AI Brand Visibility Tracking: Reddit, TikTok, and Custom Prompts

Ahrefs’ December 2025 product release added TikTok video visibility, Reddit-in-SERP tracking, and custom AI queries to Brand Radar—three changes that make a useful point for SEO teams: AI-generated recommendations do not begin and end inside an AI chatbot. With AI brand visibility tracking, we can measure whether our brand appears in real buyer questions, identify which competitors replace us, and connect those answer-level outcomes to the conversations and content formats shaping discovery.

The practical payoff is not another social-listening dashboard. It is a repeatable way to track brand mentions in Reddit and TikTok, test the buyer prompts that matter, compare ChatGPT and Gemini results, and prioritize the gaps where a competitor owns more of the answer.

What changed in December 2025—and why it matters

Ahrefs’ December 2025 update bundled nine product changes, but the most relevant group was in Brand Radar: TikTok tracking, Reddit visibility in Google search results, and custom AI queries. Its TikTok feature scans video titles, descriptions, and transcripts, then surfaces the excerpts containing the tracked brand rather than presenting a full transcript. Its Reddit index focuses on Reddit results that rank in Google, including titles, descriptions, and subreddit names in search snippets. (ahrefs.com)

Those are useful additions because buyer research is fragmented. A prospective customer may see a TikTok comparison, read an opinionated Reddit thread, search Google, and finally ask an AI assistant for “the best option for a five-person agency.” If we measure only a branded keyword rank or only an AI mention count, we miss much of that path.

We see the release as evidence of a broader measurement requirement:

1. Monitor the places where people discuss and evaluate brands. Reddit and TikTok are different environments, with different signals and content formats.
2. Measure the AI answer itself. A mention in a thread does not prove that ChatGPT, Claude, Gemini, Perplexity, or Grok will recommend the brand.
3. Use specific buyer prompts rather than generic category keywords. “Best project management software” and “best project management software for a regulated healthcare agency” are not the same visibility test.
4. Compare brands in the same answer set. A brand can gain mentions while still losing the commercially valuable recommendations to named competitors.

That distinction is central to our approach. We treat social and search surfaces as potential inputs and evidence, while the AI-generated answer is the outcome we need to measure.

AI brand visibility tracking is not ordinary social listening

Traditional social listening answers questions such as: How many people mentioned our brand? Was sentiment positive? Which post received the most engagement? Those are useful questions, but they are not sufficient for AI search.

AI brand visibility tracking starts with a different unit of analysis: a prompt, an engine, a location, a date, and the generated answer. For every tracked check, we want to know whether the answer mentioned our brand, whether it cited a page associated with us, which alternatives it named, and what recommendation context it used.

For example, imagine a cybersecurity consultancy tracks this prompt:

> “What are the best penetration-testing providers for a mid-market SaaS company in the United States?”

A social-monitoring tool might show that the consultancy was discussed 80 times during the month. An AI visibility workflow instead records whether the consultancy was named in the answer, whether three competitors appeared instead, whether the assistant used a “best for enterprise” qualification, and which cited sources supported that result.

Ahrefs’ current documentation similarly separates mentions, citations, impressions, and AI Share of Voice. It counts a brand mention once per AI response even if the brand name appears multiple times in that response, which is a sensible reminder not to inflate visibility by raw text repetition. (help.ahrefs.com)

For a deeper measurement model, see our guide to AI search measurement. The key principle is simple: count answer-level outcomes consistently before trying to optimize them.

How to track brand mentions in Reddit and TikTok without confusing the signal

Reddit and TikTok brand monitoring should answer two separate questions: Where is the brand being discussed? and what themes might later affect AI-generated recommendations? Neither platform should be treated as a direct proxy for model citations.

Reddit: monitor recurring buyer language and comparison themes

The December 2025 Ahrefs release tracks Reddit visibility through Reddit pages that appear in Google’s results, rather than by treating every Reddit post as an AI response. Its index uses titles, descriptions, and subreddit snippets visible in SERPs. (ahrefs.com)

That distinction matters. A Reddit thread may be influential because it ranks in Google, earns links, expresses first-hand product experience, or becomes a source that other content cites. But an AI engine can choose different sources, retrieve a thread without citing it, or ignore it entirely.

