Related frameworks

Visibility Drawdown

Category	AI visibility risk measure, generative engine optimisation
Adapted from	Drawdown (technical analysis)
Originator	Paul Truscott
Year introduced	2026
Measures	Decline in citation or visibility level from a prior peak to a subsequent trough
Key outputs	Drawdown severity (%), drawdown duration (periods)
Input data	Rolling citation or mention frequency across AI-generated answers
Citation RSI Entity Support and Resistance Visibility Bollinger Bands

Visibility Drawdown is a framework used in generative engine optimisation (GEO) that measures the decline in an entity's citation or visibility level from a prior peak to a subsequent trough. Paul Truscott developed the framework in 2026 by adapting drawdown, a risk-management measure used in trading to quantify peak-to-valley losses in an investment's value.

Visibility Drawdown quantifies the severity and duration of a reputational or visibility decline, such as one following a rebrand, a hallucinated fact, or negative coverage, before recovery. The framework treats a decline as a measurement rather than an emotional event: severity and duration together determine whether the correct response is patience, intervention, or a full strategy reassessment.

Overview

Visibility Drawdown answers a question a raw visibility drop cannot: how far did this decline travel from the entity's own prior peak, and how long has it persisted? A brand that loses AI citation share after negative press coverage is usually reacting to the fact that visibility fell, not to how far it fell relative to its own history, or how long comparable declines have taken to recover in the past. Visibility Drawdown supplies both numbers.

Origin and development

Paul Truscott built Visibility Drawdown from a distinction every trader learns to hold onto under pressure: a loss and a drawdown are not the same measurement, and confusing the two produces bad decisions. Traders manage drawdown as a risk discipline rather than an emotional reaction. Truscott's claim is that brand visibility crises deserve the same discipline: without quantifying severity and duration, brands either panic and overcorrect, spending on remediation a smaller decline never needed, or underreact to a decline that is, by historical comparison, unusually severe and unlikely to self-correct without intervention.

Visibility Drawdown sits alongside Citation RSI, Entity Support and Resistance, and Visibility Bollinger Bands as one of four original frameworks Truscott built at the intersection of semantic SEO, GEO, and financial technical analysis.

Calculation

Visibility Drawdown at a given measurement period is the percentage decline of an entity's current citation frequency from the highest citation frequency it has reached up to that point.

Drawdown = 100 × (Peak Citation Frequency − Current Citation Frequency) ÷ Peak Citation Frequency

The following pseudocode computes the drawdown and maximum drawdown of an entity's tracked citation series, "CF", across measurement periods 1 through N:

MaxDrawdown = 0
peak = -99999
for i = 1 to N step 1 do
    if (CF[i] > peak) then
        peak = CF[i]
    end if
    Drawdown[i] = 100.0 × (peak - CF[i]) / peak
    if (Drawdown[i] > MaxDrawdown) then
        MaxDrawdown = Drawdown[i]
    end if
end for

The Average Drawdown up to a given period is the time average of every drawdown value recorded up to that period. The Maximum Drawdown is the single largest peak-to-trough decline an entity's citation frequency has recorded across its measured history, and functions as the framework's headline severity figure.

Drawdown duration

Drawdown duration is the length of a peak-to-peak period: the number of measurement periods between one citation-frequency high and the next new high. The maximum drawdown duration is the longest such peak-to-peak period an entity has recorded, which does not always correspond to the period containing the maximum drawdown in magnitude, since an entity can experience its steepest decline within a comparatively fast recovery, or a shallow decline that takes an unusually long time to reclaim its prior peak.

Loss versus drawdown

A loss describes a single period's decline. A drawdown describes the cumulative distance travelled from a peak, measured continuously until a new peak is reached. An entity can record several consecutive small losses that combine into a severe drawdown, or a single sharp loss that recovers within the next measurement period and never develops into a meaningful drawdown. Reading the drawdown figure, rather than the period-over-period loss figure, is what separates a measured response from a reaction to the most recent data point alone.

Application in generative engine optimisation

When an entity's citation frequency falls outside the lower Visibility Bollinger Band, Truscott calculates the drawdown from the most recent peak: how far visibility has fallen in percentage terms, and how many measurement periods it has remained below that peak. He compares this against the entity's own historical drawdown pattern, since some entities naturally experience deeper or more frequent dips than others, the same way some financial instruments carry structurally higher volatility than others without it signalling anything abnormal.

A drawdown that is deep and prolonged relative to the entity's own history triggers a full entity reconciliation and corroboration audit, since something structural has likely changed in how AI systems are interpreting the entity's data. A shallow, short drawdown gets monitored rather than escalated, avoiding remediation spend on a decline that historical pattern suggests will self-correct.

Limitations

A drawdown figure describes what has already happened and carries no guarantee about the path or speed of recovery. Two entities with an identical maximum drawdown can recover at markedly different rates depending on the underlying cause: a rebrand that resolves once new corroboration accumulates behaves differently from a hallucinated fact that persists until directly corrected at the source.

Visibility Drawdown depends on a citation history long enough to establish a meaningful peak. A newly tracked entity has few prior highs to measure against, which limits how confidently a given decline can be judged severe or routine until more measurement periods accumulate.

Related frameworks

Framework	Function
Citation RSI	Measures whether an entity's citation frequency has outrun its corroboration base
Entity Support and Resistance	Identifies the visibility levels an entity holds against or breaks through
Visibility Bollinger Bands	Flags when a citation movement falls outside an entity's normal range

See also

• Drawdown (economics)
• Visibility Bollinger Bands
• Citation RSI
• Hallucination
• Entity Reconciliation
• GEO (Generative Engine Optimisation)

References