We will cover what these metrics are, why it’s important to track them, and any parallels with traditional SEO, and where AI discovery is completely different. Let’s get started!
1. AI Presence Rate (APR)
What it is:How often your brand appears at all in AI-generated answers for queries relevant to your business. Why it matters:
If you don’t appear, you don’t exist. This is the AI equivalent of “are we even indexed?” in SEO. SEO Comparison:
Closest match is impressions in Google Search Console — but APR is more binary and context-driven.
2. Answer Share of Voice (AI-SOV)
What it is:Percentage of AI answers where your brand is mentioned vs. competitors for the same prompt. Why it matters:
It shows how often AI assistants choose you as the exemplar, recommended tool, or authoritative source. SEO Comparison:
Traditional SOV + keyword ranking share blended together.
3. AI Recommendation Rate (AIRR)
What it is:How frequently an AI agent explicitly recommends your brand (“Use X”, “The best tool for Y is…”). Why it matters:
Recommendation intent is more valuable than visibility. LLMs often have a preference to suggest one top recommendation. Being that one top recommendation matters. SEO Comparison:
Closest thing is featured snippets or “Top Pick” carousels, but AIRR is more conversational and opinionated.
4. Contextual Relevance Score (CRS)
What it is:How accurately an AI model describes your category, features, pricing, integrations, etc. Why it matters:
LLMs hallucinate. You need to know whether they’re misrepresenting your brand. SEO Comparison:
Equivalent to content accuracy and brand SERP quality, but more difficult to control because it’s model-driven.
5. Citation Rate (CR)
What it is:How often the AI system cites your website, docs, or content as a source. Why it matters:
Cited content influences how models learn and generate responses. It’s the AI-era equivalent of backlinks. SEO Comparison:
Maps closely to backlinks, but instead of humans linking to you, models choose your content when grounding.
6. Brand Sentiment in AI Outputs
What it is:The overall sentiment associated with your company/brand. Are outputs positive, neutral, or negative? Why it matters:
Even if you show up, you don’t want the AI saying “this product is outdated” or “customers complain about…”. SEO Comparison:
Similar to online sentiment tracking, but AI-generated text influences millions of answers automatically, not just a few reviews.
7. Model Latency to Update (MLU)
What it is:How long it takes for different AI systems to reflect new product info, pricing, features, or brand changes. Why it matters:
Some models lag months behind, which can hurt accuracy. SEO Comparison:
Similar to Google recrawl speed, but for LLMs the delay is often much longer.
8. Brand Definition Stability
What it is:How consistently the model describes your brand over time and across queries. Why it matters:
Instability signals outdated training data or poor grounding. SEO Comparison:
Closest match: brand SERP stability — the consistency of branded search results.
Bonus Section: What Matters Most Today
Although all of these metrics provide value, if you need to prioritize, focus on the following:- AI Presence Rate (APR) — baseline visibility
- Answer Share of Voice (AI-SOV) — competitive comparison
- AI Recommendation Rate (AIRR) — actual influence
- Brand Sentiment in AI outputs — reputation risk control