When reviewing Reddit, we recommend tagging findings into four buckets:

• Category language: phrases buyers use for the problem, such as “local-first analytics” or “agency reporting software.”
• Competitor comparisons: recurring “Brand A vs. Brand B” discussions and reasons given for choosing either.
• Objections: implementation friction, pricing concerns, privacy worries, or feature gaps.
• Proof points: screenshots, workflows, implementation details, expert comments, and firsthand results.

A useful output is not “we received 24 Reddit mentions.” It is “buyers repeatedly ask whether data leaves their device; our tracked AI prompts rarely explain our local-first setup; two competitors are repeatedly positioned as simpler.” That creates a testable content and messaging hypothesis.

TikTok: track the wording around demonstrations and recommendations

TikTok deserves its own process because video recommendations often compress a product into a few memorable claims. Ahrefs’ announced implementation scans descriptions and transcripts and returns the relevant mention snippets, which is more useful for research than a generic count alone. (ahrefs.com)

For TikTok, capture the specific language around:

• “Best tools” roundups and comparison videos
• Setup demonstrations and product walkthroughs
• Creator opinions about who a tool is for
• Complaints that show a category need is unresolved
• Repeated terms in captions, spoken transcripts, and comments

We would then convert the strongest patterns into AI tests. If several videos frame a category around “privacy-first reporting,” add a prompt that asks which products fit that requirement. If a competitor owns the phrase “no-code agency dashboard,” test whether AI engines repeat that framing and whether our brand is absent.

This is how Reddit and TikTok brand monitoring becomes decision-grade research rather than a disconnected stream of mentions.

Build custom AI prompts around decisions, not keyword lists

Custom AI prompts are the bridge between market conversation and measurable AI visibility. Ahrefs introduced custom queries for ChatGPT, Gemini, Perplexity, and Copilot in its December 2025 release, with daily, weekly, or monthly tracking options by chatbot. (ahrefs.com)

The lesson is bigger than a single vendor feature: a fixed prompt library lets us observe the same commercial question over time. It lets us see changes in brand inclusion, citations, competitors, and recommendation wording without relying on one-off manual searches.

A good custom prompt library normally contains 20 to 60 prompts at the start. We divide them by buyer stage rather than creating a long unprioritized keyword list:

1. Problem discovery: “How can an agency measure whether clients appear in AI answers?”
2. Category evaluation: “What tools track brand visibility across ChatGPT and Gemini?”
3. Competitor comparison: “Compare [our brand] with [competitor] for local-first AI visibility tracking.”
4. Use-case qualification: “What is the best way to monitor AI mentions for a multi-client SEO agency?”
5. Objection handling: “Which AI visibility tools keep prompt and result data under the customer’s control?”
6. Action prompts: “How do I find prompts where competitors are named but my brand is missing?”

Our recommendation is to record each prompt’s intent, target country or market, priority, owner, and expected competitors. This is more durable than treating a spreadsheet of keywords as the measurement plan. For a direct comparison of the two approaches, read AI search monitoring prompts vs. keyword lists.

Measure AI search visibility across ChatGPT and Gemini—and do not assume parity

A common reporting error is combining all AI engines into one number before inspecting the underlying differences. ChatGPT may cite editorial reviews, Gemini may produce a different shortlist, Perplexity may show more overt source links, and Claude or Grok may phrase recommendations differently depending on the prompt and available capabilities.

Our AI Visibility Tracker is designed for prompt-level checks across ChatGPT, Claude, Gemini, Perplexity, and Grok using the customer’s own API key. That local-first workflow gives us control over the prompt set, the schedule, the retained results, and the model access we pay for. It also avoids assuming that an aggregate vendor dataset is a substitute for the exact buyer questions we need to test.

We recommend viewing engines side by side before calculating an aggregate:

The table is illustrative, not a benchmark. Its purpose is to show why one blended score can conceal a real opportunity. We need to know where the gap occurs before deciding what to fix.

Vendor support also changes over time. Ahrefs’ current custom-prompt documentation lists Google AI Overviews, AI Mode, ChatGPT, Perplexity, Gemini, Copilot, and Grok, while noting that it could not collect new Grok data at the time of that documentation because of a policy change. (help.ahrefs.com) That is exactly why we recommend validating engine availability and response behavior at the time of each reporting cycle.

Separate brand mentions, brand citations, and competitor gaps

A brand mention means the assistant named the brand. A citation means the assistant visibly attributed information to a source page or domain. They overlap, but they are not interchangeable.

