Cross-Engine Consistency Score: The 10-Point Gap That Changes Everything

Cross-engine consistency measures the variance in how different AI engines evaluate, cite, and represent your domain. Rather than evaluating performance on a single engine, this criterion compares your scores, citation rates, and visibility across ChatGPT, Claude, Perplexity, and Google AI Overviews to identify gaps indicating engine-specific optimization opportunities.

The evaluation aggregates data from multiple Intelligence Report criteria -audit scores, citation rates from the live citation test, hallucination patterns, content extraction accuracy -and measures how much these metrics vary across engines. A perfectly consistent domain would have identical scores everywhere. In practice, no domain achieves this because each engine has different evaluation priorities, different training data, and different retrieval mechanisms.

The consistency score identifies three types of cross-engine variance. Structural gaps -your technical optimization works well for one engine's crawler but not another's. Your schema might get parsed correctly by ChatGPT but ignored by Claude. Content preference gaps -one engine values your content characteristics (depth, citation patterns, authority signals) more than another. Training data gaps -one engine has more or better training data about your domain, typically because of differences in crawl schedules and data sources.

Here's what makes this criterion uniquely actionable: the gap threshold. When variance between your best-performing and worst-performing engine exceeds 10 points, it signals that engine-specific optimization can dramatically improve your overall visibility. A 10+ point gap means you've already proven you can achieve strong scores -you just need to apply the right optimization for each engine's specific preferences.

You can't predict cross-engine consistency from single-engine testing. Each AI engine has its own evaluation model, crawl infrastructure, and content preferences. ChatGPT relies heavily on Bing's search index for retrieval. Claude places more weight on machine-readable governance signals like llms.txt and structured data. Perplexity indexes the web independently and weights recent content more heavily. Google AI Overviews use Google's existing search index and knowledge graph.

AI-level testing across engines reveals these differences in practice. We've found the data from the customer support vertical demonstrates the magnitude of cross-engine variance. The scoreboard tells the story:

Tidio (63) earned a +14 point Claude bonus for strong machine-readable signals, while ChatGPT evaluated it closer to baseline. LiveChat (59) had a +12 Claude bonus driven by clean HTML and structured data. Crisp Chat (34) -the lowest overall scorer -got the largest Claude bonus in the group at +17, meaning Claude heavily rewarded specific signals ChatGPT largely ignored.

Flip side: HelpSquad scored 47 from ChatGPT but only 42 from Claude. A -5 gap in the opposite direction. This penalty indicated HelpSquad's content had characteristics ChatGPT valued (possibly stronger Bing presence or more web mentions) but lacked the governance signals Claude specifically rewards.

These cross-engine gaps aren't random noise. They reflect systematic differences in how each engine evaluates content. The Intelligence Report identifies the specific signals driving each gap, enabling targeted optimization that can close a 15-point engine-specific deficit with focused improvements rather than broad content overhauls.

The cross-engine consistency analysis synthesizes data from across the Intelligence Report's other criteria to build a comprehensive cross-engine profile. It collects your scores and metrics from each engine -audit scores, citation rates from the live citation test, hallucination counts, and content extraction quality ratings.

The system calculates three variance metrics. Score variance -the statistical spread of audit scores across engines. Standard deviation above 5 points indicates significant inconsistency. Citation variance -the spread of citation rates across engines for the same query set. Hallucination variance -whether different engines hallucinate different facts, indicating inconsistent training data.

Next comes driver identification for each engine pair with a significant gap. Using AI evaluation, the system compares signals each engine rewards and penalizes. For a ChatGPT-to-Claude gap, it might identify Claude rewarding your llms.txt file and strict robots.txt AI policy while ChatGPT is indifferent to these signals but rewards your Bing presence. For a Perplexity-to-ChatGPT gap, it might find Perplexity citing your recent blog posts heavily while ChatGPT relies on training data missing your latest content.

Driver identification produces a signal importance matrix -a table showing which signals each engine cares about most and how your domain performs on each. This matrix is the foundation for engine-specific recommendations. If Claude rewards governance signals and your governance score is 40/100, the path is clear. If ChatGPT rewards Bing presence and your Bing webmaster profile isn't set up -equally clear.

The output includes overall consistency score (0-100, where 100 means identical performance across all engines), per-engine-pair gap analysis, signal importance matrix, and prioritized optimization recommendations ordered by expected impact.

For domains with gaps exceeding 10 points, the report also generates preliminary recommendations for engine-specific audit products. A 15-point ChatGPT deficit triggers a "ChatGPT Unfair Advantage" audit recommendation. A 15-point Claude deficit triggers a "Claude Unfair Advantage" audit recommendation. These engine-specific deep-dives investigate the gap at a granular level with engine-specific criteria.

Above 80: your domain performs similarly across all tested engines. This is ideal -your AEO strategy is engine-agnostic, and improvements benefit you across the board. Domains at this level typically have strong fundamentals (comprehensive content, complete schema, active entity presence) that all engines value equally.

Between 50 and 80: moderate cross-engine variance. You likely perform well on one or two engines but underperform on others. The per-engine-pair gap analysis shows exactly where. Start here: address the largest single gap -the one engine where you significantly underperform. Closing one 15-point gap improves aggregate visibility more than incremental improvements across all engines.

Below 50: significant engine-specific performance differences. This usually means your optimization has inadvertently targeted one engine's preferences while ignoring others. The signal importance matrix becomes critical -it tells you which specific signals to add or improve for each underperforming engine. Common fixes: adding llms.txt for Claude, improving Bing webmaster presence for ChatGPT, ensuring recent crawlable content for Perplexity, strengthening Knowledge Graph connections for Google AI Overviews.

The 10-point gap threshold is the key decision point. Gaps below 10 points usually aren't worth engine-specific optimization -they fall within normal variance and fluctuate over time. Gaps above 10 points are systematic. They indicate a specific missing signal one engine rewards and another doesn't. These gaps are stable and persist until you address the underlying cause.

When reviewing engine-specific recommendations, consider business value per engine. If 80% of your audience uses ChatGPT and your ChatGPT citation rate is 15 points below Claude, the ChatGPT gap has higher business impact. If your audience skews toward technical users preferring Claude or Perplexity, those engine gaps may matter more. The Intelligence Report provides the data -prioritization depends on your business context.

Monitor cross-engine consistency over time. As AI engines update models and recrawl your content, your consistency score will change. Running the analysis quarterly reveals whether your multi-engine optimization is converging (gaps shrinking) or diverging (gaps growing). The trend matters more than any single measurement.