How to Track Competitors in AI Answers

Knowing how competitors perform in AI answers is as important as tracking your own mentions. Competitor AI visibility data tells you who you are really competing against for AI recommendations, which queries are contested, and what tactics your competitors are using that you are not.

This guide covers how to systematically track competitor mentions in AI answers and turn that data into actionable insights.

Why competitor tracking matters for AEO

In traditional SEO, you know your competitors from keyword research: the same sites that rank for your target keywords are your competitors. In AI search, the competitive landscape is different.

Your pricing-page competitor may not be who ChatGPT recommends most often in your category. A product that does not even directly compete for customers may occupy significant AI share of voice because of its content strategy and review site presence.

Competitor AI tracking shows you:

• Your actual AI competitors (not just who you think you compete with)
• Which queries are most contested and which have gaps
• What content and distribution strategies are driving each competitor’s AI mentions
• Where you are losing recommendations you should be winning

Step 1: Discover your AI competitors

Your AI competitors are the brands that appear when potential customers ask AI assistants about your product category. They are not necessarily the same as the companies you compare to on your pricing page.

Run 10 of your core category queries in ChatGPT and list every brand mentioned. You will likely find:

• 2-3 brands you already know are direct competitors
• 1-2 brands that are adjacent (slightly different use case) but appear in the same recommendation lists
• Occasionally, a brand you have never heard of that has somehow become a default AI recommendation

All of these are your AI competitors. The ones you are unfamiliar with deserve closer investigation - they may have a content or review strategy worth studying.

Step 2: Parallel tracking

The most valuable competitor data comes from running the same queries at the same time for yourself and your competitors.

Structure your tracking like this:

• Query 1, Run 1: does your brand appear? does Competitor A? does Competitor B? what position for each?
• Query 1, Run 2: same
• Query 1, Run 3: same
• Query 2… and so on

This creates a synchronized dataset where the conditions are identical, making comparisons valid. If you track your brand on Monday and competitors on Friday, you introduce timing noise that makes comparison less reliable.

Step 3: Pay attention to framing

AI assistants do not just recommend products - they position them. “Great for enterprise teams but expensive” versus “best value for bootstrapped founders” are very different framings even if both mention your product.

Record the framing for competitor mentions, not just whether they appeared. Common framing patterns:

• Price positioning (“more affordable than X”, “premium option”)
• Use case focus (“best for teams”, “solo founder friendly”)
• Feature differentiators (“has the most integrations”, “easiest to set up”)
• Caveats (“less customizable than alternatives”, “limited analytics”)

Understanding how AI frames your competitors tells you how AI perceives the competitive landscape - and whether your own positioning needs adjustment.

Step 4: Citation analysis for competitive intelligence

Perplexity’s citation feature makes it a goldmine for competitive intelligence. When Perplexity recommends a competitor, it cites the source. That source is where the AI learned to recommend that competitor.

Build a citation map for your top 3 competitors:

• Which review sites are cited? (G2, Capterra, Product Hunt)
• Which third-party comparison articles are cited?
• Are any competitors’ own blog posts being cited?
• Are any aggregator directories being cited?

For each high-frequency citation source, ask: can I get listed, featured, or covered there? The citation map is a direct content and distribution roadmap.

Step 5: Gap analysis

The most valuable output of competitor tracking is a gap list: queries where competitors appear but you do not.

For each gap query:

1. Note which competitor appears (it might be different competitors for different queries)
2. Note the citation source if available from Perplexity
3. Assess why they appear and you do not: stronger review presence? A specific piece of content? More mentions on the cited source?
4. Estimate the effort to close the gap versus the potential impact

Prioritize gaps where:

• The query has clear buyer intent (someone asking this is close to purchasing)
• The reason for the gap is addressable (e.g., you just need more G2 reviews, not a complete product rebuild)
• You have a genuine claim to the recommendation (you actually solve that use case well)

Turning competitor data into action

The goal of all this tracking is to change what you do, not just to know things.

A useful framework for acting on competitive gaps:

• If a competitor appears due to more reviews on a citation site: start a review generation campaign targeting that site
• If a competitor is cited from a blog comparison post you are not in: reach out to the author or create a better piece
• If a competitor appears on a query type you have never created content for: create a targeted piece addressing that query
• If a competitor appears with consistently positive framing and you appear with caveats: investigate whether the caveat is fair and address it in your product or content

AskRank automates competitor tracking across all four major AI assistants, shows you a competitor bar chart on the dashboard, and lets you drill down into per-query and per-LLM data. The Pro plan tracks 3 brands (yours plus 2 competitors) across 75 prompts.

See also: How to measure AI share of voice, AskRank vs Otterly, and AI share of voice in the glossary.