For example, an answer might say, “AI Visibility Tracker is a local-first desktop option,” without linking to us. That is a mention. Another answer might cite our comparison page but recommend a competitor in the final shortlist. That is a citation without the desired recommendation outcome.

We therefore track at least four fields for every prompt-engine result:

• Mention status: our brand appears or does not appear.
• Citation status: our domain or a relevant third-party page is cited or is not cited.
• Competitor list: every competing brand named in the response.
• Answer role: recommended, compared, dismissed, described neutrally, or omitted.

A competitor gap occurs when one or more competitors appear in a prompt answer where our brand has a credible reason to appear but does not. The gap is not proof that we should force a mention. It is a prioritization signal.

Suppose 12 of 20 “best AI visibility software” prompts name two competitors, while our brand is omitted in 10. We review the cited sources, the feature language in the answer, and whether our website clearly addresses the buying criteria. If the answers emphasize cloud dashboards while our genuine advantage is desktop privacy and bring-your-own-key control, the work may be to clarify positioning—not to imitate the competitors.

This is also why our guide to measuring an AI visibility index emphasizes a documented denominator and a stable prompt set. A gap count without context can be noisy; a gap tied to high-intent prompts and named competitors is actionable.

Use share of answer to show who owns the recommendation space

“Share of answer” is a practical reporting metric for the proportion of recommendation opportunities a brand occupies across a defined tracked set. It is related to AI Share of Voice, but teams should define it explicitly because vendors can calculate visibility metrics differently.

Here is a straightforward internal formula:

Share of answer = brand recommendation appearances ÷ total eligible recommendation slots × 100

Assume we track 30 prompts. Each answer can contain up to three explicit recommended brands, producing a maximum of 90 eligible slots. If our brand appears as a recommendation in 18 slots, our share of answer is 20%.

That number becomes useful only when we state the rules:

• Which prompts are included?
• Which engines and locations are included?
• Does a neutral mention count, or only a recommendation?
• Are repeated brand names in one answer counted once?
• Are all brand slots weighted equally, or are high-intent prompts weighted more heavily?

Ahrefs describes AI Share of Voice alongside mentions, citations, and impressions in its current metric documentation. Its impressions metric is based on the search volume associated with prompts where a brand appears, illustrating that visibility reports can use different denominators depending on the question being answered. (help.ahrefs.com)

For our product, we prefer a transparent share-of-answer definition based on the prompt set we actually care about. An agency can report a client’s overall share, then segment it by engine, buyer stage, country, or competitor. The result is easier to explain in a client review than a vague claim that “AI visibility improved.”

Why a local-first, bring-your-own-key workflow changes the reporting conversation

AI visibility monitoring involves valuable data: buyer questions, client names, competitor lists, geographic targeting, prompts, and generated outputs. For agencies especially, that can be sensitive research.

Our local-first desktop approach keeps the tracking workspace on the customer’s device and uses the customer’s own API key for the AI engines they choose to monitor. The practical advantages are clear:

• Prompt ownership: We control the exact prompt library rather than adapting to a generic market dataset.
• Cost visibility: Model usage is tied to the customer’s own API account, so teams can match frequency to prompt importance.
• Data control: Client research and generated responses stay within the local workflow rather than becoming another cloud project by default.
• Flexible testing: We can add, pause, revise, or segment prompts as campaigns, markets, and competitors change.

Local-first does not mean results are automatically more accurate. Prompt wording, model versions, web access, location, and stochastic output still affect what an AI assistant returns. It means the measurement system is more inspectable: we can see the prompt, engine, date, answer, and comparison logic behind every reported result.

That is a useful contrast with broad cloud AI search visibility platforms. For the trade-offs between large-scale indexes and direct prompt-level tracking, see AI Visibility Tracker vs. cloud AI search visibility tools.

A practical agency workflow: from Reddit insight to AI visibility action

Consider an SEO agency managing a B2B software client. The client wants to know why two competitors are named in AI-generated answers while it rarely appears.

Week 1: establish the baseline

Build a 30-prompt library across category, use case, comparison, and objection queries. Run each prompt across the selected engines—such as ChatGPT, Claude, Gemini, Perplexity, and Grok—using a consistent location where applicable. Capture mentions, citations, answer role, and competitor names.

At this stage, do not change content based on one answer. Establish the baseline first. If 14 of 30 high-intent prompts omit the client while Competitor A appears in 18, that is a meaningful initial pattern worth investigating.

