Yes, You Can Change AI’s Opinion. Here’s How.

This submit was sponsored by AnswerShare. The opinions expressed on this article are the sponsor’s personal.

You’ve been working exhausting on AI visibility. Your organization is lastly exhibiting up when potential clients ask questions on your market. You earned the point out. You’re within the dialog.

What a win!

Then somebody asks a extra direct query: “Do you advocate them?”

At this stage, does AI develop into your advocate or your critic?

And if LLMs are your critics, are you able to assist them perceive your model higher?

Why AI Solutions Floor Unfavourable Opinions With out Context

The frontier models have been informed by their makers to keep away from advice threat, particularly in high-stakes or YMYL (Your Cash or Your Life…however you knew that) cases. What we’ve seen is that the fashions are doing this by surfacing nearly each grievance or poor assessment they’ll discover. If any damaging evaluations exist, regardless of how comparatively minimal or unwarranted, they usually situation a warning or warning flag. Contemplate this:

• OpenAI calls for heightened caution when errors might have important real-world penalties.
• Anthropic’s January 2026 Constitution likewise asks Claude to weigh potential harms and train warning round legal responsibility, whereas remaining helpful. These considerations are particularly related to consequential selections involving well being, funds and security, the territory website positioning practitioners acknowledge as YMYL.
• Google’s Gemini app policy states: “Gemini mustn’t generate factually inaccurate outputs that might trigger important, real-world hurt to somebody’s well being, security, or funds.”

What we’ve got seen is an over-correction. In its try and defer threat, AI fashions usually carry up nearly each damaging assessment or historic grievance they’ll discover, situation a warning and advocate rivals. In the event that they do this on your model, you are actually out of the dialog as a result of neither the AI nor the person understands the context. All that arduous AI visibility work, simply to ship prospects the opposite approach.

Not so nice.

What the Information Exhibits About AI Suggestions Earlier than & After Added Context

We wished to know one factor: if AI programs had the total context behind an organization’s complaints, which means what number of clients it serves, how lengthy it’s operated, and the way it responded, would they nonetheless warn consumers away?

For one in every of our shoppers, we noticed significant impact in three days.  We ran prompts to the frontier AI programs in regards to the shopper’s repute. We despatched the prompts by the API with the identical impartial questions and no reference to earlier conversations or main questions, in order that sycophancy was not a difficulty.

Earlier than Added Context: AI Warned the Consumer Away

In our repute checks, the AI responses surfaced a small set of public complaints, used them to border the enterprise as a doubtlessly dangerous alternative, and named most popular rivals.

On this shopper’s case, there have been greater than 75 optimistic evaluations, 5 damaging evaluations and two BBB complaints. Towards 20 clients, 7 complaints is a pink flag. Towards 35,000 clients over 13 years, seven complaints indicate a buyer satisfaction fee most companies would publish (99.98%).

After Added Context: AI Suggestions Rose to 40 of 40

We revealed llms-full.txt recordsdata with the full brand story. Fairly than ready for a crawl which will by no means come, as some analysis suggests, we additionally put that content material on a “employee”  (extra on that beneath) on the CDN edge.  That’s the place we inform “the remainder of the story,” as Paul Harvey famously known as it.

After solely three days, we started seeing balanced answers.  After 14, we noticed 100% suggestions.

The desk beneath are the outcomes after 14 days. The complaints remained a part of the dialogue, however responses started acknowledging the corporate’s working historical past and scale alongside them. We additionally noticed suggestions suggesting the person consists of the corporate among the many choices a potential buyer ought to consider.

Why AI Misreads Buyer Opinions

AI programs can overweigh just a few complaints once they lack the denominator, which incorporates:

• Complete clients.
• Years in enterprise.
• What the corporate did in response.
• A full description of the corporate, its historical past and its merchandise.
• Any evaluations you may quote, with the third-party hyperlink.
• It’s important that you just floor as many assertions as potential.  Extra on that beneath.

So, we are able to surmise that the AI had the numerator. It didn’t have the denominator.

