Strongest signal available
When 3 LLMs independently mention you, it's nearly impossible for that to be noise or hallucination.
AI Visibility · Deep Dive
Cross-LLM Consensus.
Entity consistency compounds. When three independently-trained LLMs agree, you have the strongest signal possible.
Cross-LLM Consensus, defined.
Cross-LLM Consensus is a per-query measurement of how often multiple large language models independently surface the same brand entity in their answers — calculated as the pairwise intersection of entity mentions across ChatGPT, Claude, and Gemini, averaged over the probed query network. Backed by research on entity identity resolution and the Google patents covering knowledge graph alignment.
Entity resolution research consistently shows that consistency is the single strongest signal for search systems. When ChatGPT, Claude, and Gemini independently resolve the same entity to the same brand, it means your entity profile is deeply encoded across training corpora — not a fluke of one model's data. Consensus is the core of Patnick's Clarity dimension.
Four concrete outcomes.
Clarity score input
Cross-LLM consensus is the largest component of the Clarity dimension in Patnick's three-score model.
Measurement stability
Consensus signals are more stable over time than single-LLM measurements — they don't flip on model updates.
Identify blind spots
When only 1-of-3 LLMs mentions you, the other two have a blind spot you can target with specific fixes.
The 4-step process.
- 01
Group probes by query
All probe runs for the same query are grouped across all 3 LLMs.
- 02
Count mentioning LLMs
For each query, count how many LLMs have brand_mentioned = true.
- 03
Compute agreement ratio
mentionRatio = mentioning / probed. Symmetric agreement around 0.5 is weakest, 0 or 1 is strongest.
- 04
Average across queries
The average positive-consensus ratio becomes the Cross-LLM Consensus score (0-100).
See it in the dashboard.
This is how cross-llm consensus surfaces inside the real Patnick dashboard. Enter the your audit to click through it.
patnick.com/dashboard
Related capabilities in AI Visibility.
Frequently asked questions.
- What is cross-LLM consensus?
- Cross-LLM consensus measures how often multiple independent large language models agree that your brand entity belongs in the answer to the same query. If ChatGPT, Claude, and Gemini all surface your brand for 'best running shoes', consensus for that query is 100%. If only Claude does, consensus is 33%. Averaged across your probed query network, it becomes a per-site consensus score that dominates the Clarity dimension in the 3-score model.
- Why is consensus the strongest signal?
- Entity resolution research consistently shows that signal consistency is what compounds in search systems. Any single LLM mention could be a quirk of that model's training corpus or alignment process. When three independently-trained models converge on mentioning the same entity, it means your brand is genuinely resolved across the training data landscape — not a statistical accident. This is the same principle that underpins the Google author rank and entity-oriented search patents: consistent signaling over time and across contexts outperforms spiky high-volume signaling every time.
- How does consensus feed the Clarity score?
- Cross-LLM consensus contributes 0.5 of the Clarity dimension weight (dominant). The other components are sentiment consistency (0.3) — low variance in how LLMs describe you — and citation density (0.2) — fraction of mentioning probes that include a URL back to your domain. Consensus is the single biggest lever: improving entity identity and schema markup typically moves consensus 5-10 points, which moves Clarity 2.5-5.
- What's a good consensus score?
- 75+ is excellent — your entity is firmly in the canonical answer set across all three LLMs. 50-75 is good but has per-model gaps worth investigating. 25-50 means only one model resolves you reliably, and the signal is fragile. Below 25 means you're essentially invisible to 2 of 3 LLMs, which is a Clarity crisis. Because consensus requires three independent shifts to move, scores change slowly — and that's good news when you're climbing.
- Can consensus be high while presence is low?
- Yes, and it's one of the most revealing patterns. If your brand is mentioned by all 3 LLMs for the 10% of queries where it's mentioned at all, consensus is 100% but presence is 10%. This says your entity is crystal-clear in a narrow niche. The correct response isn't to 'fix consistency' (there's nothing to fix) — it's to expand coverage into adjacent query networks while preserving the entity consistency that's already working. The Demand dimension will rise without hurting Clarity.
- How often does consensus shift?
- Slowly. Because consensus requires three independent models to change simultaneously, it's the most stable signal in the 3-score model. Typical month-to-month movement is 5-10 points as models update or your entity profile strengthens. Dramatic swings usually indicate a provider release (GPT-5 → GPT-5.5) rather than a real visibility change — and when that happens, the movement propagates across every brand in that model's corpus, not just yours. Patnick flags these provider-release events so you don't misread them as your own performance changes.
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