ktau decodes how large language models choose which brands to recommend — then engineers a brand-specific plan to put yours at the top. Visibility goals in. Measurable AI rankings out.
We treat the LLM not as a black box but as a system that can be modeled. Every brand gets its own map: where it wins, where it loses, what would change the answer. We translate the map into action and the action into visibility.
We probe the recommendation surface at scale — every attribute, every query, every competitor. The result is a granular model of how the LLM thinks about your category and where your brand actually stands inside it.
From the map we generate a brand-specific plan: which topics, which sources (owned · earned · affiliate · social), which formats, and the order of operations that will move the LLM most efficiently — tuned to your stage.
We draft the content, publish through the right channels, monitor the LLM response in real time, and iterate. Every recommendation is closed-loop — tied to a measurable change in your AI rankings.
Other tools optimize for retrieval — the moments the LLM searches the web mid-answer. We optimize for that, plus the deeper game: what the model learns about your brand when it next trains. ktau content imprints. The LLM keeps recommending you, even when no search happens.
When the LLM reaches out to the web mid-answer, your content is the source it picks. Higher citation share. Better positioning inside the response. Faster lift, measured weekly.
The next time a foundation model trains, your content becomes part of what it knows. The LLM starts recommending your brand without any search at all. Visibility that compounds, model release after model release.
One workspace that takes you from visibility audit, to brand-specific strategy, to drafted and published content, to measured lift. Each surface below maps to a piece of the method.
Attribute-by-attribute view of where you win, where you lose, and which sources are shaping the LLM's answer in your category.
A brand-aware queue of content prescriptions — what to publish, where, and the projected effect on each visibility goal.
Drafts written to be ingested. Structured for retrieval and shaped for training.
Push to owned, earned, affiliate, and social — the right surface for each prescription.
Goal-bound reporting. Every recommendation is tied to a measurable change in your AI rankings.
Granularity adapts to your visibility stage. If you're invisible, we build category presence. If you already dominate, we sharpen attribute-level recommendations. Either way: you set the goals, the software delivers them.
Pitch clients with a real model of their AI position, not a dashboard. Run every account from one workspace. White-label reporting. Recurring revenue from strategy and content — not from selling another measurement tool.
We publish what we learn. Not because we have to — because the category needs better thinking than another best-of list. Read what we're finding in retrieval, training, and how recommendations actually form.
Tell us your brand and your category. We'll deliver an attribute-level AI visibility map and the first three moves that would change it.