How to Optimize for AI Searches - Competitor Research (Part 7 of 7)

See exactly where our competitors outrank we in AI answers. Zeover benchmarks we against our competitors across ChatGPT, Claude, Gemini, and Grok and identifies the queries where they appear and we don’t. Start our competitive analysis.

This is the final part of the series on how to optimize for AI searches. The previous six covered everything on a brand’s own site: llms.txt, schema markup, machine-readable content, brand boilerplate, content cadence, and measurement. This part is about what competitors are doing - and how to use that intelligence to find opportunities we’re missing.

Generative Engine Optimization isn’t a solo game. Our competitors are optimizing too. Some are ahead of we on certain queries. Some are quietly rewriting their boilerplate to capture an emerging keyword we haven’t noticed. Some are publishing a volume of content that positions them as the authority in a sub-category we could legitimately claim. Competitor research is how we find those signals, respond to them, and occasionally leap ahead.

TL;DR

• AI engines don’t just show we in isolation - they show we next to (or instead of) our competitors.
• Run the benchmark queries and record which competitors appear most often. These are the strongest AI-search rivals, which may not match traditional SEO rivals.
• Look for high-volume keywords competitors are embedding into their boilerplate and positioning. If they fit the brand, embed them into its own.
• Analyze what formats competitors use for their most-cited content - the gaps are where opportunities live.
• Zeover runs competitive analysis automatically across ChatGPT, Claude, Gemini, and Grok.

Our AI-Search Competitors May Not Be Who We Think

The competitors showing up when users ask AI engines about a category may be different from the ones a brand thinks of as direct rivals. Traditional competitive analysis groups companies by product similarity, pricing, target market, or marketing overlap. AI-search competition groups companies by whoever AI engines find citable on the queries customers ask. This is the core of the SEO vs. GEO competitor gap: a brand’s SEO competitors and its GEO competitors are rarely the same list. SEO competitor maps are built on keyword overlap and SERP adjacency. GEO competitor maps are built on who AI engines actually cite when users ask about the category. The two diverge because AI engines weigh structural clarity, entity relationships, and cross-platform consistency differently than traditional ranking algorithms.

An independent boutique firm might compete with major enterprise vendors in AI answers purely because their content is better structured. A B2C consumer brand might compete with B2B platforms in AI answers because the query is ambiguous enough to surface both. A small startup might appear with multi-billion-dollar incumbents because they wrote one exceptionally well-cited blog post.

The first step in competitor research is letting AI engines show who the real competitors are. Run the benchmark queries and record which other brands appear. The names that show up repeatedly are the AI-search competition, regardless of whether traditional market research identifies them as rivals.

Step 1: Map Who Appears Instead of We

Take our full query list and run each query through ChatGPT, Gemini, Perplexity, and Claude. For every query where our brand doesn’t appear, record which brands do. Keep a running tally.

After 20-30 queries, we’ll have a clear picture of which competitors dominate the queries where we’re absent. These are the brands to focus on. They’re already doing something we aren’t.

For each recurring competitor, build a quick profile:

• What are the top 3 pages on their site that appear as citations?
• Do they have a consistent boilerplate across LinkedIn, their homepage, and press releases?
• What content formats do they rely on (blog posts, case studies, white papers, YouTube)?
• How often do they publish?
• What keywords appear in their H1s, H2s, and meta descriptions?

Step 2: Find the Keywords They Own That We Could

Look at the cited pages from our top competitors. What keyword or phrase does each page target? Frequently, we’ll find that one or two keywords appear across multiple competitor pages but are missing from our own content completely.

Example: if we sell HR software and three of our competitors have pages targeting “AI-powered applicant tracking,” but our homepage and product page don’t include that phrase, we’ve identified a specific keyword gap. AI engines are learning to associate our competitors with that keyword cluster. We aren’t in the conversation.

Some of these gaps are legitimate. If competitors have gone deep into a sub-category that isn’t core to our product, we might deliberately leave it alone. But frequently, the gap is just an oversight. A keyword that fits our brand but never made it into our boilerplate, our homepage, or our content calendar.

When one of these is found, add it to the boilerplate. Update positioning language to include the keyword where it naturally fits. Rewrite the top-ranking page to reference it. Publish new content specifically targeting it. Within 4-8 weeks, AI visibility on that keyword should start moving.

