Reputation management
Shape what AI Overviews say about you.
Someone asks ChatGPT about you, or reads Google's AI Overview, and takes the answer as fact. That answer is not written from nothing. It is synthesized from your site, your profiles, your coverage, your reviews, and the threads about you. helm shapes that source layer, because changing what the models read changes what they write. That is AI Overview optimization: the same source work whether the answer comes from Google's AI or an assistant.
When the first answer is written by AI
More and more, people do not read a page of search results. They read a single synthesized answer at the top of Google, or they ask an assistant directly. That answer is built by a model that reads what is publicly available about you and compresses it into a few sentences. If the sources are thin, outdated, or dominated by one bad thread, the answer inherits the problem.
This matters because an AI answer carries a tone of authority a list of links never did. It can confuse you with someone who shares your name, describe a venture you left long ago as current, or repeat an accusation as if it were settled fact. Most people will not click past it to check. Whatever the model says is, for practical purposes, the first impression.
Where AI answers go wrong.
The answer is about someone else
Google's AI or an assistant blends you with a different person or company that shares your name, and their history becomes part of your answer. We work to separate the two identities at the source level.
The answer is out of date
The model describes a company you no longer run, a role you have left, or a dispute that was resolved. The sources still tell the old story, so the answer does too. We bring the record current.
One bad thread dominates
A single complaint thread, review pile-on, or critical post carries disproportionate weight in the synthesis, and the answer leads with it. We build stronger truthful sources so the model has better material to draw from.
The answer says almost nothing
When the public record is thin, models hedge, guess, or pull from whatever exists, however marginal. A sparse answer is an open door for the wrong source. We give the model substantial, accurate material to work with.
Before more people ask
Press, an announcement, a season of scrutiny: when attention is coming, the question about you gets asked far more often, and the AI answer is what most of those people will read. We put the source layer in order before the volume arrives, not after.
How we shape what the models say
Map what the models currently say
We start with the questions your buyers, clients, or counterparties ask, run them across Google's AI Overviews and the major assistants, and record what comes back. Then we trace each claim in those answers to its likely source: a review page, a news article, a forum thread, an old bio. That map tells us exactly which sources are feeding the problem.
Strengthen the sources models trust
AI systems lean on sources that are clear, consistent, and machine-readable. We tighten your own site and profiles so they state clearly who you are and what you do, align names, descriptions, and details across every surface, and add structured data so the entity behind the name is unambiguous. Consistency across sources is what models reward.
Separate you from the wrong entity
Misattribution is one of the most common AI answer problems: a model folds someone else's record into yours because the names match. The fix is disambiguation. We build out enough distinct, corroborated detail that the model can no longer plausibly merge the two: your location, your work, your history. The same discipline applies when an old version of you is shadowing the current one.
Work the negative sources directly
If the answer is leaning on a review, thread, or post that breaks a platform's own rules, we pursue removal at the source, the same way we do in ordinary search work. Where removal is not realistic, we rank stronger truthful material around it so it carries less weight in what the model reads. When a source comes down or loses prominence, the synthesized answer tends to follow.
Watch the answers as they shift
AI answers are not fixed. They regenerate, vary by phrasing, and change as the underlying sources change. So we re-ask the same questions on a steady cadence, log how the answers move, and report inaccurate output through the feedback channels platforms provide where they exist. The work is iterative: adjust the sources, watch the answer, adjust again.
What is realistic
What the models will repeat, and what they won't
Models repeat what the record supports. Feed them accurate, consistent, substantial sources and the answers tend to reflect that. But nobody dictates the output, and a promise about what a model will write would not be an honest one. The model decides; what we shape is the record it decides from.
Removal of an underlying source is only realistic where the content breaks a platform's own rules or the law; we pursue it where the case is real and say plainly when it is not. Where removal is off the table, suppression and source-building carry the work, and results arrive gradually as models re-read the web. A matter that turns legal goes to counsel, and the source map we built goes with it. You will know which route yours is before any work starts.
How a wrong answer gets fixed.
Audit the answers
We put the questions that matter about you to Google's AI surfaces and the major assistants, log every answer, and trace each claim back to a source. You get a plain account of what is being said and why.
Shape the sources
We strengthen what you own, align your profiles, build credible corroborating material, and pursue removal where a source qualifies for it. Every move targets a specific claim in a specific answer, not content for its own sake.
Re-test and hold
We re-run the same questions at set intervals, track how the answers move against the first audit, and keep adjusting the sources as the output shifts. Then we keep watch, because the web changes and the answers change with it.
What people ask about AI answers.
Can you change what Google's AI Overview says about me?
Often, yes, indirectly. AI Overviews are synthesized from public sources: your site, profiles, reviews, coverage, and threads. No one edits the answer itself, but changing the sources the model reads changes what it writes. We find the sources behind the current answer, correct and strengthen them, then re-test. The answer is the model's; the material it works from is where the influence lives.
Why does ChatGPT or Google AI say something wrong about me?
Usually because the sources are wrong, thin, or ambiguous. Models compress whatever is publicly available. If your name is shared with someone else, if old information was never updated, or if a critical thread is the loudest thing written about you, the model picks it up and states it with confidence. The error almost always traces back to a specific source, and that source can be addressed.
Is it ethical to influence what AI says about you?
Yes, the way we do it. Everything in this work is truthful material published openly: accurate descriptions, consistent profiles, real credentials, legitimate coverage. We do not feed models false information, fabricate reviews, or manipulate anything covertly. Where we pursue removal of a source, it is because that content breaks a platform's own rules or the law. Correcting the record is not gaming the system. It is putting the record right.
How long does it take to change an AI answer?
It depends on what is driving the answer. Updating your own site and profiles is fast; getting models to re-read the web and reflect the change is not in anyone's direct control, and removal or suppression of third-party sources takes longer still. We set expectations case by case at the first conversation, and we track the answers so you can see movement as it happens.
Is AI Overview optimization a one-time fix or ongoing?
Both exist, and the honest answer is that the surface keeps moving. A focused engagement can correct a specific wrong answer by fixing the sources behind it. But models update, sources shift, and new content appears, so most situations call for a monitoring arrangement: we keep re-asking the questions and step in when an answer starts to drift. You choose the depth; we tell you what yours actually needs.
Take the helm
Ask what AI says about you.
Send us the name people search. We will read the answers privately, work out what is feeding them, and tell you what it would realistically take to move them. The next time the models look, the record can be ready.