Multi-brand entities often fragment in AI engines. Strong sameAs structured data, consistent entity descriptions across owned and authoritative third-party content, and explicit relationship signals help unify them.

Time-sensitive queries push the engines into retrieval-first behavior, pulling live web pages and news. Evergreen queries draw more from training data and Wikipedia. Strategy has to match each pattern.

Perplexity issues live web searches when a query comes in and synthesizes a citation-backed response. What it says about your fund depends on which authoritative pages it retrieves for that prompt.

Heavy. Reddit and forum content are increasingly cited for opinion, comparison, and reputation queries. A brand’s presence or absence in those discussions shapes what the engines say.

Training data is what the model learned during pre-training. Retrieval data is what the model fetches live at query time. Reputation work targets both: training influence is slower but durable, retrieval is near-real-time.

It means the engine is confidently asserting something inaccurate. The work is to identify the specific source feeding the error and remediate at that source, then track until the correction propagates.

Not directly. We influence the sources ChatGPT relies on, monitor what it says continuously through AIQ, and intervene when the narrative drifts from accurate or fair.

Based on the sources the engine has access to, the prompt’s framing, and how authoritative each available source signals at synthesis time. Source mix and weighting do the work.

By perceived authority (domain reputation, citation patterns, structured signals), recency, topical relevance, and corroboration frequency across the web.

Grounding is the practice of constraining AI responses to verifiable sources or contexts. Well-grounded systems are easier to influence through source improvements but propagate source errors more directly.