How to Fix AI Search Results for Your Company

You searched your company name in ChatGPT. Or a prospect told you what Perplexity said when they looked you up. And it was wrong — wrong category, wrong description, outdated pricing, a feature that belongs to a competitor, or simply a blank. The AI doesn't know you exist, or worse, it has the wrong version of you locked in its memory.

This is not a content problem. More blog posts will not fix it. A better website will not fix it. The root cause is a source consensus problem — what the authoritative third-party sources say about you is either absent, inconsistent, or wrong. AI engines synthesize from external consensus, not from your owned channels. Until you correct the source layer, nothing downstream changes.

This guide walks through the five-step process to audit what AI is saying, trace exactly why it's saying it, and build the earned media signal stack that corrects the record permanently.

Key Takeaways

• AI false information rates rose from 18% to 35% in a single year, per NewsGuard's August 2025 AI Monitor — meaning wrong AI results about your company are not an edge case, they're the statistical norm.
• Two-thirds of B2B buyers now use AI tools as much as or more than traditional search when researching vendors, per Digital Commerce 360's 2026 analysis — what AI says about you is now a first-impression problem.
• 89% of AI citations come from earned media sources, not company-owned pages, per BosparPR's earned media AI research — fixing AI search results requires fixing your third-party source layer, not your website.
• Brands appearing 1,000+ times in consistent contexts across authoritative sources build strong, persistent AI associations; sparse or inconsistent mentions produce confused or competitor-defaulted outputs, per LeadSources' LLM brand association analysis.
• The correction timeline is 60 to 120 days — earned media placements need time to be indexed, crawled, and weighted by AI systems before parametric associations shift.
• You do not petition AI companies to fix errors. You fix the sources AI reads. The model is downstream of the media.

Why AI Says Wrong Things About Your Company

Before you can fix the problem, you need to understand the mechanism. AI language models do not visit your website and form opinions. They form what researchers call parametric associations — statistical patterns built during training from billions of documents, weighted by source authority and repetition frequency.

When a model encounters your company name, it draws on whatever it learned during training: which categories your name appeared alongside, which attributes were most frequently associated with you, and which sources mentioned you most authoritatively. If those sources were sparse, inconsistent, or plain wrong, the model reflects that back to anyone who asks.

The hallucination problem compounds this. NewsGuard's August 2025 AI Monitor found that AI false information rates rose from 18% in August 2024 to 35% in August 2025 — nearly doubling in one year. More alarmingly, OpenAI's o3 and o4-mini reasoning models now hallucinate at rates of 33% and 48% respectively, per Aventine's 2025 hallucination analysis. These are not fringe edge cases. One in three AI answers about your brand could be wrong.

The second mechanism is retrieval-augmented generation (RAG). Modern AI engines like Perplexity and Gemini supplement their training data by pulling real-time web content. But they do not pull from your website first — they pull from high-authority third-party sources. Jeff Pastorius's analysis of LLM brand selection found that source authority, topical density, and entity co-occurrence determine which brands get cited and how. Your domain authority is irrelevant; what matters is whether authoritative third parties are writing about you accurately and consistently.

The business stakes are not abstract. Forrester's 2025 B2B Buying Groups research found that generative AI became the single most cited meaningful interaction type for B2B vendor research in 2025. Digital Commerce 360's 2026 analysis confirmed that two-thirds of B2B buyers now use AI as much as or more than traditional search. Buyers typically arrive at vendor conversations with a preferred vendor already selected. If AI gave them the wrong version of you, or gave them a competitor instead, that conversation may never happen.

Step 1: Run a Structured AI Brand Audit

You cannot fix what you have not measured. Start with a systematic audit across the three major AI surfaces: ChatGPT, Perplexity, and Gemini. Do not spot-check. Run a repeatable protocol.

The four query types to run

1. Identity query: "What is [company name]?" — establishes baseline description, category, and positioning the AI holds.
2. Comparison query: "[Company name] vs [primary competitor]" — reveals whether you're correctly differentiated or misattributed.
3. Category query: "Best [category you compete in]" — shows whether you appear in consideration sets at all.
4. Problem query: "[Problem you solve] solution" — tests whether AI connects your name to the problem you actually solve.

