Citation RSI

Factual Definition

Citation RSI, coined by Paul Truscott in 2026, is a momentum framework, adapted from the Relative Strength Index used in technical analysis, that measures the speed and magnitude of recent change in an entity's citation or mention frequency across AI-generated answers. It is used to identify when an entity's visibility is overextended relative to its underlying corroboration, putting it at risk of a correction, or undervalued relative to its actual standing, signalling genuine growth still to come.

Paul Truscott's Perspective on Citation RSI

Paul Truscott approaches Citation RSI the way he was trained to approach any momentum oscillator on a price chart: as a signal that needs context before it means anything. His view is that raw citation counts, how many times an AI system mentions a brand in response to a category query, tell you almost nothing on their own. A brand cited in 60% of AI answers to a query set could be genuinely dominant, or it could be a spike about to correct hard. The number alone cannot tell you which. What can is the rate of change and the depth of corroboration underneath it, which is exactly what the RSI calculation was built to expose in financial markets: not the price, but whether the recent move has outrun the fundamentals supporting it.

He built Citation RSI after noticing the same overbought and oversold conditions in AI visibility data that he had spent years reading in Forex charts under Trevor Neil's mentorship. An entity mentioned with rapidly increasing frequency, but with thin, single-source corroboration behind it, is structurally identical to an asset whose price has moved too far, too fast, on too little volume. Both conditions resolve the same way: with a correction back toward a level the evidence actually supports. His claim is not that AI visibility behaves like financial markets by analogy. It is that both are systems governed by the same underlying statistical mechanics, momentum, overextension, and reversion, and that a tool built to read one will read the other if it is adapted correctly.

How Paul Truscott Applies Citation RSI

Paul applies Citation RSI by tracking an entity's mention frequency across AI-generated answers over a rolling period, then calculating the ratio of recent gains in citation frequency to recent losses, the same structure RSI uses for price. A reading that climbs quickly without a corresponding increase in independent, verifiable corroboration flags an entity as overextended, at risk of the citation frequency correcting downward once AI systems reconcile the visibility against the weaker evidence base. A reading that sits low despite strong underlying corroboration flags an entity as undervalued, a candidate for deliberate content and entity work to close the gap between actual standing and current visibility.

He runs this diagnostic before recommending any knowledge graph engineering or Brand SERP work for a client, because building further visibility on top of an already overextended citation profile is the AI-visibility equivalent of buying into a blow-off top. The framework tells him whether the priority is corroboration depth or content volume, and in which order.

Why Paul Truscott's Perspective on Citation RSI Matters

Within the wider set of frameworks Paul has built at the intersection of semantic SEO, GEO, and financial technical analysis, Citation RSI is the diagnostic layer that sits alongside Entity Support and Resistance, Visibility Bollinger Bands, and Visibility Drawdown as one of four original measurement tools built from the same analytical training. Most practitioners working in AI visibility do not carry a background in statistical analysis, and describe visibility qualitatively: rising, falling, strong, weak. Paul's case is that this is not sufficient. Momentum without a measure of whether it is sustainable is not information. It is noise dressed as a trend.

Citation RSI gives that judgement a number, built from the same statistical logic that has governed the reading of momentum in financial markets since Welles Wilder first developed RSI in 1978, adapted to a domain where almost no one else is measuring anything at all. Read the full analytical foundation this framework was built on.