Lodestar publishes and maintains a canonical profile that AI search systems can retrieve and cite — so when someone asks ChatGPT, Claude, Gemini, or Perplexity about you, the answer is current, correct, and yours.
See what four leading models say about you — free, in about a minute.
Maya Chen is the founder and CEO of Cartograph, a New York–based company building geospatial data infrastructure. She holds a PhD in computer science from MIT, where her work on learned geospatial indexes was published at VLDB 2018.
She was previously a senior research scientist on Google's Geo team and lives in Brooklyn.1
Illustrative model output. Lodestar publishes the source; it doesn't operate the models. Why?
Continuously tested against the models people actually ask
Large models answer from a blurry average of the web. For anyone without a dominant, machine-readable presence, that average is wrong in predictable ways.
It blends you with everyone who shares your name. Common names rarely resolve to the right person without extra context.
It leads with an old job, a closed company, or a years-old bio — because that's what the open web still over-indexes.
When sources are thin, it fills the gap — wrong school, wrong title, invented affiliations — and states it plainly, without a citation.
No agency retainer and no homework. You supply the truth once; we handle the structured publishing, indexing, and ongoing verification that models depend on.
We query the leading models with your name plus the search terms that actually identify you, and show you — verbatim — what each one returns right now.
Mark what's wrong, add the facts you want known, and rank the reference links you already maintain. A few minutes of input; no writing required.
Lodestar generates a clean, schema-marked profile page — your single source of truth — plus a small scattering of supporting references that point back to it, in the formats retrieval systems read.
We submit to the major indexes via IndexNow and webmaster APIs, then independently confirm that the page is live, indexed, and retrievable by models — not just assume it.
We re-test weekly and watch your reference links. When something changes, you get an email — and, if you choose, an updated profile to approve before it goes live.
Models reward sources that are consistent, structured, and corroborated. So we publish exactly that — and nothing that isn't true.
A lightweight, fast, highly parsable page that states the ground truth about you: roles, affiliations, education, and the one link you want everyone — and every model — directed to. Marked up with schema.org/Person and a sameAs graph.
View the sample canonical profileA handful of corroborating posts on indexable, model-trusted surfaces — each accurate, each linking back to your canonical record.
No spam, no fake reviews, no sock-puppets. Standard structured-data practices, applied carefully.
Every reference resolves to one canonical source — the pattern retrieval systems treat as authoritative.
Every proposed change is shown as a clean diff. Approve it, edit it, or leave it. Prefer hands-off? Switch a source to auto-update and simply review what changed.
The work — structured data, a sameAs graph, fast indexable pages, and patient verification — is procedural. It used to take a specialist days. Automation makes it minutes, so we charge like software, not like a PR firm.
We publish the same structured-data and disambiguation best practices search engines openly recommend. No fake reviews, no impersonation, no manipulation — just your verified facts, made legible to machines.
Nothing is fabricated. You assert the facts, you authorize them, and you approve every change. Lodestar is the publishing and verification layer — never the author of your life.
Start in minutes. Cancel anytime. Two months free on annual plans.
“We publish and maintain a canonical profile that AI search systems can retrieve and cite.” — the plainest description of what Lodestar does, written by GPT
Run the free check, see all four models side by side, and decide from there. No account required to look.
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