A growing share of buying research never reaches a results page. Someone asks an assistant, gets an answer with three cited sources, and everything else is invisible. Being one of those three is a different discipline from ranking - and unlike most of what gets called AI marketing, it is measurable.
From$2,000/ month
Citation baseline, answer-first content engineering, structured data and monthly share-of-citation reporting.
Indicative starting price. The fixed fee for your scope is quoted after the two-day diagnosis, before any build begins.
What makes it work
Assistants lift self-contained passages that answer one question completely. That is a structural property of the writing - a direct claim, its qualification, and the evidence in the same paragraph - not a keyword density target.
Models cite what several independent sources agree on. Which means the work is as much about where else your claims appear - documentation, directories, industry press, comparison sites - as about your own pages.
There is no rank tracker for a conversation. We monitor a fixed prompt set across assistants and report share of citation, which sources beat you, and what changed after each release.
Capabilities
A fixed prompt set run across the major assistants to establish where you currently appear, where competitors do, and which sources they draw on.
Rewriting and structuring existing pages so a model can extract a complete, attributable answer without inference.
Schema, clean semantics, crawlable rendering and llms.txt-style surfaces so parsers get the facts right.
Getting your claims corroborated where models actually look - documentation, reference sites, industry publications, communities.
Monthly reporting on share of citation by prompt and assistant, with the reasoning behind movements rather than a number in isolation.
In detail
A category name is not a scope. These are the individual pieces of work inside this practice - take the two that apply to you and ignore the rest.
A fixed prompt set run across the major assistants to establish where you currently appear, where competitors do, and which sources they draw on.
Defining the questions your buyers actually ask an assistant, which is the equivalent of keyword research for this channel and is rarely the same list.
Restructuring pages so a model can extract a complete, attributable answer from a single passage without having to infer anything.
Schema.org markup, clean semantics and crawlable rendering, so parsers get your facts, prices and entities right.
llms.txt, clean sitemaps and stable canonical URLs, giving assistants an unambiguous version of what you offer.
Making your name, category, location and claims identical everywhere they appear, because inconsistency is what stops a model asserting anything about you confidently.
Getting your claims corroborated on the properties models actually cite: documentation, reference sites, industry press and communities.
Owning the "X vs Y" and "best X for Y" queries that assistants lean on heavily when asked for a recommendation.
Monthly reporting on how often you are cited per prompt and per assistant, who beats you, and what changed after each release.
What you receive
Around three weeks end to end. That comes from scoping tightly to one workflow - not from skipping a phase. Each still ends in evidence you can check.
Free calculator
Further reading
Let's talk
We will run a baseline across the major assistants on the prompts your buyers actually use, and show you where you appear, where you do not, and who is being recommended instead. Fixed fee, delivered as a written report.
Related practices
