The Retainer exists because of a specific problem: AI search visibility from a single, time-boxed sprint doesn't hold on its own. In my own data, visibility from a single sprint tends to peak early and partially fade without sustained volume behind it. Models build trust the same way people do - through consistent claims, repeated across enough content that a pattern becomes an established fact rather than a passing mention. One sprint's worth of articles can start that pattern. It takes ongoing publishing to keep it from decaying.
Who this is for
Firms that have already run a sprint - with me or otherwise - and have real citations to show for it, and firms that want to skip straight to sustained, compounding visibility rather than a single time-boxed push. Either way, you're past the "does this even work for us" question and into "how do we keep this growing."
What's included
01
Two articles a week, scheduled and ongoing
Consistent, scheduled publishing so gains compound between articles instead of rising and falling with each one. Consistent volume matters more than any single article - visibility tends to fade without sustained output behind it.
02
Priority phrases first, not spread thin
The same concentrated approach that proved the Sprint: easier on-site wins prioritised before harder comparison, best-of and vertical-specific queries - the fuller range of what buyers actually ask an AI model when they're close to a decision.
03
Internal linking architecture
Articles get connected to each other deliberately, not left as isolated pages, so the site builds topical authority a model can trace across multiple pieces rather than a single one.
04
An ongoing freshness rhythm
Once the priority list is covered, this shifts from new content to a mix of new pieces and updates to older ones. A citation's median half-life runs to about 4.5 weeks for content that exists in one place, roughly doubling when the same claim is also referenced elsewhere - AI models weight recency more heavily than traditional search does.
05
Entity signals: Wikipedia and Wikidata
Wikipedia submission included at no extra charge, where eligible - no outcome guaranteed, since inclusion depends on meeting Wikipedia's own notability standards. One of the clearest signals Gemini's AI Overviews draw on from outside a page's ranking position.
07
Monthly data review & prompt strategy session
A structured citation-tracking pull every month - what moved, what didn't, and exactly where the next month's content should focus - plus a session to reset the prompt targets as your positioning evolves.
Why the retainer exists: the decay problem
This is the same dynamic that shows up in the case study data: citation volume and visibility both moved fastest while publishing stayed active and consistent. Six articles, published on a defined cadence, generated 2,357 citations in a single week and moved visibility from 0% to 30% on target prompts. Independent tracking backs up why: a citation's median half-life is only a matter of weeks without reinforcement. That volume and consistency is the mechanism, not a side effect - stop publishing, and the same models that built confidence in your positioning stop seeing it reinforced. Read the full case study →
Sprint vs. Retainer
The Sprint is the proof phase: a time-boxed, on-site engagement that builds your authority framework and gets your first citations on the board - in my most recent sprint, eight weeks kick-off to measurable visibility. The Retainer is what comes after - the same discipline, sustained indefinitely, with prompt coverage widening over time instead of resetting. See what's in the Sprint →
Built on the Authority Framework
Both the Sprint and the Retainer are applications of the same underlying methodology. Read the Authority Framework →
Further reading
Why your agency isn't showing up in AI search (and how to fix it) covers the "no repeated signal" problem the Retainer is built to solve, in more depth. For how the tactics above shift once you're tracking more than one platform, see how to get cited by ChatGPT, Claude, and Gemini.