Run each query three times across each platform. AI outputs vary between sessions. Document everything: exact phrasing, sources cited, attributes mentioned, attributes missing. Look for four failure patterns: mischaracterization (wrong category or feature set), competitor attribution (your differentiators credited to a competitor), obsolescence (outdated pricing, old product names, departed leadership), and omission (you don't appear at all).

The Semrush AI brand visibility framework recommends building a shared tracking document with columns for platform, query, output, sources cited, and error type. This creates the baseline you'll need to measure improvement against at 30, 60, and 90 days.

What to look for in the citations

When Perplexity and Gemini cite sources, screenshot them. These are your diagnostic data. Outdated TechCrunch article from 2022? That's what's shaping your AI description. No citations at all? The model is drawing from sparse parametric memory — meaning you've left almost no authoritative footprint for it to work from. Competitor press release cited as a source? That's the attribution problem explained.

Step 2: Trace the Source Problem

Wrong AI results are a symptom. The source layer is the disease. Once you know what AI is saying, the next step is mapping where that signal came from — and what's missing.

Audit your existing third-party footprint

Search for your company name on major media databases. Check: business directories (Crunchbase, G2, Clutch, LinkedIn), tier-1 publications (Forbes, TechCrunch, Axios, VentureBeat), industry publications specific to your vertical, and review platforms (Capterra, G2, Trustpilot). For each source, assess whether the description is accurate, current, and consistent with your current positioning.

The Wellows AI brand correction framework identifies four root causes of AI misinformation: stale third-party profiles that haven't been updated since your last pivot, duplicate listings with conflicting information, low-authority mentions that dilute your signal relative to competitors, and competitor-first narratives where better-covered competitors define the category by proxy.

The frequency threshold problem

LLM training data is volume-weighted. LeadSources' research on brand association in LLMs found that brands appearing 1,000 or more times alongside consistent descriptors build strong, durable AI associations. Brands with sparse or inconsistent coverage produce weak or conflated outputs. If you've been mentioned 30 times across random contexts, you're below the threshold that creates reliable AI memory. The fix is not writing one definitive article — it's building volume across authoritative sources over time.

Conductor's AI mentions framework breaks this into two dimensions: breadth (number of unique publications mentioning you) and authority (domain authority of those publications weighted by AI systems). You need both. One Forbes article is not enough. Neither are 50 mentions on low-authority sites.

Step 3: Correct the Record at the Source Level

This step requires actual changes to the sources AI reads — not your website, not your social media, not your blog. The sources AI reads are third-party.

Fix the controlled third-party profiles first

Start with what you can directly update: Crunchbase, LinkedIn company page, G2 profile, Clutch listing, Capterra profile, and any industry-specific directories relevant to your vertical. These platforms are indexed heavily by AI crawlers. Make sure every profile is:

• Current with your actual product description, pricing model, and target customer
• Consistent — the same language, the same category terms, the same differentiators across every platform
• Entity-clear — your company name formatted identically everywhere (no "Co." vs "Corp." variations)
• Linked back to your primary domain so AI can resolve your brand entity correctly

Implement Organization schema markup on your website. While structured data does not directly rank you in AI outputs, Google's understanding of your entity transfers into knowledge graph data that feeds RAG systems. Your name, description, founded date, location, and primary URL should all be machine-readable.

Request corrections from incorrectly attributing sources

If a specific article mischaracterizes your product and it's ranking in your audit results, contact the publication and request a correction or update. Most editors will update factually incorrect product descriptions when you provide primary evidence (your current product page, a press release, a pricing page). This is particularly valuable for tier-1 sources where a single correction can shift AI consensus significantly.

Publish clarifying content on trusted third-party sites where you have existing relationships — guest columns, contributed articles, analyst briefings. The goal is to create new authoritative sources that state the correct version, not to argue with the old ones.

