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What is Perplexity?

The answer engine that answers questions with verifiable citations, instead of returning a list of links.

Perplexity is an answer engine: instead of returning ten blue links like a traditional search engine, it formulates a direct answer to the question, backed by verifiable citations to the sources it used. Technically it is a large-scale application of retrieval augmented generation: it searches the web in real time, selects sources, and generates the answer anchored to that material, with clickable links attached to each claim. Compared to a traditional search engine, where the user clicks and reads several pages to form an opinion, Perplexity shifts the synthesis work from the person to the machine: the result is faster to consume, but it also moves the responsibility for checking sources downstream of reading, not upstream of it. Founded as a startup by former OpenAI and Meta researchers, it carved out a space distinct from both classic search engines and general-purpose chatbots, positioning itself specifically around research and verification rather than open-ended conversation.

What it does, and why it matters for marketing

Perplexity has become a central case study for GEO (Generative Engine Optimization): if your content is not written to be citable, structured, with verifiable claims and clear sources, it simply does not show up in the answers Perplexity generates for queries relevant to your industry. It is one of the engines (alongside Google's AI Overviews and citations from ChatGPT and Claude) that made "being cited by an AI" a distinct visibility metric from "ranking first on Google".

How to evaluate it as a buyer

For a company, Perplexity has two distinct uses worth keeping separate. As an internal research tool, it is useful for anyone who needs to quickly verify facts with traceable sources, a real advantage over a generic chatbot with no citations. As a traffic and visibility channel, it should be monitored alongside the other answer engines: understanding whether and how your own content gets cited is now part of the SEO toolkit, not a frontier experiment. Neither use justifies treating it as a substitute for primary research when the stakes are high: citations should always be checked against the source.

  • GEO & AEO · The practices for getting cited in AI answers (ChatGPT, Perplexity, AI Overviews): SEO's evolution in the era of generative engines.
  • RAG · A technique that grounds an LLM in your company data: it retrieves relevant documents and feeds them to the model before it answers.
  • LLM · An AI model trained on huge amounts of text that understands and generates language: the engine behind ChatGPT, Claude and Gemini.
  • Grounding · Anchoring a model's answers to verifiable sources instead of its memory: RAG is the main technique used to achieve it.
  • Share of model · The share of mentions and citations a brand gets in generative-engine answers compared to competitors.

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