Ask a search engine about a company today and, increasingly, you do not get ten blue links. You get a paragraph. Google’s AI Overviews (the successor to the SGE experiment), Gemini, ChatGPT, Perplexity and Copilot all do the same thing: they read a large number of pages about your brand, compress what they find into a few confident sentences, and put that summary in front of the buyer before your website gets a single click.
For reputation management, that is a bigger shift than any algorithm update in the last decade. The thing a prospect reads first is no longer a page you can edit or a result you can outrank. It is a synthesis, written by a model, from sources you may not even know exist. This is why AI online reputation management has become its own discipline, separate from classic online reputation management.
This guide explains how AI search affects brand reputation, how generative search engines assemble a view of your brand, why the old playbook leaves gaps, and what businesses can realistically do to control the brand summary in Google Gemini and AI Overviews.
Generative Engine Optimization and ORM: Why They Now Overlap
Generative engine optimization (GEO) is the practice of making your content easy for AI systems to retrieve, understand and quote. Generative engine optimization ORM goes one step further: it applies the same thinking to what is said about you, not just what you publish. SEO decides whether you are found. GEO decides whether you are quoted. ORM decides whether what gets quoted is accurate and fair. In an AI answer, all three show up in the same paragraph.
What Has Actually Changed
Traditional online reputation management (ORM) was built around the results page. You pushed damaging links down, built up owned and positive assets, and managed reviews on the platforms that ranked. Success meant a clean first page.
Generative search changes the unit of competition:
- The answer replaces the list. An AI Overview or Gemini response often satisfies the query without a click. If the summary is negative or wrong, many buyers never reach the page that would correct it.
- Sentiment gets averaged. A model does not show you one angry review in context. It blends many mentions into a tone: “generally well regarded, though some customers report slow support.”
- Old problems resurface. A resolved complaint thread from three years ago can still shape the summary if it remains one of the more detailed sources on the topic.
- Errors sound authoritative. Models can merge two companies with similar names, repeat outdated facts, or state a rumour as settled. The fluent tone makes it more believable.
- Sources are mixed. The answer may draw on your website, review platforms, forums, news, Wikipedia-style references, and social posts in the same paragraph.
How AI Search Shapes SGE Brand Perception and Pulls Brand Sentiment
SGE brand perception is formed the same way across most AI search products. The details differ by product and change often, and none of the vendors publish their full recipe. But the broad pattern is consistent enough to plan around. Google’s SGE experiment has since become AI Overviews and AI Mode, so we use “SGE” here as shorthand for Google’s generative results.
1. Retrieval: finding sources
For live-search products such as AI Overviews, Gemini with search, Perplexity and Copilot, the system first retrieves pages relevant to the query, much like classic search. Pages that rank well, load cleanly and answer the question directly are more likely to be pulled in. This is why strong traditional SEO still matters: it feeds the retrieval step.
2. Synthesis: summarising what was found
The model reads the retrieved passages and writes a summary. It favours content that is specific, consistent across sources, and easy to quote: clear statements of what you do, who you serve, how you are rated, and what customers say. Vague marketing copy gives it little to work with, so it fills the gaps from third parties.
3. Background knowledge: what the model already “knows”
Even without live retrieval, a model carries an impression of your brand from its training data. That impression is slower to change and harder to correct directly. Over time, consistent, well-sourced coverage is the main way it shifts.
4. Corroboration: agreement across sources
When several independent sources say the same thing, the summary tends to repeat it. When sources conflict, the output becomes hedged or picks whichever is more detailed. A single glowing page on your own site rarely outweighs a pattern of complaints on third-party platforms.
The Sources That Shape Your AI Summary
The same categories of source keep appearing behind AI-generated brand descriptions. Check each one for your own brand.
- Review platforms: Google Business Profile, Trustpilot, G2, Glassdoor, Yelp and industry-specific sites. Recent, detailed reviews carry the most weight in tone.
- Community discussion: Reddit, Quora and niche forums. These are especially influential because they read like candid, first-hand opinions.
- News and press: Articles about lawsuits, layoffs, outages or controversies tend to persist and get quoted.
- Reference pages: Wikipedia, Wikidata, Crunchbase and similar entity databases help the model decide what your company is and which company you are.
- Your own properties: Website, About page, FAQs, help centre, leadership bios and social profiles.
- Employee and partner content: Glassdoor, LinkedIn posts, partner directories.
Why the Old ORM Playbook Falls Short
Suppression, pushing a bad result off page one, still has value, but it does not solve the AI problem on its own. A damaging article on page two can still be read and summarised by a model. And ranking a positive page does not guarantee it is the one the model quotes.
Likewise, a high star average is no longer enough. Models respond to what reviews actually say. A 4.6 rating whose written reviews repeatedly mention “hard to cancel” or “no one replies” can produce a summary that leads with exactly those complaints.
The goal shifts from “control the results page” to “make the accurate story the easiest one to find, quote and corroborate.”
Gemini ORM Strategy: What Businesses Must Do for AI Search Reputation Control
You cannot dictate what a model writes, and anyone promising guaranteed control is overselling. What you can do is improve the evidence it works from. These generative AI ORM tactics are the ones that consistently move summaries, and together they form a practical Gemini ORM strategy that also applies to ChatGPT, Perplexity and Copilot.