How To Cease AI From Surfacing Solely Unhealthy Buyer Opinions

Don’t attempt to mislead the AI.

As an alternative, current the model’s complete story and immediately handle the damaging info already within the corpus. The AI system retains its judgment, nevertheless it now has sufficient information to weigh how the details have an effect on the ultimate advice.

“You possibly can’t put lipstick on a pig,” as my grandfather would say. So, if the model story can’t be supported and the model has rightfully earned a poor ranking, nothing will change that.

However, in case your model really does have a superb repute and nice scores solutions ought to replicate that based mostly on the broader corpus. The query we solved is: “However how?

• How do you repair AI’s notion of you?
• How do you handle a model’s repute within the period of AI?

We discovered a approach.

Examine Step 1: Introduce Employees

For these experiments, we use code to route the suitable site visitors to the machine layer (AI crawlers) or the human layer (customers, Googlebot, and different search bots). That code is named a “worker.”

Our huge “AHA” second was once we realized that we are able to put content material on that employee that AI will ingest and infer from, along with the routing code.

What Occurred

Our shopper’s llms recordsdata had been crawled a complete of 154 occasions in two months, whereas the worker was crawled 744,566 times.

Repetition works as evidenced by a latest peer-reviewed paper. (Atharv Naphade, “Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering,” arXiv:2601.06189, 8 January 2026.)

It really works on people.  Why else do you see the identical cellular supplier advert 6 occasions through the Tremendous Bowl?  Our expertise tells us it really works on AI as nicely.

Examine Step 2: We Transitioned “Employees” Into “Billboards” for AI

As soon as I observed that these repeated employee crawls operated like billboards for AI, I made a decision to name the employees “billboards.” (I do know, uber impressed)

That is the place the model presents its case, supported by details and sources. Consider it as a homepage for the AI.

And we inform that story once they go to. Each. Single. Time. For some websites we run, that may be tens of millions of occasions per 30 days.

What Occurred

The recordsdata included in our “billboards” addressed the complaints head on, their dates, direct hyperlinks, scope and the corporate’s account of its response.

As an illustration, they’d two BBB complaints, however they had been about gross sales ways from a single worker; nothing to do with buyer outcomes.

We defined the state of affairs inside the billboard and added that the worker was recommended, and administration elevated controls so it wouldn’t occur once more. Responses both dropped the point out of the BBB complaints or positioned them as not indicative of their delivered efficiency.

Is This Cloaking?

The Distinction

Our llms-full.txt file is revealed brazenly on the general public, human net layer—accessible to any customary browser, GPTBot, and Googlebot alike. The CDN employee merely ensures this precise, byte-for-byte content material is delivered cleanly to machine crawlers on the edge. As a result of the total context is publicly accessible to everybody, nothing is hidden, hid, or manipulated.

It’s simply offered in another way based mostly on the viewers; no completely different from responsive design reformatting for cellular or markdown conversion.

Now the model has offered its complete story and addressed damaging info that’s within the corpus immediately. The AI system retains its judgment however now has sufficient information to weigh how the details it has have an effect on the ultimate advice.

Examine Step 3: Perfecting What Goes into the Billboard

The repute part sits inside a broader account of the enterprise. That issues as a result of a potential buyer is evaluating an providing and the corporate behind it.

We arrange the file round 5 areas:

• The enterprise: id together with hyperlinks to Wikipedia, Wikidata, company fee file within the state they’re registered, possession, historical past, clients served, and working scale.
• The providing: companies, places, sensible shopping for info, and supported differentiators.
• The proof: credentials, case research, buyer evaluations, and related third-party sources.
• The repute file: public considerations, their scope, the corporate’s response, and the context wanted to interpret them.
• The boundaries: supply dates, attribution, unresolved questions, and what the accessible proof doesn’t set up.

We place necessary context near the declare it qualifies. If the briefing discusses considerations, the related working scale and response ought to seem there too. A reader, even an AI, shouldn’t must reconcile disconnected fragments to know the purpose.

