Third‑party platform seeding is our practice of publishing structured content on trusted hubs like Medium, Substack, and LinkedIn articles so AI platforms can easily access and reuse it. These destinations have clean markup, real‑author profiles, and consistent crawl frequency, which increases the odds that models cite and summarize our work.
Why these hubs help
They make extraction easy. Minimalist layouts and semantic headings give models clear signals about what each section does. Real identities and editorial tone increase perceived trust. Most importantly, these sites are crawled at a steady clip, so updates propagate quickly to retrieval‑based systems.
What we publish there
We repurpose the strongest sections of our cornerstone guides into hub‑native posts. A typical post opens with a TL;DR and includes a compact comparison table and a five‑question FAQ. For research, we share one or two headline charts with a short methods note and link back to the full report on our site. The goal is to give readers and AIs enough structure to reuse, not to copy the entire source article.
Formatting tips that work
Open with a summary paragraph that states the outcome clearly. Use short sections and question‑led subheadings so answers surface cleanly. Include author bios and clearly dated updates. Where comparisons matter, include a simple table that encodes tradeoffs and “best for” scenarios.
How we measure impact
In our platform we tag hub posts by topic and track changes in brand mentions and citations for related prompts. We run link citation audits to see which hub posts show up as sources and how prominent those citations are. We then compare share‑of‑voice across platforms before and after seeding to see where this tactic moves the needle.