Benchmarking Your Brand Against Competitors With an AI Visibility Audit

What a competitor benchmark in AI answers measures

Classic competitive SEO compares rankings. An AI benchmark compares share of answer: across a fixed set of buyer questions, how often each brand is named, where it appears in the answer, how it is described and which sources the engine cites for it. Those measures matter more than any single ranking, because an AI answer usually presents a short list and buyers rarely look beyond it.

The benchmark should cover the engines your buyers use: ChatGPT, Perplexity, Google AI Overviews or AI Mode, and Copilot at minimum. Results differ between engines, and the differences are often the most useful finding, because they point to the sources each engine trusts.

Cover the languages your buyers use as well. Answers to the same question in Arabic, German or English can name quite different brands, so a single-language benchmark can badly misread your position in a multilingual market.

Your AI competitors may not be your sales competitors

Most teams start with the rivals their sales team names. AI engines often surface a different set: a larger brand from an adjacent category, a well-reviewed newcomer, or a comparison site that answers the question itself. A good audit lists every brand named across the prompt set before narrowing to the main competitors for detailed comparison.

This step regularly changes priorities. If one review site is cited in most answers, it is effectively a gatekeeper, and an accurate listing there may be worth more than anything you publish yourself. If a rival appears mainly because of one well-structured comparison page, you know exactly what you are competing against.

It is also worth recording brands that appear on only one engine. A rival that dominates Perplexity but barely appears in ChatGPT is usually winning through a specific set of cited sources, which is a narrower and more beatable advantage than it first looks.

Building a fair prompt set and sampling method

A benchmark is only as good as its questions. Build the prompt set from real buyer language: sales calls, site search, Search Console queries and support tickets. Cover the funnel stages, from broad category questions to head-to-head comparisons and objections. Twenty to fifty prompts per market is a practical range.

Then control the variables. Run each prompt several times, since AI answers vary between runs, and from the locations and languages your buyers use. Log the date, engine and mode. Report results as rates across runs, not single screenshots, and keep the prompt set fixed so the next benchmark compares like with like. A benchmark that changes its questions every month cannot show progress.

Reading the gaps: why rivals get named

Once you know who wins, the audit asks why. Compare the cited sources: are rivals referenced from review sites, industry publications or their own pages? Compare content readiness: do their pages state fees, features or credentials in plain sentences an engine can lift? Check access: can AI crawlers reach your pages, including past CDN and firewall rules that block bots while robots.txt allows them? And check entity consistency: do your name, products and facts match everywhere they appear?

The output should be a short list of specific, fixable reasons, not a general instruction to produce more content.

Weight the gaps by commercial value. Losing a broad category question matters, but losing head-to-head comparisons and cost questions usually matters more, because those buyers are closer to choosing. A good audit ranks the fixes with that in mind rather than treating every prompt as equal.

Worked example: a broker benchmarked against three rivals

Take a forex broker competing in the Gulf against three better-known brands. The benchmark runs forty English and Arabic questions, from which brokers are licensed in the UAE to comparisons of specific account types. An illustrative result: the broker is named in a small share of answers, while one rival appears in most, largely because two regional comparison sites list it first and both are cited heavily.

The broker also appears with an outdated minimum deposit taken from an old directory listing. The fixes are concrete: correct the listing, earn accurate coverage on the two comparison sites and publish clear account-terms pages in both languages. This illustrates the method; it is not a client result.

What Suggesting.ai does with a benchmark

Suggesting.ai's first audit is free and delivered within 48 hours, and it includes a competitor benchmark on the buyer questions you care about. You get the prompts, raw answers, brands named, sources cited and the reasons behind each gap. From there we run organic GEO to close those gaps and, where your category and market are eligible, ChatGPT Ads to appear in sponsored placements on high-intent conversations.

For forex brokers, OpenAI's policy generally restricts financial-services ads outside the US, so we confirm eligibility rather than assume it. The goal is that when AI is suggesting brokers, you are named alongside the leaders, not after them.

Re-benchmarking and reporting

Repeat the benchmark monthly with the same prompt set and report share of answer, average position, citation share and accuracy for each competitor. Pair it with your own traffic data: AI referrals, where ChatGPT links carry utm_source=chatgpt.com, and Search Console Web data, which includes AI Overviews and AI Mode. Watch for sudden shifts in a rival's share; they often mean the rival earned a new citation source, which you can then inspect and answer.

Share the benchmark beyond marketing. Sales teams can prepare for the objections AI raises about you, product teams can see which features engines associate with rivals, and leadership gets a simple, comparable view of competitive position that does not depend on any one channel's reporting.