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Daniel Cheung

SEO Manager + AI Search Researcher | 8+ Years

About

Who is Daniel Cheung?
  • AI search researcher with a special interest in semantics
  • Founder of Ternith and ContentGrapher
  • Senior SEO Manager, Asia-Pacific and Japan, at Adobe

Daniel Cheung is a Chinese-Australian AI search researcher and practitioner. Born in Hong Kong and living in Australia since 1990, he studied Speech Pathology at the University of Sydney before starting his first business in 2010 as a wedding photographer. That work built his skills in storytelling, communication, and digital marketing, and led him into search marketing in December 2018.

He joined Prosperity Media as an SEO and content junior and progressed to Team Leader, then moved in-house as SEO Manager at Optus, before joining Adobe in April 2023 as Senior SEO Manager for Asia-Pacific and Japan. His background spans entrepreneurship, agency leadership, and enterprise SEO, which has shaped a practical approach to search grounded in communication, systems thinking, and how work actually gets implemented inside organisations.

In 2026 he founded two tools. Ternith (ternith.ai) diagnoses which of three failure modes is keeping a brand out of AI assistant answers — discoverability, compellingness, or positioning. ContentGrapher (contentgrapher.io) maps the concepts in a piece of content, builds the ideal explanation framework for that topic and audience, and surfaces the gap as actionable writing guidance. His current focus is the semantic, structural, and organisational foundations that make discoverability possible in modern search — including what is required for scalable answer-engine optimisation.

Advice For Anyone Joining The Industry

Spend the beginning absorbing — be a literal sponge of information. The more exposure you get, the quicker you’ll grasp the lexicon of the industry.

But theory and advice is just theory and advice. Sooner or later, to become a practitioner, you must test and find your own truths. Be bold and test. Challenge best practices, gather your own data, find what works for you, and keep repeating the process, because learning never ends.

And build the soft skills. The ability to read a room and a person is how you’ll earn a great pay package. Knowing your stuff gets your foot in the door; the ability to lead, to communicate, to actively listen — these are the things that propel your career and earning potential.

Areas Of Interest

  • Semantic strategy and AI discovery
  • Entity-based search
  • Answer-engine optimisation (AEO)
  • Enterprise change management

Education, Awards, Certifications

  • Nothing that is relevant to what I do these days — I studied to be a speech pathologist and also almost completed a MBA (finance).
  • Everything I have achieved in my SEO career has been self-taught and built through real relationships with others.

Affiliations & Past Work

  • Senior SEO Manager, Asia-Pacific and Japan, at Adobe (Apr 2023 – present).
  • Founder of Ternith (Jul 2026 – present) and ContentGrapher (Apr 2026 – present).
  • Previously SEO Manager at Optus (2022–23); SEO and content junior → Team Leader at Prosperity Media (Dec 2018 – Apr 2022).
  • I share my writing on search and semantics on my website.

Published Work & Inclusions

Any Results/Highlights From Your Work You Want To Promote? Any Impact You’re Proud Of?

Two pieces of recent work I’m proud of.

    1. Ternith — which brands do AI assistants actually recommend, and why not you? When a brand doesn’t appear in ChatGPT, Claude, or Gemini answers, the reason isn’t always the same. Ternith runs two measurement passes on separate days and tells the brand which of three things is going wrong: the AI never surfaces it for the category (discoverability), the AI reads its pages but recommends a competitor (compellingness), or it wins one kind of buyer and loses another (positioning). A failure mode is only asserted when both runs agree and fall outside the noise band — otherwise it ships as inconclusive, with the evidence and a free resolution run.
    2. ContentGrapher — does the content actually explain the topic? ContentGrapher maps the concepts in a page, builds the ideal explanation framework for that topic and audience, and flags the gaps. The validation research is what I’m proudest of. In the Findability Study, acting on ContentGrapher’s boundary recommendations produced 84% AI findability versus 4% without — a gap that held on 164 of 166 head-to-head questions. In the Decoy Study, filling the gaps ContentGrapher flagged beat filling random structural additions by 11 percentage points on pages with five or more gaps. The Agreement Study checked whether the gap detection is stable across models: boundary-classification consistency of 87–94% per concept across eight models.

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