams keep a monitoring tool for prompt-level dashboards and hand Tellr the work those dashboards point to. One senior team then runs the work under guardrails, approvals and an audit trail.

• Weekly citation share for your category's Google and AI Overview queries, labeled by source type
• Answer-shaped articles and comparison pages published directly to your CMS
• Guideline-checked Reddit replies behind an approval gate, with live status tracking
• Category ad intelligence and ready-to-run creative
• A weekly digest and a monthly program review, queryable through Tellr for Claude

Bottom line

You can measure visibility in ChatGPT and Claude if you keep a disciplined prompt set, repeat your runs and know how each tool collects answers. Profound and Scrunch AI lead on enterprise-grade tracking, Semrush is the practical add-on for teams already in its suite, and free reports give small teams a starting baseline.

Tracking alone does not move the answer. Choose llm visibility tools that match your team's capacity to act, and if your team spends $10k+ a month and needs the content, Reddit presence and creative produced as one governed program, put Tellr on the shortlist.

FAQ