Situation: A leading developer data platform wanted to maintain and grow visibility among its practitioner audience. They wanted to show up as a thought leader and dependable source of both accurate information as well as innovative technology. This meant leaning into the new way that things are found online: Generative Engine Optimization (GEO). The brand wanted solid, tech-forward content, optimized for LLM retrieval, but readable and informative enough for publication on reputable third-party channels. 

Challenge: There is no reliable GEO playbook, nor a consistent understanding of LLM behavior and retrieval preferences. And the subject matter was highly technical with no room for inaccuracy. Writing about topics like database architecture, vector search, multi-cloud infrastructure, and transactional data guarantees requires precision to hold up to a developer audience.

Action: I developed a proprietary framework for GEO content development, from topic discovery to final optimized draft, that maintains technical accuracy and optimizes for LLM retrieval, while significantly reducing time to publish. I used the framework to research and write long-form technical articles published across two prominent developer platforms. Each piece required building working fluency in a specific technical domain, with claims pressure-tested against primary source material throughout.

Result:  I researched and wrote three long-form technical articles published across two prominent developer platforms. Total of 16 non-branded AI citations and combined viewership of approximately 1,550 readers. 

Reduced end-to-end copy development time from 25-30 hours to 10-12 hours total (topic discovery, outline, draft, revision, final copy).