How to Track Brand Visibility in ChatGPT and Perplexity

An AI visibility tracker answers a bounded question: when a defined set of prompts is run, does an AI answer mention your brand, cite your website, or choose a competitor instead?

That is useful evidence, but it is not a universal ranking. ChatGPT and Perplexity answers can vary with wording, model, location, time, browsing mode, and conversation context. A good tracking program controls what it can, saves the evidence, and treats each result as a sample.

If you are comparing tools, start with the measurement method—not a vendor's single composite score.

Quick answer: what should a ChatGPT or Perplexity visibility tracker measure?

At minimum, look for:

• the exact prompt and the date it ran;
• the AI surface or model sampled;
• whether the brand was mentioned;
• whether the brand's domain or page was cited;
• which competitors were mentioned or cited;
• the cited domains and URLs behind the answer;
• enough history to see whether the same prompt changes over time;
• an export or report that preserves the underlying rows.

Mentions and citations are different. A response can name a brand without linking to it, or cite a page without naming the brand prominently. Track both.

Our comparison method, updated July 28, 2026

This guide reviews current primary vendor documentation and the public New Reward workflow. We evaluated each option against seven questions:

1. Engine coverage: Which AI answer surfaces does the vendor say it samples?
2. Prompt control: Can you define the buyer questions that matter to your business?
3. Evidence depth: Can you inspect mentions, citations, pages, domains, or answer-level results?
4. Competitive context: Can you compare your brand with named competitors?
5. Freshness: How often can the same prompt set be rerun?
6. Portability: Can the reviewed data leave the product in a useful report, CSV, API, or dashboard?
7. Handoff: Once a gap is found, who changes the page, schema, content, or public evidence?

Features and access can change. Confirm the current vendor page and your exact plan before buying; this comparison intentionally omits prices.

The shortlist

Option	Strongest fit	What current official documentation says	What to verify in a trial
New Reward	Teams that need the finding turned into approved, shipped, inspectable work	New Reward begins with an AI visibility score, then routes evidence into a proof loop that separates prepared, approved, implemented, and live-verified states	The exact prompt sample, source evidence, implementation scope, approval boundary, and retest plan
OtterlyAI	Teams seeking a focused AI-search monitoring and content-intelligence product	The OtterlyAI product page describes prompt research, AI-search analytics, content audits, competitor comparisons, and coverage that includes ChatGPT and Perplexity	Prompt controls, location support, citation detail, alerting, exports, and which features are available in the intended plan
Ahrefs Brand Radar	SEO teams that want AI visibility beside Ahrefs research	Ahrefs says Brand Radar combines a large search-backed prompt index with custom prompts, competitor comparison, cited pages and domains, and multiple AI platforms	The difference between the discovery index and your custom prompts, each index's refresh cadence, and API/report access
Semrush AI Visibility	Teams that already operate in the Semrush ecosystem	Semrush documents visibility, brand-performance, competitor, prompt-research, and prompt-tracking features, with different source databases and update schedules	Which report uses synthetic, database, or custom prompts; which AI surfaces each report covers; and which export/report options are included
Peec AI	Marketing teams that want prompt-level segmentation and reporting integrations	Peec's official product page describes prompt setup, brand and source visibility, model/country/tag filters, daily prompt runs, CSV export, Looker Studio, and API connections	The exact model and region coverage, citation definitions, competitor setup, retention, and the reporting fields your team needs

These products do not produce directly interchangeable scores. Ahrefs explains that its discovery index is derived from search-backed questions while custom prompts have their own schedules. Semrush documents multiple prompt databases and refresh cadences. Peec and OtterlyAI use their own prompt and engine configurations. Compare underlying prompts and evidence rows before comparing percentages.

OtterlyAI: focused AI-search monitoring

OtterlyAI is the focused specialist in this group. Its official product page says it covers prompt research, brand and website monitoring, content audits, and optimization guidance across multiple AI answer systems.

That makes it a sensible shortlist option when your primary need is:

• a dedicated ChatGPT or Perplexity visibility tracker;
• prompt and citation monitoring rather than a broad traditional SEO suite;
• competitive AI-search reporting;
• a workflow your existing content or SEO team will act on.

Before choosing it, verify the prompts, locations, engines, refresh schedule, export shape, and content-audit features available in the plan you would actually use. For a workflow-first decision, read how to choose an OtterlyAI alternative or compare the OtterlyAI alternatives side by side.

Ahrefs Brand Radar: discovery index plus custom prompts

Ahrefs is useful when your team already uses its SEO data and wants AI-answer evidence in the same research environment.

Its official help center distinguishes two important jobs:

• a search-backed discovery index for finding mentions, citations, cited pages, domains, and competitors across a broad prompt set; and
• custom prompts that can be scheduled for the questions you choose.

