An AI search competitor analysis measures how often AI engines cite you versus your competitors across a defined set of buyer prompts, and it exposes exactly where you are losing the answer. It matters more than a traditional SEO competitor audit because the engines barely agree with each other: a 2026 per-engine audit found only 11 percent of the domains ChatGPT cites overlap with the domains Perplexity cites. A competitor can dominate one engine and be invisible on another, so a single-engine view misleads you. With AI search visits up 42.8 percent year over year, from 15.6 billion to 27.4 billion between Q1 2025 and Q1 2026, the stakes are real. This guide walks through how to run the analysis: building a prompt set, calculating share of voice, reading citation gaps, and choosing tools.
What is an AI search competitor analysis?
An AI search competitor analysis is a structured measurement of your brand’s citation share against competitors across the major AI engines for a fixed set of category prompts. It answers one question: when a buyer asks AI about your category, how often does it name you versus the competition, and on which engines?
It differs from a keyword-ranking audit because there is no single ranked list to check. You run a defined prompt set through ChatGPT, Perplexity, Claude, and Google AI Mode, count who gets mentioned and cited, and compare shares. The output is a picture of competitive visibility across surfaces that behave differently and cite different sources. That per-engine view is the whole point, because a blended number hides the engine-by-engine reality that decides where you actually win, which ties into how visibility itself works, covered in what is AI visibility.
How do you measure share of voice in AI search?
Share of voice in AI search is the percentage of AI answers where your brand wins versus competitors, calculated as your brand citations divided by total category citations, times 100. You run a defined set of category-relevant prompts across your target engines, count how many responses mention your brand, and divide by the total responses across all brands.
In 2026 the metric comes in three flavors worth separating: share of answer, whether you appear in the response at all; share of citation, whether you are the linked source; and share of mention, how prominently you are named. Track all three, because you can be mentioned without being cited, which is weaker visibility than a linked citation. Run the same prompt set on a fixed cadence so the numbers are comparable over time, and compute share per engine rather than blended, since engine behavior diverges. The measurement discipline behind these numbers is the same one in how to measure GEO ROI.
The gap between the three flavors is where the real competitive read lives. A competitor with high share of mention but low share of citation is being described by the engine from third-party sources without a link back, which is beatable, because you can earn the citation with better owned content. A competitor with high share of citation is the source the engine trusts to answer from, which is a harder position to displace and tells you to study what they publish and where they are referenced. When you see your own name appear in answers but never as the linked citation, that is a signal your content is being read but not trusted enough to attribute, usually a structure or authority problem rather than a coverage one. Reading the three metrics against each other turns a raw share number into a specific to-do list.
Curious how your citation share stacks up against your top competitor across ChatGPT, Perplexity, and Google AI Mode? Get your free AI visibility audit and see the exact prompts where they are winning and you are missing.
How do you build the prompt set for the analysis?
Build a fixed set of category-relevant prompts that mirror how real buyers ask AI about your space, spanning discovery, comparison, and decision-stage questions. The prompt set is the instrument; if it does not reflect real buyer language, the share-of-voice numbers measure nothing useful.
Start with the questions buyers actually type: “best [category] tool,” “[competitor] alternatives,” “how to solve [problem],” and comparison prompts like “[you] vs [competitor].” Include both branded and unbranded prompts, since unbranded discovery queries reveal whether you show up when the buyer does not already know you. Keep the set fixed across runs so month-over-month numbers stay comparable, and size it large enough to be stable, typically a few dozen prompts per category. Roughly a third of US consumers now reach for an AI tool at the product-discovery stage, so weight the set toward the discovery and comparison prompts where new buyers form their shortlist.
How do you read citation gaps between engines?
Read them per engine, because the engines cite almost entirely different sources: only 11 percent of ChatGPT’s cited domains overlap with Perplexity’s. A competitor beating you in ChatGPT may be absent in Perplexity, so a gap on one engine is a distinct problem from a gap on another and needs its own fix.
Lay your results out engine by engine and look for the pattern behind each gap. If a competitor wins Perplexity but not ChatGPT, check whether they dominate the sources that engine favors, Reddit and community content weigh heavily on Perplexity, for instance. If they win ChatGPT, look at their owned content structure and third-party convergence. The fix follows the source: to close a Perplexity gap you may need community and review presence, while a ChatGPT gap may call for answer-first owned content. This is why per-engine reading matters, and the source-weighting differences are covered in what sources do AI engines cite.
What tools run an AI search competitor analysis?
Purpose-built AI-search tools like Profound, Peec AI, Otterly.AI, and LLM Pulse run the prompt sets, track citations across engines, and compute share of voice out of the box. Manual spot-checks work for a first pass, but the volume and drift of AI citations make a dedicated tool worth it for ongoing tracking.
LLM Pulse is a close fit if you want share of answer, share of citation, and share of mention with multi-model aggregation and weekly cadence, while Profound, Peec AI, and Otterly.AI offer engine-by-engine share of voice across large prompt volumes. The reason tooling matters is drift: AI citation patterns shift 40 to 60 percent month over month across major engines, so a quarterly manual check is too slow to catch a competitor’s move. Track monthly at minimum, weekly in fast-moving categories. The broader tool landscape is in best GEO tools 2026 and AI visibility tracking tools.
How often should you run the analysis?
Run it monthly at minimum, and weekly if your category moves fast. AI citation patterns drift 40 to 60 percent month over month, so a quarterly cadence misses competitor gains and your own decay until it is too late to respond quickly.
The frequency is a function of volatility. Because the engines re-weight sources and refresh citations constantly, your share of voice is a moving number, not a fixed rank you can check once a quarter. A monthly run catches most meaningful shifts and gives you a trend line; a weekly run is warranted when you are in an active competitive push or a category with rapid content turnover. Whatever cadence you pick, keep the prompt set and engines constant so the comparison stays honest, and log the results so you can see direction over time rather than a single snapshot.
Frequently asked questions
How is AI competitor analysis different from SEO competitor analysis? There is no single ranked list. You measure citation share across engines that cite different sources, so you compare per-engine visibility across a prompt set rather than keyword rankings on one results page.
Why measure each engine separately? Because the engines barely overlap. Only 11 percent of ChatGPT’s cited domains match Perplexity’s, so a blended number hides where you actually win and lose, and the fixes differ by engine.
What is a good AI share of voice? It depends on category and competitor count, but the useful target is out-citing your named competitors on the discovery and comparison prompts that drive shortlists, tracked as a trend rather than an absolute.
How many prompts do I need in the set? Enough to be stable, typically a few dozen per category, spanning discovery, comparison, and decision-stage questions, kept fixed across runs so month-over-month numbers stay comparable.
How often do AI citations change? Citation patterns drift 40 to 60 percent month over month across major engines, which is why monthly tracking is the floor and weekly is better for fast-moving categories.
Benchmark yourself against your top competitor
You cannot close a citation gap you have not measured, and the per-engine reality means the gap is probably bigger on one surface than you think. Claim your free AI visibility audit and we will run your category prompts across the major engines, show you where your competitors are winning citations, and hand you the gaps to close first. No pitch, just the benchmark.
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