Step 1: Read your current AI summary
Before changing anything, record what the major engines say today. Ask Google (AI Overviews where available), Gemini, ChatGPT, Perplexity and Copilot questions a buyer would ask:
- “What is [Brand] and what do they do?”
- “Is [Brand] legit?” and “[Brand] reviews”
- “[Brand] vs [competitor]”
- “Problems with [Brand]” and “[Brand] complaints”
- “Who is the CEO of [Brand]?”
Save the answers, note which sources are cited, and repeat monthly. This is your baseline and your scoreboard. Results vary between sessions and locations, so look for patterns rather than single outputs.
Step 2: Fix factual errors at the source
If the summary is wrong, trace it to the page it came from. Models usually repeat an error that exists somewhere. Correct your own pages first, then request corrections on directories and databases, and ask publishers to update outdated articles. Where a platform has a feedback option on the AI answer itself, use it, but treat that as a supplement to fixing the underlying source rather than a replacement.
Step 3: Publish a clear, quotable brand fact base
Give models accurate material to draw on. Maintain a plain-language About page, a leadership page with real names and roles, a pricing or policy page, and an FAQ that directly answers the questions people ask about you, including the uncomfortable ones such as refunds, cancellation and data handling. Short, direct answers under question-style headings are far easier for a model to lift than long marketing prose.
Step 4: Strengthen entity signals
Make it unambiguous which company you are. Use consistent name, address and descriptions everywhere. Add Organization and LocalBusiness structured data, link your official social profiles, and keep your Google Business Profile, LinkedIn page and any Wikidata or Crunchbase entries aligned. This reduces the risk of being confused with a similarly named business.
Step 5: Build review volume and recency, honestly
Ask satisfied customers for reviews on the platforms your buyers use, and make it easy. Aim for steady flow rather than bursts, and encourage detail: reviews that describe what the customer bought and how the problem was solved give a model something specific to repeat. Do not buy, fake or incentivise only positive reviews. Platforms penalise it, regulators increasingly act on it, and a manipulated profile that gets exposed becomes the headline in the summary.
Step 6: Respond to criticism in public
Reply to negative reviews and threads calmly, factually and quickly, and say what was done to resolve the issue. Models read replies too. A complaint followed by a credible resolution reads very differently, and often summarises very differently, from a complaint left unanswered.
Step 7: Show up where the conversation happens
If Reddit threads or forum posts shape opinion about you, a transparent presence matters. That means real staff identifying themselves, answering questions and correcting facts, never astroturfing. Authentic participation is slow, but those discussions are among the most heavily weighted sources in AI answers.
Step 8: Earn third-party coverage
Industry features, expert commentary, awards, case studies and credible press give models independent corroboration of the positive story. One well-placed, substantive mention often does more than a dozen self-published pages.
Step 9: Handle legitimate negative content properly
Where content is defamatory, private, or violates a platform’s rules, use the official removal and legal routes. Where it is true but outdated, the best approach is usually to publish updated, well-sourced information and encourage fresh reviews so the recent picture outweighs the old one. Be wary of anyone who offers to “delete” accurate negative coverage.
Step 10: Keep the technical basics healthy
Make sure your key pages are crawlable, fast, indexable and not blocked from search or AI crawlers you want to be visible to. If your site is the best source on you but a model cannot read it, it will rely on someone else’s description.
Measuring Progress in Managing Brand Sentiment in AI Overviews
Managing brand sentiment in AI Overviews is an ongoing measurement job, not a one-time fix. Because AI answers are variable, track trends rather than individual outputs:
- Share of tested prompts where your brand is described accurately
- Overall tone of the summary: positive, neutral, mixed or negative
- Which sources are cited, and whether they are owned, earned or hostile
- Review volume, recency and rating on the key platforms
- Whether your brand appears at all for category and comparison queries
Expect changes to take weeks to months. Retrieval-based answers can respond fairly quickly once a strong new source is live. Shifts in a model’s built-in impression are slower.
Mistakes to Avoid
- Trying to game the model. Hidden text, fake reviews, sock-puppet forum accounts and mass-produced low-quality pages tend to backfire.
- Ignoring the uncomfortable questions. If you do not answer “is it a scam?” on your own site, someone else will.
- Treating it as a one-off project. Sentiment keeps accumulating. The summary reflects whatever is being said about you now.
- Watching one engine only. Different products weigh sources differently. Check several.
- Chasing rating over substance. The written content of reviews is what the model reads.
Key Takeaways
- Generative search puts a model-written summary between your brand and the buyer, often before any click.
- That summary blends sentiment from reviews, forums, news, reference databases and your own site, and rewards sources that agree with each other.
- Start by recording what each AI engine says about you today, then trace errors and negative themes to their source.
- Publish clear, quotable facts, strengthen entity signals, and build honest, detailed, recent reviews.
- Respond publicly, take part openly where discussions happen, and earn independent coverage.
- You cannot dictate the output, but you can make the accurate story the best-supported one. Avoid anyone who promises otherwise.
See What AI Says About Your Brand
If you are not sure what Google’s AI Overviews, Gemini or ChatGPT currently tell your customers, that is the place to start. Delima Digitals’ AI online reputation management services begin with a free reputation audit that documents how AI search describes your brand, which sources are driving it, and what to fix first.