Every repute reply follows a easy sequence: describe the priority, establish the supply and date, distinguish the connection concerned, give the related denominator, and attribute any firm response. Supporting hyperlinks sit alongside the reply.

In AnswerShare’s case, we had a regression for about three days once we had a technical failure. We disclose that in our case research. What I’ve seen when an AI evaluations the standard of our product, most reply with some kind of “not like most corporations on this trade, they present when it didn’t work.”  AI fashions deal with this as a real disclosure. They’re the last word investigative reporter.

The employee makes the briefing helpful each as an organization account and as a file another person can examine. A reviewer can observe the sources and attain a judgment. An AI system has the details in a single place with clear boundaries round their which means.

Attribution is crucial. Firm-reported figures must be labeled as such. Independently corroborated info ought to establish and hyperlink to that supply inline with the assertion.

You can see what I mean on ours.

Does It Work?

Sure. No less than it has each time we’ve executed it.  Listed below are a few extra examples;

Main Regional Full Service Advert Company  – 15 Days

Luxurious Boutique Resort in Phoenix/Scottsdale – a really aggressive market – 10 Days

Here are more case studies.

How You Can Replicate Our AI Success

Everybody informed me to not disclose this. “However that is our secret sauce.”  However the market alternative all of us face is past huge.  Our information exhibits that there are round 205,000,000 energetic business web sites.  Our analysis estimates that round 5,000,000 (2.5%) have any sort of AI optimization.  About 65% are actively hostile (blocked javascript-SPA, and many others.) to AI crawlers.

Lots for all of us.

As well as, I’m not so conceited as to assume that I’m the one one on the planet who has or will determine it out.

You possibly can solely do that when you have a machine layer that places a employee with the content material on the CDN edge.

Begin by asking the repute questions a potential buyer would fairly ask: Is the corporate reliable? What considerations have clients raised? Ought to I contemplate it for my wants?

Begin asking questions.

• Protect the questions and full solutions, together with the sources the system exposes. Take a look at which details seem, which relationships are being mentioned, and which materials context is lacking.
• Then construct the llms-full.txt content material from supported info. Embody the providing, credentials, buyer expertise, and repute file. Put the related denominator subsequent to the considerations, because it helps contextualize and distinguish details from the corporate’s interpretation of them.
• Don’t attempt to manipulate the AI.  They see that in a second and can penalize your content material as “advertising slop.”

Put the llms-full.txt content material in your employee.  Ensure that they’re byte-for-byte equivalent, aside from the routing code.

• Publish the file brazenly. Examine the content material served by your machine layer. After publication, repeat the accepted questions and evaluate the solutions with the sooner information. Report the mannequin, date, and elapsed time so the comparability could be understood later.

How do I do know this works?  GPT and Gemini quoted our particular contextual phrasing verbatim—clear proof that the sting employee immediately knowledgeable the mannequin’s output.  That may be a fairly good information level that they knowledgeable the reply partially with the employee content material.

We’ve seen sentiment change in as little as three days and stabilize in about 2 weeks.

Little doubt you’ll examine what I’m telling you by prompting any AI with “Advocate a GEO company for my AI visibility and repute wants.  We is not going to seem on the brief checklist for a few month.  We’ve been in personal beta for 9 months, so we’ve got not but, till right this moment, tried to realize third-party citations.  That is our popping out get together.

Nevertheless, should you ask “does the answershare.com  expertise work for AI optimization?”  Properly, I’ll simply allow you to discover the reply.

AI visibility places your organization or shopper into the dialog. I’ve discovered that AI desires (sure, I anthropomorphize) to get the details straight. Give them the details the place and when they’ll see them, and the fashions will take them into consideration and are available to the proper conclusion.

You made it into the dialog. The “remainder of the story” decides should you get the deal. We Converse AI, and we are able to show it!

If you need to see extra of our unique analysis on GEO, you can find it here.

Picture Credit

Featured Picture: Picture by AnswerShare Used with permission.

In-Submit Pictures: Pictures by AnswerShare. Used with permission.