For the paid search side of this shift, see How Are AEO and AI Overviews Changing Paid Search in 2026?
JDM starts with an LLM visibility and competitive analysis in Profound. We find the prompts where competitors are named and you aren't, then look at why: which pages are cited, how their answers are structured, and which outside sources support them.
From there, we build content aimed at mid- to bottom-funnel intent, such as comparison, "alternatives," and "best software for" pages. We describe your differentiators in factual, specific language AI systems can easily rephrase, and keep comparisons balanced so AI systems treat them as trustworthy. Schema markup and internal linking help AI systems understand how the pages fit together and reinforce topical authority.
Measurement runs prompt by prompt. We track mentions, citations, and position for each target prompt across the major AI platforms, alongside AI referral traffic and pipeline, so you can see which content is earning visibility and what it's worth.
This approach is reflected in JDM's work with LinkSquares, an AI-powered contract lifecycle management platform competing with Ironclad and DocuSign:
Read the full LinkSquares case study.
Start with a list of the prompts your buyers actually ask, then check which ones already name your product and which name competitors. Fix technical access first: make sure your key pages are indexed and that robots.txt doesn't block AI search crawlers, then rework or build pages so each opens with a direct answer, add comparison and use-case content for gaps, and track results prompt by prompt. Strong SEO and AEO reinforce each other, since pages that rank well are more likely to be retrieved and cited. JDM's AI and LLM search services follow this sequence.
Choose a platform based on which AI engines it tracks, how many prompts it can monitor, and whether it shows the URLs being cited, not just whether your brand was mentioned. Look for competitor tracking, position and sentiment data, and exports or integrations that let you connect visibility to traffic and pipeline. Dedicated AI visibility trackers go deepest on prompt monitoring, while broader SEO suites are adding AI features on top of keyword data. JDM is a Profound partner and uses it for client tracking.
It depends on whether you want a chatbot inside your product or your brand to show up inside ChatGPT, Perplexity, and Gemini. For an in-product assistant, conversational AI and retrieval platforms handle the integration. For visibility in public chatbots, there's no plugin to install: AI systems decide what to cite from crawlable content and third-party sources, so the work is content structure, authority, and tracking.
Both tools build recommendations from sources they find on the web, so you need clear product information on your site and consistent coverage on the third-party sites they cite. Publish pages that state who your product is for, what it does, and how it compares, and keep those details consistent across your site, review profiles, and listings. Earn mentions in category roundups and reviews, and check that robots.txt allows OAI-SearchBot and PerplexityBot. See JDM's playbook on building authority that gets cited.
Track metrics in three layers: visibility (mention rate, citation count, and position on target prompts), engagement (AI referral sessions and conversion rate), and business impact (demo requests, pipeline, and revenue from those sessions). Compare your mention rate against competitors on the same prompts to show share of voice. Add branded search volume and self-reported attribution, since many buyers see you in an AI answer and come back later. For measuring a related channel, see how JDM measures Reddit's impact on AEO and SEO.
Compete on specific prompts, not the whole category: comparison, "alternatives," and use-case prompts are where AI systems weigh options instead of naming the default leader. Publish balanced comparison content that states your differentiators plainly, and support it with reviews and third-party mentions. LinkSquares used this approach against Ironclad and DocuSign and grew AI search citations more than 275% month over month (case study). For more tactics, see 5 Ways SMBs and Challengers Can Outmaneuver Bigger Competitors in AEO.
Open each page or section with a one- to two-sentence answer, then add detail, examples, and proof. Use question-based headings, short paragraphs, comparison tables for features and pricing, and clear statements of who the product is for. Add relevant schema, such as Article, FAQPage, and SoftwareApplication, and link related pages together so AI systems see the full topic.
SEO aims to rank pages in search results; AEO aims to get your product named or cited inside AI-generated answers. The foundations overlap, including crawlability, quality content, and authority. AEO puts more weight on extractable answers, third-party mentions, and comparison content, and it's measured by prompts and citations rather than keyword rankings. For SaaS, the two work together, since well-ranking pages are more likely to be retrieved by AI search.
In GA4, create a custom channel group that captures referral sources such as chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, and claude.ai. ChatGPT often adds a utm_source parameter to the links it cites, which makes those visits easier to spot. Google AI Overviews clicks are reported as regular Google organic traffic, so track those through prompt-level citation monitoring instead. Connect AI referral sessions to your CRM to see which ones turn into demos and pipeline.
New or updated pages can start earning citations within weeks once they're indexed, but steady growth usually builds over several months. Results depend on domain authority, how competitive the prompts are, and how much third-party coverage you have. For LinkSquares, a new comparison page drove a 275% month-over-month increase in AI search citations, with inclusions in AI Overviews, ChatGPT, and Gemini (case study).
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