Week 2: research the source and conversation layer

Review Reddit threads that rank for the relevant category and comparison terms. Review TikTok videos that repeatedly explain the buyer problem or make product recommendations. Extract exact customer language, objections, demonstration topics, and competitor framing.

If Reddit users repeatedly describe a feature using a phrase absent from the client’s site, that is a content-language gap. If TikTok creators demonstrate a workflow the client supports but does not explain, that is a proof-format gap. Neither observation alone proves causation, but both inform what to test.

Weeks 3 and 4: make one focused change and recheck

Create or improve one high-value page, documentation section, comparison asset, or third-party proof opportunity. Then rerun the same prompts on the planned cadence. Compare the answer roles and citations rather than simply looking for a rising total mention count.

The report to the client should answer four concrete questions:

1. Which high-intent prompts changed?
2. On which AI engines did the change appear?
3. Which competitors lost or gained answer share?
4. Which sources or pages were cited in the revised results?

This workflow prevents a common failure mode: treating social activity, SEO work, and AI monitoring as unrelated programs. They are different measurements, but they can inform the same buyer-question strategy.

What to optimize after you find a visibility gap

A missing brand mention is not a command to publish more content. First diagnose the reason for the absence.

If an AI answer cites competitors’ pricing pages, your gap may be commercial clarity. If it cites expert reviews and Reddit discussions, the gap may be third-party validation. If it repeatedly uses a capability your product does not offer, the correct response may be better qualification rather than attempted visibility growth.

We prioritize fixes in this order:

1. Truth and positioning: Can we credibly satisfy the criterion in the buyer prompt?
2. On-site clarity: Does an appropriate page state the capability, audience, limitation, and proof clearly?
3. Source support: Are there reputable, relevant third-party references that substantiate the claim?
4. Prompt coverage: Are we testing enough realistic variations to know whether the gap is broad or narrow?
5. Measurement discipline: Did we preserve the original prompt, engine, location, and scoring rules when comparing periods?

That sequence helps us avoid chasing every transient answer. AI-generated recommendations can change, but a well-defined prompt library, transparent share-of-answer metric, and competitor-gap view give us a stable way to decide what deserves attention.

FAQ

How can I track my brand’s visibility in Reddit and TikTok?

Track Reddit and TikTok separately from AI answers. For Reddit, monitor ranking threads, titles, snippets, subreddits, comparisons, and recurring objections. For TikTok, review video descriptions, transcripts, creator recommendations, and demonstrations. Then turn the strongest themes into custom AI prompts and measure whether those themes correspond with brand mentions, citations, or competitor gaps in generated answers.

What are custom AI prompts and how can they improve brand visibility tracking?

Custom AI prompts are the exact questions we choose to monitor repeatedly across selected AI engines. They improve tracking because they reflect real buyer situations, such as a use case, location, budget, or comparison, instead of relying only on broad category terms. A stable prompt library also makes before-and-after comparisons possible when content or positioning changes.

Which AI engines can be monitored for brand mentions and citations?

In our workflow, AI Visibility Tracker supports prompt-level monitoring across ChatGPT, Claude, Gemini, Perplexity, and Grok with the customer’s own API key. Availability, web-search behavior, response formats, and citation display can vary by provider and can change over time, so we recommend recording the engine and check date with every result rather than assuming all platforms behave alike.

How do Reddit and TikTok content influence AI-generated recommendations?

Reddit and TikTok do not guarantee an AI mention or citation. They can, however, reveal the language, comparisons, creator narratives, proof points, and buyer objections that shape category discovery. Some content may also become visible through search or be referenced by other sources. We use those signals to design better prompts and identify messaging gaps, then validate outcomes directly in AI-generated answers.

What is share of answer in AI search, and how is it measured?

Share of answer measures the proportion of eligible recommendation positions a brand occupies across a defined prompt set. For example, if 30 prompts allow three recommended brands each, there are 90 possible slots. If a brand receives 18 recommendation appearances, its share of answer is 20%. Define the engines, prompts, location, and what counts as a recommendation before reporting it.

Can marketers track competitors and identify brand visibility gaps across AI engines?

Yes. For each tracked prompt, record every named competitor, your brand’s mention and citation status, and the role each brand plays in the answer. A competitor gap appears when competitors are recommended for relevant buyer questions while your brand is absent. Segmenting that gap by engine and prompt intent shows whether the issue is broad positioning, missing proof, weak comparison content, or a narrow model-specific pattern.