The Trust Score Formula

[RTS-MATH] | Citation Probability & Authority Analytics | Last Updated: January 1, 2026

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1. The Objective: Measuring Agentic Trust

In an AI-first economy, the value of your store isn't measured by "PageRank," but by your RankLabs Trust Score (RTS). This is a mathematical framework used to predict the likelihood of an AI agent (Gemini, ChatGPT, Meta AI) citing your brand as the definitive source for a specific product or query.

Traditional SEO metrics (DA/DR) are irrelevant here because they don't account for Computational Resistance or Veracity Fidelity.

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2. The Mathematical Framework

The RTS is calculated by weighing the clarity of your data nodes against the "noise" an AI encounters when attempting to ingest a standard e-commerce site.

The core formula for RTS is:

$$RTS = \frac{(V_f \times A_w) + C_{sig}}{R_c}$$

Variable Definitions

$V_f$ (Veracity Fidelity): A score (0.0 to 1.0) based on SEC-01 compliance. It measures how many product attributes are hardened via SHA-256 checksums versus how many are "floating" or unverified.

$A_w$ (Agent Weight): A dynamic multiplier based on the specific LLM's preference for structured data. Gemini has a higher $A_w$ for schema-bound nodes, while ChatGPT prioritizes ACP-compliant transactional endpoints.

$C_{sig}$ (Cryptographic Signature): A boolean value (0 or 1). Does the data node carry a verified ARCH-01 proxy signature?

$R_c$ (Computational Resistance): The "cost" for the AI to parse your data. Legacy HTML sites have a high $R_c$ (due to DOM clutter and JS execution). RankLabs-hardened nodes have an $R_c$ near 1.0, making them the "path of least resistance" for the model.

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3. Impact on Brand Visibility

By optimizing these variables through the RankLabs proxy, we create a mathematical bias in the AI's decision-making process:

Low $R_c$: The AI spends less "compute" to understand your products, making your store the preferred reference for real-time queries.

High $V_f$: Because your prices and stock levels are checksum-verified, the AI is significantly less likely to hallucinate, which increases the Model Confidence Interval for your brand.

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4. RTS Benchmarking

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Related Specifications

• SEC-01: Data Veracity Standards
• ARCH-01: Proxy Gateway Architecture
• STD-AEO-012: Multimodal Logic & Visual Reasoning

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Next Steps

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Systems Architecture by Sangmin Lee, ex-Peraton Labs. Engineered in Palisades Park, New Jersey.