Why this research exists
AI engines rewrite their rules constantly. If your strategy is chasing the current rules, you are permanently one update behind. Our research question is different: which properties of content keep getting it found and cited even after the engine changes? Those invariant conditions — not this quarter's tricks — are what we are trying to measure.
The method: parallel tracks
The research runs as several tracks at once, each calibrated against real citation outcomes across mainstream AI engines rather than hand-labeled guesses. The tracks have to agree before we draw a conclusion — a signal that only one track can see doesn't count. And a conclusion only stays on the list if it still holds after an engine update; the ones that don't make it get dropped.
Where it stands
The data pipeline and baseline models are running; we are now accumulating observations across engine update cycles. This page will stay honest: no numbers until we can stand behind them. When the findings hold up, we publish them.
What this means for clients
The GEO recommendations inside our marketing-growth practice are fed by this research pipeline — measured conditions, not folklore. Working with us means your visibility strategy updates when the evidence does. The research is still running, but the method has already done real work: a service business with fifteen years in its local market — six figures a month on marketing, beautiful traffic reports, and consistently zero customers from them — saw overall customer visits grow 109% in the first month after rollout, and over half of its traceable customers now arrive through AI search.
