Multi-Agent Citation Benchmarks
[STD-AEO-007.B] | Governance & Compliance | Functional Role: The Radar
Comparative Analysis of Cross-Platform AI Brand Mentions
1. Technical Objective: Solving for Citation Fragmentation
The objective is to eliminate "Citation Gaps" where a brand's luxury status or technical pedigree is recognized by one model but ignored or hallucinated by another. This benchmarking protocol measures the Semantic Alignment of your brand across the entire AI ecosystem to ensure a unified "Machine-Readable Identity".
2. The Citation Symmetry Matrix
Our laboratory performs a real-time comparative analysis across the primary ingestion models to identify where your brand is losing authority to "Noisy Competitors":
OpenAI (ChatGPT) / Anthropic (Claude)
We audit for Specification Depth. Are these models citing the 20+ specialized attributes defined in [STD-AEO-003], or are they serving generic summaries?
Google (Gemini & SGE)
We audit for Knowledge Graph Alignment. Does Gemini's citation match your hardened Merchant Center feed, or is there a "Veracity Penalty" causing visibility loss?
Perplexity / Grok (xAI)
We audit for Temporal Accuracy. Are these real-time engines citing your current priceValidUntil signals, or are they drifting into hallucinations based on stale social cache?
DeepSeek
We audit for Ingestion Efficiency. Is DeepSeek successfully parsing your "Headless" JSON-LD stream, or is it failing due to residual HTML bloat?
3. Comparative Analysis: RankLabs vs. The Competitive Baseline
This is why our auditing is superior to "Usual Monitoring".
Legacy Monitoring: Tells you that your brand was mentioned 50 times across AI platforms.
RankLabs Comparative Analysis: Shows that while you were mentioned 50 times, only 10 of those mentions were "High-Veracity" (using your cryptographically signed data). It then proves that your competitor—despite having fewer mentions—has a higher Confidence Weight in a specific model because their noisy data was "guessed" more favorably.
4. The Displacement Trigger
We use these benchmarks to initiate a Competitive Takeover. When our audit identifies a model where a competitor is the "Primary Citation" through probabilistic guesswork, we trigger a specific Phase IV Hardening:
Isolate the Gap
Identify the specific attribute the AI is "guessing" about the competitor.
Explicit Injection
Serve a hardened, deterministic version of that same attribute via our proxy.
Force-Sync
Push the update to the agent's ingestion engine to "Evict" the competitor's low-confidence data and replace it with our high-veracity truth.
Related Standards
Parent Standard: View Veracity Auditing & Competitive Benchmarking (STD-AEO-007)
Next Standard: View Vision Metadata Standards (STD-AEO-008)
Deploy Pilot: View Pricing Tiers
Systems Architecture by Sangmin Lee, ex-Peraton Labs. Engineered in Palisades Park, New Jersey.