We built a model of how foundation models choose which brands to recommend. ktau turns that understanding into the precise plan that moves them — query by query, attribute by attribute, brand by brand.
The GEO category has converged on the same instrument: a dashboard. Citations, share of voice, sentiment. A picture of where you stand. Useful — but inert.
To move the LLM, you need a different kind of instrument. One that doesn't just describe the answer, but predicts what would change it. That is the work ktau does, and it is what separates strategy from monitoring.
From the outside, an LLM's recommendation looks like a single fluent sentence. From the inside, it's the outcome of thousands of weighted signals: training data, retrieval candidates, source authority, attribute matches, conversational context. The output is fluent; the decision is probabilistic.
We treat that decision as something to be modeled, not guessed at. For every category we work in, ktau builds a brand-aware model of the recommendation surface — what the LLM is drawing on, where the leverage points sit, and what would tip the answer in your direction.
We don't audit once. We probe the recommendation surface every day, every model, every locale. The point is to keep the model live — because the LLMs underneath it are.
A brand with zero AI presence has a different problem than a category leader, and the answer is not "publish more." Emerging brands need category-level content first. Established brands need attribute-level depth. Dominant brands need defense.
ktau detects which problem your brand actually has, then prescribes accordingly — what to publish, where to publish it, and what each piece is forecasted to move. This is the line between us and content factories. We don't ask you to publish more. We tell you which one piece will most efficiently move your weakest attribute — and we measure the effect.
"We don't ask you to publish more. We tell you which one piece will move the LLM most — and we measure it."
Every piece of content produced through ktau is engineered for two effects. Most of the category optimizes for the first. We optimize for both — and the second is where durable AI visibility actually lives.
When the LLM reaches out to the web mid-answer, your content is the source it picks. Citation share rises. You appear in the response. Lift is measurable in weeks.
When the next foundation model trains, your content is part of what it absorbs. The model begins recommending your brand without any search at all. Visibility compounds across model releases.
Decode → Direct → Deliver, but not in a straight line. Every published piece updates the model. Every model update refines the next prescription. Over weeks the loop tightens, and the LLM's answer in your category stops describing the market and starts describing your brand.
Different brands have different problems. The method makes the difference between them obvious — and actionable.
Send us your brand and category. Within 72 hours we deliver an attribute-level visibility map, the leverage points we'd attack first, and a forecast of what they would move.