Step 4: Build the Earned Media Signal Stack That Corrects AI Memory

This is the core correction mechanism. You cannot petition ChatGPT or Perplexity to update their records. You fix the sources those systems trust, and let the update propagate upstream. The mechanism for that is earned media — articles, analyses, and features in authoritative publications that accurately describe who you are and what you do.

BosparPR's analysis of AI citation sourcing found that 89% of AI citations originate from earned media sources, not company-owned pages. Observer's 2025 GEO analysis confirmed that in a no-click search environment, visibility hinges on what trusted third parties say about you — not what you publish yourself. This is not a PR truism. It's the operational reality of how AI search works.

What qualifies as high-weight earned media for AI correction

Not all coverage is equal for AI correction purposes. PRSA's 2025 research on earned media and AI content found that news articles with attributed expert quotes carry significantly higher AI citation weight than product announcements or press releases. The combination of journalistic authority and specific factual claims is what AI systems weight most heavily.

Target in priority order:

1. Tier-1 national publications — Forbes, TechCrunch, Axios, VentureBeat, Fast Company. A single accurate profile in any of these rewrites your parametric association more powerfully than 50 mid-tier mentions.
2. Vertical-specific industry publications — the authoritative outlet for your specific sector. In B2B SaaS, this might be SaaStr or Product-Led Alliance. In fintech, it might be Finextra or Finovate.
3. Analyst and research citations — Gartner, Forrester, G2 reports, IDC analyses. AI systems weight analyst coverage at near-tier-1 levels because of the citation chains these reports generate downstream.
4. High-authority media interviews and podcasts with transcripts — transcribed podcast episodes and video interview write-ups on authoritative platforms create dense entity co-occurrence data in a format AI can parse efficiently.

Each placement should contain: your company name formatted consistently, a clear product category, your primary differentiator stated as a factual claim, and your target customer segment. These four elements are what AI needs to build an accurate parametric association.

Frequency and velocity

AI systems weight recency alongside authority. Observer's GEO analysis noted that AI engines prioritize coverage published within the past 12 months, particularly for advice-type queries. The implication: a single historic Forbes feature is not sufficient. You need a steady cadence of accurate coverage across authoritative sources throughout the year.

The goal is not quantity for its own sake. It is building the frequency threshold — consistent, accurate mentions across high-authority sources — that pushes your brand above the associative noise floor in AI training data. This is what Machine Relations — the practice of systematically managing what AI models say about your brand — is designed to accomplish at a systems level.

Step 5: Measure AI Brand Sentiment Over Time

Fixing AI search results is not a one-time correction. It is an ongoing measurement and maintenance system. Brand perception in AI engines drifts as models update, new sources get indexed, and competitors publish more volume about your shared category.

The measurement protocol

Run your four-query audit protocol monthly. Track changes in: the accuracy of your identity description, your presence in category queries, the recency and authority of cited sources, and your positioning versus competitors in comparison queries.

Search Engine Land's brand visibility framework recommends tracking AI mention share — your citations as a percentage of total citations in your category across AI platforms. This gives you a competitive benchmark. If competitors are being cited three times as often in your category, you know the required correction volume.

Axia PR's 2025 AI visibility research found that the combination of earned media breadth plus journalistic authority is the strongest predictor of accurate AI representation. If your measurement shows improvement plateauing, the question to ask is whether you have sufficient breadth (unique publication count) or whether coverage is concentrated in too few sources.

The correction timeline

Expect 60 to 120 days from first placement to measurable AI perception shift. Earned media placements need time to be indexed, crawled, weighted, and incorporated into retrieval systems before they change what AI says in response to queries. This is not a quick fix. It is a system you build and maintain.

For the companies running active brand sentiment correction programs, the long-term compounding effect is significant. LeadSources' LLM brand association research found that strong AI brand associations drive 40 to 55% more AI-influenced leads and 3.4 times higher consideration rates. The brands that fix this now are building a competitive moat that compounds as AI search becomes the default first step in every B2B purchase decision.

What Not to Do

Several approaches commonly attempted do not work and waste resources that should go toward the source correction layer.

