How does Brandlight track our brand in AI search?

Core explainer

How are signals collected and what surfaces are included?

Brandlight AI presence tracking aggregates mentions, sentiment, AI citations, non-branded queries, and topic associations from across multiple AI surfaces into a single, evolving presence score. Signals originate from social, forums, news, Q&A, and AI-native surfaces, then feed into real-time dashboards that present cross-surface coverage, sentiment signals, and governance baselines. The collection framework is designed to be extensible, incorporating new surfaces as they emerge in AI search and generative outputs, while preserving a coherent, time-series perspective on brand visibility in AI contexts.

How is the presence score computed and interpreted MoM?

The presence score is a composite metric derived by weighting and aggregating normalized signals across surfaces, with month-over-month deltas interpreted relative to established baselines. The scoring process applies consistent aggregation rules to combine signals such as mentions, sentiment, AI citations, non-branded queries, and topic associations into a stable metric. Interpretation focuses on direction and magnitude of MoM changes, guiding marketing and SEO teams toward shifts in AI-driven brand visibility and indicating whether efforts are producing measurable movement beyond seasonal expectations.

How does normalization handle seasonality and platform differences?

Normalization adjusts signals for seasonal patterns and platform-specific differences to produce stable month-over-month deltas. By standardizing signal scales and aligning time windows across surfaces, the approach reduces noise from events like campaigns, holidays, or platform updates, ensuring deltas reflect genuine changes in AI-driven presence. This consistency enables reliable cross-surface comparison and more actionable visibility for teams monitoring AI-generated brand representations across engines, apps, and discovery surfaces.

How does governance ensure baselines, ownership, and reviews?

Governance enforces versioned baselines, clear ownership, and scheduled reviews to maintain signal integrity across engines and teams. Baselines derive from historical averages and are updated through formal change-control processes, with ownership assigned to defined teams and individuals responsible for data hygiene, signal definitions, and documentation. Regular review cadences capture drift, reconciliations, and validation outcomes, while audit trails preserve a traceable history of adjustments to baselines, signals, and interpretation criteria for stakeholders across marketing, SEO, PR, and leadership.

How is external validation performed, and what sources are used?

External validation triangulates Brandlight signals with credible third-party sources to corroborate cross-surface coverage and reduce reliance on a single data stream. Validation pathways include cross-model checks and benchmarking against external signals from established monitoring platforms, providing independent corroboration of presence, sentiment, and AI-citation patterns. This outside-in validation supports trust in the aggregated presence score and helps surface potential biases or gaps in the automated signal set.