The short answer: measure everyone on the same yardstick. Run a single panel of category prompts on a schedule, record which brands each engine names in each answer, and compute per-brand mention rates and share of voice from the results. The number to watch is the competitor gap — your visibility minus theirs, trended over time. It tells you who's winning your category's AI answers and whether your work is closing the distance.
Why one yardstick, not per-brand checks
The instinct is to ask engines about each competitor separately — "tell me about Acme," "tell me about Initech." That measures the wrong thing. Buyers don't prompt about brands they haven't discovered; they ask category questions — "best CRM for small agencies," "alternatives to the market leader" — and the answer names whichever brands it names. Competitive AI visibility is about who appears in those shared answers, so every brand must be scored against the identical prompt panel, on the same engines, over the same window. Change the prompts between brands and the comparison is meaningless.
Build the panel from real buyer questions: best-of queries, alternatives-to queries, head-to-head comparisons, and problem-shaped questions where your category is the solution.
The metrics that fall out
From one sweep of answers, three competitive metrics emerge:
- Per-brand mention rate — the fraction of answers naming each brand. The atomic comparison: if a rival appears in 60 percent of answers and you appear in 20, that's the headline.
- Share of voice — each brand's percentage of all brand mentions in the category's answers. Because an AI answer is zero-sum shelf space, share framing shows who gains when someone loses.
- Competitor gap — your visibility minus a rival's, trended. Single snapshots are noisy; the trend line is the verdict on whether your GEO work is closing distance or watching it widen.
Break each one out per engine. Competitors are frequently strong on one engine and absent on another — a rival owning Perplexity but not ChatGPT points at the retrieval sources feeding Perplexity, and tells you exactly where to compete.
From numbers to moves
Tracking pays off in the specific answers, not just the aggregates:
- Read the answers where competitors appear and you don't. Note the framing and follow the citations — those cited pages are the reading list your category's answers get written from, and your placement targets.
- Watch prompts where you're compared head-to-head. How engines summarize you-versus-them is your AI-era sales narrative; wrong claims in a comparison are urgent fixes.
- Alert on step changes. A rival's sudden jump usually traces to something findable — a new comparison page, a burst of reviews, fresh coverage — visible in citations within days, and often worth emulating.
Automating the loop
Nothing here requires a tool — run the prompts, tally the brands, keep the spreadsheet. What's hard to sustain manually is consistency: dozens of prompts across four engines on a fixed schedule with uniform scoring, week after week, is exactly the drudgery that erodes. Brandflare's competitor tracking runs your panel against ChatGPT, Gemini, Perplexity, and Claude in scheduled sweeps, scores every answer with the same LLM judge, and charts share of voice and gap trends per engine. A free audit shows the starting picture — you against your category's incumbents — in minutes.
Once you can see the gap, the question becomes how to close it: start with how to improve AI visibility.