Step 3: Analyze Competitor Content Formats

Different AI engines prefer different content formats. Competitor research surfaces which formats are working in the category specifically.

Look at the cited pages and count the format types:

• Listicles (e.g., “10 Best X for Y”) get cited at materially higher rates than standard blog posts in industry citation analyses.
• How-to articles get cited heavily by ChatGPT for instructional queries.
• Comparison posts (X vs Y, alternatives to X) get cited for displacement queries.
• Case studies with specific customers and metrics get cited for credibility queries.
• YouTube videos hold a 29.5% citation share in Google AI Overviews.

If competitors are winning citations mostly through listicles and comparison posts, and the content library is 90% narrative blog posts, there’s a format gap. Rebuilding some of the content library around listicles and comparisons - without sacrificing depth - can make a difference quickly.

Step 4: Reverse-Engineer Their Boilerplate

Read our competitors’ homepages, About pages, and press release boilerplate. Note the exact phrases they use to describe what they do.

We’re looking for:

• The category terms they claim (and what AI engines now associate with them)
• The differentiators they lead with
• The customer segments they name explicitly
• The numbers or milestones they front-load (funding, customers, scale)

If multiple competitors all describe themselves as “the leading [X]” or claim a specific category like “AI-powered workflow automation for mid-market enterprises,” AI engines are aggregating those descriptions into a consistent picture of that category. Our own boilerplate either plays into that picture or tries to carve out an adjacent claim. Either strategy can work - but drifting somewhere between the two (neither fitting the established category nor clearly differentiated from it) is the worst outcome.

Step 5: Check the Queries They Own for Embedding Opportunities

Some queries in the category have high volume and clear commercial intent. “Best project management software for remote teams.” “Top [X] platforms for startups.” “Category with integrations to Popular Tool.”

If our competitors have captured these high-volume queries and we haven’t, the fastest response is embedding the relevant keywords into our brand’s boilerplate and positioning. This doesn’t mean keyword stuffing. It means identifying the one or two queries that represent the largest pool of potential customers asking about our category, and making sure the brand’s own content reflects those queries explicitly.

Tactical steps:

• Add the keyword to the homepage H1 or subhead if it fits naturally.
• Include it in the canonical boilerplate so every press release reinforces it.
• Create a dedicated landing page targeting the exact query.
• Publish content that earns citations on the query.

Keyword research used to mean finding phrases with high search volume and low competition. For AI visibility, it means finding phrases competitors are being cited for that the brand should be cited for too.

How Often to Do This

Competitor research isn’t a one-time exercise. New competitors emerge. Existing competitors rewrite their positioning. AI engines update their training data. What was true about the competitive landscape in Q1 may not hold by Q3.

A reasonable cadence:

• Quarterly deep dive. Full competitor mapping, boilerplate analysis, content audit. Takes a day or two and sets strategy for the quarter.
• Monthly tracking. Run the benchmark queries and track which competitors are moving up or down. Flag significant shifts.
• Weekly scan. Skim our competitors’ blogs and press pages for new content. Note anything that could shift the competitive picture.

How Zeover Automates Competitor Research

Zeover runs this analysis continuously. Define our competitors once, and the platform tracks their visibility with ours across ChatGPT, Claude, Gemini, and Grok. We see side-by-side comparisons for every query, visibility trends over time, and specific gaps where they appear and we don’t.

When a competitor publishes new content that moves their visibility, the change appears in the next benchmark run. When a gap is closed with new content, the shift is visible. The measurement loop that would take hours per week to run manually happens in the background.

That’s the Series

This is the end of the How to Optimize for AI Searches series. Seven parts, one complete playbook:

1. Start with your llms.txt
2. Schema markup is not optional anymore
3. Make your content machine-readable
4. Lock our brand boilerplate across every channel
5. Content, content, content
6. Measure, benchmark, and iterate
7. Competitor research (this post)

Each part builds on the last. Together they form a complete approach to AI search optimization. Most brands won’t run all seven steps in-house. The ones that want to compete in AI answers without staffing an internal GEO team use Zeover to handle the pipeline: audit, remediation, content generation, measurement, and ongoing competitive intelligence.

The brands winning AI visibility today aren’t the ones with the biggest budgets. They’re the ones that optimize for AI searches consistently across all seven steps. Start with part one and work through to the end.