Choose the comparison job before choosing a tool
A market-landscape study asks which companies sell a product and how their offers differ. An SEO study asks which pages rank or earn clicks in Google Search. An AI-answer study asks which brands are named, recommended, described accurately, or accompanied by displayed sources for a controlled question set. Decide which job the buyer needs before comparing dashboards.
For AI-answer work, define the competing products from real buyer alternatives and observed answers. Record aliases and parent brands so the same entity is counted consistently. Do not add unrelated companies just because a model mentioned them once.
Build matched buyer questions
Include category discovery, use-case recommendations, named comparisons, pricing or implementation questions, and brand-fact checks. Write down the market, language, user context, and the answer surface for every run. A consumer ChatGPT session, a model API response, Google AI Mode, and a traditional Google result page are different observations.
Freeze a baseline portfolio before changing content. Use the same question set and competitor definitions in a later check; place newly discovered questions in a separate exploration set. If a question asks for an agency service, do not score a software vendor as though it answered the same buyer need.
- Example discovery question: “Which AI search visibility tools show the sources behind brand mentions?”
- Example comparison question: “How do Digraph and an alternative differ on answer evidence and prompt limits?”
- Example accuracy question: “Which AI answer surfaces does Digraph directly collect?”
Save the answer-level record
For each attempt, retain the exact prompt, timestamp, platform or model, market, full answer, named brands, answer role, displayed citation URLs, and reviewer decision. Count failed or unusable runs separately and state the valid-answer denominator. A list of brand names without the answers cannot explain whether the model recommended, merely mentioned, or warned against a product.
A displayed citation is evidence of what the interface showed, not proof that the cited page caused a recommendation. If the surface provides no citations, record that limitation rather than interpreting an empty list as zero source influence.
Compare presence, framing, and evidence separately
Report mention and recommendation rates over the same valid-answer set. If using share of voice, state whether its denominator is valid answers, all tracked-brand mentions, or another unit; multiple competitors can appear in one answer. Review the wording of each recommendation and verify any product facts against current documentation.
Group the displayed source URLs by the claim they support. An owned product page, independent review, community discussion, and dated benchmark have different editorial roles. A competitor may have more mentions because the prompt portfolio favors its use case, so inspect prompt-level results before declaring a market-wide lead.
Turn a repeated gap into one test
Choose a gap that repeats across relevant questions: a missing product fact, an inaccurate comparison, a useful source competitors have and you lack, or an unclear category page. Assign one owner and one bounded change. Prefer improving a verifiable page or earning a relevant independent reference to manufacturing reviews or copying a competitor citation.
Log the baseline answers, action, publication date, and a recheck under the same scope. Repetition reduces the chance of mistaking normal answer variation for progress. A changed answer remains an observation; it does not by itself prove which page, link, or model update caused the change.
Keep Google Search and business outcomes beside the answer audit
Use Search Console to inspect Google impressions, clicks, and indexing for the pages you changed. Use qualified visits and leads to judge business value. Do not combine a Google search position, an AI mention, and an AI citation into one unexplained score.
The downloadable competitor worksheet keeps each answer and its denominator beside the comparison summary. It is a blank method template, not a Digraph ranking study or a claim about current competitors.
Compare the approaches
| Question | Evidence | Limit |
|---|---|---|
| Who offers a comparable product? | Product pages, pricing, and verified offer details | Does not show search or answer visibility |
| Which pages win Google Search? | Google results, Search Console, and dated rank checks | Does not measure AI-answer recommendations |
| Which brands appear in AI answers? | Matched prompts, full responses, roles, and displayed sources | Does not explain hidden model weights or all user sessions |
Common questions
How do I compare competitors in AI search?
Use the same buyer questions, market, language, platform, and valid-answer rules for each brand. Save full answers and displayed sources, then report mentions, recommendations, accuracy, and repeatability separately.
Is an AI competitor analysis the same as a Google keyword gap?
No. A keyword gap compares search-result or traffic visibility. An AI-answer audit compares generated responses under a stated prompt and collection method. Both can inform the same content decision without being one metric.
Can one answer prove a competitor is ahead?
No. An answer can vary by prompt, surface, context, and time. Repeat relevant questions and report the sample and limits before describing a pattern.
Sources and methodology
Digraph publishes this guidance and provides AI visibility software. Worked examples are illustrative unless explicitly identified as measured results. A research sample does not establish total market coverage or guarantee future recommendations.
Run a matched comparison
Use the blank worksheet to keep the prompt, answer, sources, denominator, and next test together.
Open the competitor comparison worksheet