Is Your Competitor Getting More AI Recommendations Than You? Here's How to Find Out

You already know how your brand performs in Google. You probably don't know how it performs in AI search — or how that compares to your top competitor.

That gap is where business is being won and lost right now.

The AI Recommendation Asymmetry Problem

For most categories, AI doesn't recommend all brands equally. It has clear preferences — often different from what Google rankings would predict — based on how brands are represented in its training data.

The result is an asymmetry: for the same query ("best CRM for small teams"), one brand gets recommended consistently across AI engines, while its close competitor gets mentioned occasionally, or not at all.

If you don't know which side of that asymmetry you're on, you're flying blind in a market that's shifting toward AI-first search.

Why AI Competitive Rankings Differ From Google Rankings

Google's ranking logic is transparent enough that industries have built optimization playbooks around it. AI recommendation logic is more opaque, but some patterns are consistent:

Content format matters more than content volume.
A competitor with 10 structured comparison articles will outperform a brand with 100 undifferentiated blog posts in AI recommendations. AI models cite content that clearly answers "X vs Y" or "best X for Y" — not content that describes features.

Third-party citation counts more than self-published content.
If your competitor has been mentioned in 20 independent reviews, Reddit threads, and industry newsletters, AI treats them as more credible — even if your official content is more comprehensive.

Chinese and English AI rankings are often completely different.
A brand that dominates ChatGPT may barely appear in Kimi. A brand with strong Zhihu and Xiaohongshu coverage may outperform in DeepSeek while underperforming in Claude. The split is real and actionable.

How to Run a Competitive AI Visibility Analysis

Step 1: Define your comparison pair

Pick your primary competitor — the one you most often lose deals to, or the one whose positioning most closely overlaps with yours.

Step 2: Run identical scans on both brands

Use the same keyword category. Run your brand. Run theirs. Compare:

• Overall score (0–100)
• Discovery Score: who gets recommended more in category searches?
• Brand Score: whose narrative is more accurate and consistent?
• Engine breakdown: which AI engines favor them, which favor you?
• Scenario breakdown: which query types do they win, which do you win?

Step 3: Find the specific scenario gaps

The most actionable output is the scenario breakdown. If your competitor scores 80% on recommendation queries and you score 30%, that's a specific content type problem. You're not losing on brand quality — you're losing on content format.

Step 4: Trace the gap to source coverage

If they're winning in Claude but not Kimi, they have strong English-language third-party coverage and weak Chinese platform coverage. That maps directly to where you should be creating content.

What Competitive AI Data Looks Like in Practice

A real example pattern we see repeatedly:

Brand A (market leader by revenue) vs Brand B (faster-growing challenger):

Brand A has a better brand score — AI describes them more accurately. But Brand B gets recommended far more often in category searches because they've invested heavily in comparison content and third-party coverage in English markets.

Brand A wins in Kimi because they've invested in Chinese-language content. Brand B hasn't.

This kind of breakdown tells you exactly where to compete — not "do more GEO" but "create comparison content in English" or "build Zhihu presence."

Three Competitive Signals to Watch

1. Who AI recommends first in category searches
Position matters. Being mentioned third in an AI recommendation list is not the same as being mentioned first. Track not just whether you're cited but where.

2. Who AI cites in "vs" queries
When users ask "[Your Brand] vs [Competitor]", AI's answer often determines the sale. Brands with structured, specific comparison content tend to win this scenario systematically.

3. Which AI engines you're losing in
Don't optimize for all AI engines equally. Find which engine is your biggest gap vs your competitor and focus content creation there first.

The Window Is Still Open

In traditional SEO, first-mover advantage hardened into a near-permanent competitive moat over years. In GEO, that process is still in its early stages. The brands establishing AI recommendation dominance now are doing so before their competitors realize it's a contest.

Run your competitive AI visibility comparison at anchor.agentese.ai — enter your brand name and a competitor, and the report shows exactly where the gap is and what content would close it.

Anchor measures AI brand visibility across major AI engines. Competitive benchmarking is included in every report.