Ahrefs also defines its AI visibility metrics, including mentions, citations, impressions, and AI share of voice. Read those definitions before treating the dashboard number as a business KPI. For example, share of voice is comparative and depends on the tracked brands and prompt set.

Brand Radar is strongest when broad discovery and classic SEO research matter as much as a small custom prompt panel.

Semrush: AI visibility inside a broader search workflow

Semrush is the broad-suite option. Its official documentation describes separate visibility, brand-performance, competitor, prompt-research, prompt-tracking, site-audit, and reporting surfaces.

The separation matters because those reports do not all use the same data source or update schedule. A high-level visibility database is not the same thing as the custom prompts your team tracks daily. During a trial, ask which report answers each business question:

• Where is the brand broadly present or absent?
• What does a fixed set of buyer prompts return today?
• Which competitors and cited sources appear?
• Can the team export the underlying rows?
• Can AI visibility sit beside Search Console, analytics, and technical SEO evidence?

Semrush fits best when the team wants one broader search stack and already has people to interpret and implement the findings.

Peec AI: prompt segmentation and reporting portability

Peec positions itself as AI-search analytics for marketing teams. Its official page says users can organize prompts, filter results by model, country, and prompt tags, distinguish brand mentions from source citations, and connect reporting through CSV, Looker Studio, or an API.

That makes Peec worth evaluating when multiple brands, markets, personas, or funnel stages need consistent segmentation.

Test the exact grain of the export. A useful export should preserve the prompt, engine, region, date, mention state, citation URLs, competitors, and any tags required to reproduce the comparison later.

New Reward: measurement plus the implementation handoff

New Reward is not positioned as the largest standalone prompt database. Its different job is to keep a visibility finding from becoming another unresolved dashboard task.

The public workflow is:

1. Run the AI visibility score or free AI visibility audit.
2. Identify the page, entity, technical, content, or citation gap supported by evidence.
3. Get approval where a claim, account, profile, or public change requires it.
4. Ship the scoped website, content, or schema work.
5. Record the implementation state and rerun the same evidence after an appropriate window.

That distinction is important for teams whose bottleneck is not detecting gaps but closing them. See the monthly SEO and AI visibility report sample for the state-and-source model.

How to build a reliable ChatGPT visibility tracker

You can use a vendor or a controlled manual process. Either way, the operating method should be the same.

1. Start with real buyer questions

Build prompts from Search Console queries, sales calls, form language, customer questions, competitor comparisons, and service or product categories. AI-generated prompt ideas can expand the list, but should not be treated as proof of demand by themselves.

2. Version the prompt set

Do not silently rewrite prompts between runs. Store a prompt ID, exact wording, buyer intent, geography, language, and the reason it belongs in the panel.

3. Separate exact surfaces

Label the surface actually tested. A vendor's API, a search-enabled model, the ChatGPT consumer interface, and a third-party prompt database are not automatically the same evidence.

4. Save answer and source evidence

For each result, preserve at least the answer date, mention state, cited URLs, competitors, and source screenshot or export when permitted. A percentage without the underlying prompt rows is difficult to audit.

5. Rerun on a defined cadence

Daily collection may suit volatile campaigns; weekly or monthly may be enough for slower content and authority changes. Use the same collection method for before-and-after comparison.

How to track citations in Perplexity

Perplexity makes citations visible in many answers, but the workflow still needs discipline:

1. Run the same versioned buyer prompts.
2. Record whether the brand is named.
3. Save every cited domain and exact URL.
4. Separate owned citations from third-party sources.
5. Compare which competitors appear and which sources support them.
6. Turn the gap into a page, proof, entity, or authority action.
7. Rerun the same prompts after the work is live.

A citation is not automatically an endorsement, a lead, or revenue. It proves that a source appeared in that sampled answer. Connect visibility evidence to analytics and downstream conversion data before making a business-impact claim.

Questions to ask before buying

• Can I inspect the exact prompts behind the score?
• Can I add and preserve my own high-intent prompts?
• Which ChatGPT and Perplexity surfaces are actually sampled?
• Are mentions, citations, cited pages, and cited domains separate fields?
• Can I compare named competitors on the same prompt set?
• How do location, language, personalization, and model changes affect collection?
• What is the refresh cadence for each report?
• Can I export the underlying rows?
• Who owns implementation after a gap is found?
• What evidence will prove that a recommended change shipped?

The decision rule

Choose a monitoring-first product when your team already has the writers, SEOs, developers, digital-PR owners, and approval path needed to act.

Choose an implementation-backed workflow when findings repeatedly stop at the backlog.

Many teams will use both: a broad monitoring system for trend coverage and a proof loop for prioritized execution. The ChatGPT and Perplexity visibility-tool comparison shows that split directly.

The best tracker is not the one with the largest number on its homepage. It is the one whose prompt method you understand, whose evidence you can inspect, and whose findings your team can turn into verified work.