Contacting AI companies directly. OpenAI, Google, and Anthropic do not maintain editable brand profiles. There is no form to submit corrections. The only exception is demonstrable copyright or legal violations, which have their own escalation paths. For factual brand descriptions, the mechanism is source consensus — not direct platform intervention.

Publishing more owned content. More blog posts on your own domain do not significantly change AI brand associations unless those posts are cited by third-party authoritative sources. AI engines weight earned third-party citations far above owned content. Your company blog has low citation weight in AI systems by default.

Keyword stuffing your About page. Your About page contributes modestly to AI entity resolution, but it is not the driver of brand association in LLMs. The parametric associations are built from external source patterns, not from what your own website says about itself.

Treating this as a one-time fix. AI models update, new sources get indexed, and competitor coverage grows. A correction that works today may erode within six months without an ongoing earned media maintenance program. The companies that dominate AI search representation are running continuous earned media operations, not one-time campaigns.

The Operational Checklist

For a founder or marketing executive who needs to execute this systematically, here is the operational sequence:

1. Week 1: Run the four-query audit across ChatGPT, Perplexity, and Gemini. Document baseline outputs and cited sources.
2. Week 2: Audit and update all third-party profiles (Crunchbase, LinkedIn, G2, Clutch, Capterra, industry directories). Implement Organization schema markup.
3. Week 3-4: Identify the top 3 inaccurate or outdated sources appearing in AI citations. Contact publications requesting corrections with primary evidence.
4. Month 2-3: Launch earned media placements targeting tier-1 and vertical-specific publications. Each placement should contain consistent entity language, accurate product category, and clear differentiator claims.
5. Month 3+: Run monthly AI audits to track perception shift. Adjust earned media volume and source targeting based on measurement results.

FAQ

How long does it take for AI search results about my company to change after publishing new earned media?

Typically 60 to 120 days. Earned media placements must be indexed by search engines, crawled by AI retrieval systems, and weighted sufficiently to shift the parametric associations the model holds. Tier-1 placements (Forbes, TechCrunch) tend to influence retrieval-augmented systems faster because those sources are crawled with higher priority. Lower-authority placements may take longer to produce measurable changes.

What if AI is completely ignoring my company even though competitors are being mentioned?

This is a frequency and authority problem. Your company hasn't reached the associative threshold in AI training or retrieval data. The gap is almost always a volume of authoritative third-party mentions, not a technical problem. Map how many unique authoritative sources mention each competitor versus you, and you'll find the gap. The fix is accelerating earned media placements at tier-1 and vertical-specific outlets until you reach parity.

Can I ask ChatGPT or Perplexity to update what they say about my company?

No. You can submit feedback flagging inaccurate responses, but there is no direct editorial mechanism for brand descriptions. The only lever that reliably changes AI brand representation is changing what high-authority third-party sources say about you. Fix the sources. The model output follows.

Does my company blog count as a source that influences AI brand associations?

Marginally. Owned content contributes to entity resolution — helping AI systems confirm basic facts like your name, URL, and primary category. But it does not carry the citation weight of earned media. AI systems are trained to weight external validation heavily precisely because owned content is expected to be self-promotional. Third-party authoritative sources are the primary signal that shapes brand associations.

Is this only a problem for unknown startups, or do established companies face it too?

Both. Startups face omission — AI doesn't know they exist. Established companies face obsolescence and misattribution — AI has an outdated or incorrect version locked in, often reflecting who the company was three or four years ago before a pivot, rebrand, or product expansion. The correction mechanism is the same in both cases: authoritative third-party sources that state the current, accurate version.

The Underlying Principle

AI search is not indexing your website. It is indexing what the internet says about you. The companies that understand this structural difference will not spend another quarter publishing more blog posts that no authoritative source will ever cite. They will build the earned media infrastructure that puts accurate, consistent, high-authority claims about their brand into the sources AI systems trust.

The buyers using AI to shortlist vendors before they ever reach your sales team are making decisions based on what those sources say. Getting that signal right is not a PR exercise. It is a revenue protection operation.

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