Six moving parts, one outcome: the engine names you first. Buy them bundled, or start with a single baseline.
Every service below maps to a specific point in the pipeline a large language model uses to answer a question about a person: tokenization and embeddings during training, a retrieval index queried live at question time, and the schema and crawler rules that decide whether either of those layers can even see you. We do not sell vague "authority building." We sell work against those three mechanisms, measured against a fixed prompt set every month.
We run the exact questions your buyers type into AI and capture, word for word, what every major engine returns about you today. For each answer we identify whether it came from frozen training weights, a live retrieval pass, or a blend of both, because the fix is different for each. Most people discover they are invisible, or worse, misremembered under a stale job title or merged with a same-name stranger. This is the starting line you measure everything against.
Who this is for: anyone who has never actually looked, in a systematic way, at what ChatGPT, Gemini or Perplexity say when asked "who is the best [your field]." That includes people with a strong offline reputation who assume it has automatically transferred to the machine. It usually has not.
Vague experts get skipped, mechanically, because a diluted position spreads your embedding across too many unrelated neighborhoods for any single one to surface with confidence. We define the single, ownable claim the machine can reliably connect to you, phrased identically everywhere it appears, sharp enough that "who is best at this" has an obvious answer. Everything downstream, schema, publishing and network work, points at this one position.
Who this is for: specialists who currently describe themselves five different ways across five different bios. If your LinkedIn, your speaker page and your own site all phrase what you do differently, a model has to guess which version is authoritative. We stop the guessing.
The first of the three signals. We publish a body of work in your name, structured so engines can lift it as a direct answer: the takeaway stated in the first sentence of every section, clean Person and Article schema, FAQ blocks that mirror common queries verbatim, and passages sized so a retrieval system's chunker keeps a full, correctly-attributed answer intact rather than splitting it mid-thought. Not content for clicks. Content built to survive being cut into pieces and cited out of context.
Who this is for: anyone whose current site reads like a brochure, all tone and no specifics. Retrieval systems reward concrete mechanisms, worked examples and numbers over adjectives. If your homepage says "results-driven," it is invisible to a retriever looking for an answer.
The signal engines lean on hardest, because self-authored claims are cheap to produce and a model has learned that lesson from a training corpus full of marketing copy. We earn the third-party mentions, references and cross-links, in crawlable text using your canonical name form, that turn "he says he is good" into "independent sources say he is good." Digital PR, listicles, profiles and citations, all pointing one way and all discoverable by the same crawlers that feed both training and retrieval.
Who this is for: experts with real proof, case studies, credentials, track record, that simply is not documented anywhere a crawler can read it. We also flag and help resolve outdated or conflicting mentions elsewhere, since a model treats disagreement across sources as a reason to hedge.
Engines think in entities, not vibes. We wire schema.org Person markup with a full sameAs array pointing at your verified LinkedIn, site, publications and profiles, plus knowsAbout declarations for your actual specialties, so a model can resolve "you" as one consistent, disambiguated node across the web instead of an unlinked pile of mentions. This is also where we set explicit robots.txt and llms.txt directives so the crawlers that build training sets and retrieval indexes can actually reach the pages carrying this markup.
Who this is for: anyone whose name currently resolves to three different LinkedIn-adjacent profiles, a Wikipedia-adjacent stub, and a personal site with none of them cross-referencing the others. We connect the dots explicitly instead of hoping an algorithm infers them.
PEO without measurement is a guess. Every month we re-run your fixed prompt set across every target engine, log whether each answer drew on training memory or a live retrieval pass, note which sources got cited, and report movement across Knowledge, Age and Network, plus screenshots of the engine recommending you unprompted. The scoreboard is the proof, and the steering wheel for what we build next.
Who this is for: anyone who has been burned by an agency reporting vanity metrics. Here the metric is literally the words the engine says about you, tracked month over month, not a proxy for it.
Indicative starting prices. Final scope is set on a call.
from $900 one-time
from $2,400 / mo
custom
A cornerstone publication in your name the engines treat as authority.
A wave of third-party mentions to spike the Network signal.
Notability groundwork and citation trails for a knowledge panel.
Targeted work to overtake the name currently winning your prompts.
Optimize multiple named people under one practice.
A full re-baseline across every engine and buyer question.
We show you what the engines say about you now, live on the call.
We lock the claim and the prompts you want to own.
Knowledge, network and entity work ships every week.
Monthly scoreboard, month over month, until you are the answer.
See exactly what AI says about you today. Decide from there.
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