How each AI engine positions your brand
One AI Visibility number hides a more useful truth: the engines don’t agree about you. The AI Ecosystem view shows how each one mentions, recommends and cites you — and where one is quietly out of step with the rest.
ChatGPT, Claude, Gemini, Perplexity and Grok each read a different slice of the web. So one engine may recommend you confidently while another never brings you up, and a third cites a single stale source and weights it heavily. Averaged into a single score, that story disappears. Broken out by engine, it becomes a map of exactly where to act.
Four behaviours, measured per engine
Mention
How often each engine brings your entity up at all — the base rate of presence in its answers.
Recommendation
How often it actively recommends you, not just names you — the commercial difference between being known and being chosen.
Citation
How often it cites a source about you, and which sources — the provenance behind what it believes.
Coherence
Whether the engines are telling one consistent story or fragmenting — and which engine is the outlier.
The per-engine rates are grounded— computed from observed signals, each carrying provenance, a sample size and a confidence level. The engine-fit summary and strategic narrative on top are Entidex’s own assessment, and are labelled as such. You always know whether you are reading a measured fact or a strategic read.
Find the outlier, fix the association
When one engine is the odd one out, it is almost always working from a weaker or staler picture than the rest. The Ecosystem view points straight at it — which engine, on which behaviour — so you can target the associations behind the gap instead of guessing. Pair it with LLM Knowledge Accuracy to see whether the outlier is also getting facts wrong, and cross-engine consensus to watch the coherence move over time.
Go deeper with Entidex
The full entity intelligence platform — beyond Explore Entidex
- Continuous multi-source entity observation
- Alerts when sources diverge or drift
- Cross-surface consensus + visibility over time
- Evidence-anchored intelligence reports
Frequently asked questions
What is the AI Ecosystem view?
It is a cross-engine read of how the five AI engines — ChatGPT, Claude, Gemini, Perplexity, Grok — each position your entity. For every engine it shows how often you are mentioned, recommended and cited, and the tone of that treatment, then rolls it up into a coherence read: are the engines telling one consistent story about you, or five different ones?
What is grounded measurement versus assessment?
Entidex keeps the two strictly separate, and labels which is which. The per-engine mention, recommendation, citation and sentiment rates are grounded metrics — computed from observed signals, each carrying provenance, a sample size and a confidence level. The engine-fit summary, strengths, gaps and strategic narrative are Entidex's own automated assessment, flagged as such. You always know whether you are reading a measured fact or a strategic read.
Why does one engine treat me differently from the others?
Each engine is trained on a different mix of sources and retrieves differently at answer time, so your entity lands in a different position for each. ChatGPT, Claude, Gemini, Perplexity and Grok can diverge on whether they recommend you, how they cite you, and how positive they are. When one engine is an outlier, it is usually working from a weaker or staler set of associations — a fixable gap, not a fixed opinion.
How is this different from AI Visibility?
AI Visibility is one headline number for how prominently you surface. The AI Ecosystem view breaks the picture apart by engine and by behaviour — mention versus recommendation versus citation — so you can see exactly where a specific engine is strong, weak, or inconsistent, rather than an average that hides the per-engine story.
What do I do with an incoherent ecosystem?
Low cross-engine coherence is a signal that your narrative is fragmenting. Entidex shows which engine is the outlier and on what, so you can target the weak associations behind it — publish the verified record, strengthen the authoritative sources that engine leans on, and track whether coherence rises as the correction lands.