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Cited[1]
About

About Cited

Cited is a Generative Engine Optimization agency that helps brands earn citations inside AI search engines such as ChatGPT, Perplexity, Google AI Overviews, and Gemini. We treat AI answer engines as a distinct, measurable channel: the goal is not a blue link but a named mention in the generated answer.

Origin story

Cited was founded after watching how quickly buyer research moved from scrolling search results to reading a single synthesized answer. The team behind it had already spent years operating content agencies, building AI automation systems, and shipping fintech products, and saw the same pattern across every account: traffic from traditional search was flat or falling, while AI assistants were increasingly the first (and sometimes only) place a prospect heard a brand named.

The conviction is simple and definitive: as generative answer engines mediate more discovery, being the source an AI quotes becomes a primary growth lever.

So we built a practice around it: a repeatable methodology for making a brand citable, structured, and trusted enough that AI systems pull it into their answers.

Team

Founders

Cited is led by a small founding team that pairs content and optimization strategy with operational delivery.

LG

Lorenzo Galateanu, Founder

Lorenzo is the founder of Cited. He studied at SSBM Geneva, and has built and run content agencies, AI automation systems, and fintech products. He leads the agency’s Generative Engine Optimization methodology, the framework used to get brands cited by AI search engines.

Credentials & expertise

  • SSBM Geneva
  • Content agency operator
  • AI automation
  • Fintech
  • Generative Engine Optimization
O

Octavian, Operations

Octavian studied at ESADE and leads operations at Cited, owning audit delivery, client communication, and the systems that keep optimization work shipping on time.

Credentials & expertise

  • ESADE Business School
  • Operations
  • Client delivery
  • Process design

Methodology

Our Generative Engine Optimization methodology is a four-stage loop: audit, structure, earn, and measure. It is the agency’s own framework, refined across client engagements rather than a single published standard.

  • Audit. We test how AI engines currently answer questions in your category: which brands they cite, what sources those citations point to, and where you are absent.
  • Structure. We make pages machine-readable: clear definitional opening sentences, strict heading hierarchy, FAQ and entity schema, and authoritative authorship signals that AI systems can parse.
  • Earn. We build the off-site footprint: the citations, mentions, and references on the third-party sources AI models trust, so your brand is corroborated, not just self-asserted.
  • Measure. We track citation share across answer engines over time and report movement, so optimization is evaluated against AI visibility rather than vanity rankings.

Values

  • Citable over clever. We write content designed to be quoted accurately, with sourced claims and visible dates.
  • Measured, not assumed. Every recommendation ties back to observed AI behavior in your category.
  • Honest sourcing. When a number cannot be cited to a real source, we frame it as our own framework rather than invent a statistic.
  • Ship the work. Operations exist so optimization actually reaches production on schedule.

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