Why AI models put your brand in the wrong category

TL;DR:

• AI models place B2B brands in adjacent categories more often than most teams expect. In Discovered Labs' study of 1,000 B2B SaaS brands, Gemma 4 put 194 of 981 brands (19.8%) in a different category from ground truth.
• Error rates vary by vertical: regulated verticals hit 37%, IT and internal operations hit 0%. Closed assistants erred far less often: about 2% for Claude, 0.8% for Gemini, and 0.4% for GPT.
• Models learn categories from co-occurrence. A wrong label wins when it sits closer to your brand name in stored associations. Compact labels like CI/CD act as category magnets, attracting 9 misfiled brands in the study.
• The fix is explicit category language on your site plus consistent descriptions on Reddit, review sites, and comparison content.

B2B buyers ask AI assistants which vendors fit a category, and those answers don't always place your brand correctly. A model that files you under an adjacent category puts you on the wrong shortlist, next to the wrong competitors. This piece covers what the data shows, why these errors follow predictable patterns, how to diagnose them, and how to brief the fix.

If your brand doesn't appear in AI answers at all, rather than appearing under the wrong label, that's a different failure mode. See brand recall vs category accuracy to work out which one you have.

AI brand miscategorization happens when a model files your brand under an adjacent category, for example an identity platform listed among CI/CD tools. In Discovered Labs' brand recall study, regulated verticals had a 37% different-category rate, compared with 0% for IT and internal operations. Gemma 4 placed Okta under CI/CD instead of identity, and Square under card issuing instead of SMB commerce. Correcting it means stating one primary category on your site and matching that description everywhere third parties describe you.

What the data shows about AI brand miscategorization#

Discovered Labs tested what AI models know about 1,000 B2B SaaS brands before web search. Gemma 4 chose a category for 981 of them, and 194 (19.8%) differed from ground truth.

Closed assistants were wrong far less often, at about 2% for Claude, 0.8% for Gemini, and 0.4% for GPT, though GPT also declined to classify 287 brands. The closed assistants saw the category options and could answer "I do not know," while Gemma 4 ranked candidate categories with no option to abstain. Treat the Gemma 4 rate as a view into what model weights store before any web search.

The failures aren't evenly distributed. Regulated verticals had a 37% different-category rate. Developer tools followed at 30%, while IT and internal operations sat at 0%.

What this means for you: your vertical changes the odds. A developer tools brand starts with far more category risk than an IT and internal operations brand.

Which adjacent categories AI models confuse#

Discovered Labs' brand recall study logged these errors from Gemma 4. Each brand landed in a category next to its own.

Relation extraction research documents a related mechanism, entity bias, which researchers define as the spurious correlation between entity mentions and relations. A wrong label wins when it sits closer to the brand name in the model's stored associations than the right one.

Compact technical labels like CI/CD act as category magnets. The table fits that pattern: Okta and Akamai come from unrelated categories, yet both landed under CI/CD. In the study, CI/CD attracted 9 misfiled brands while containing only 2 of its own.

What may drive the 37% error rate in regulated verticals#

Regulated verticals saw the highest different-category rate in the study, against 0% for IT and internal operations. The study reports the rate without a cause, but three factors likely contribute:

1. Terminology density. Compliance-heavy language may be less common in general web text. That leaves the model fewer clear examples of the right category.
2. Adjacent category overlap. Regulated brands often span several neighboring categories. A payments brand might also offer card issuing, and the model picks one category over the other.
3. Compliance language masks product function. A brand that describes itself mainly through compliance terms gives the model little to separate it from broader infrastructure categories.

The takeaway: the study's highest error rate sits in regulated verticals. Confirm the result on the assistants your buyers use, since closed assistants erred far less often than Gemma 4.

Why neural networks cluster adjacent entities#

LLMs don't categorize brands by looking up a definition. Two inputs shape the answer: what the model absorbed in training and the pages it retrieves when it searches the web. Both reward the same fix, which is stating your category consistently wherever your brand is described.

On the training side, language models can rely on dataset bias as shortcuts for prediction, which weakens how well they generalize. If most content about identity and access management also mentions CI/CD, the model learns to cluster those concepts without learning the boundary between them.

Self-checking doesn't solve this. LLMs produce wrong information because of incomplete or outdated knowledge, and automated checks struggle too. A 2024 survey reports that even a GPT-4 verifier using Google search evidence reached an F1 score of only 0.63 at spotting false claims.

What this means in practice: the model returns the most statistically likely category. If your brand appears next to an adjacent category more often than next to your own, expect the adjacent one.

How miscategorization hits pipeline#

Miscategorization changes which shortlist you appear on, which competitors you're compared against, and whether you show up for high-intent queries at all.

AI answers now sit on top of a meaningful share of searches. In a Pew Research Center study of 900 U.S. adults' browsing in March 2025, 18% of Google searches produced an AI summary.

Users who saw one clicked a traditional result in 8% of visits, compared with 15% when no summary appeared. Clicks roughly halve, so the AI answer often becomes the shortlist.

AI search also draws on different sources than Google. A 2025 study found AI search engines show a systematic bias toward earned media, meaning third-party sources, while Google keeps a more balanced mix. Strong rankings on your own site don't guarantee AI systems will describe you correctly.

AI answers to category questions often name specific vendors. If competitors appear in the right category and you appear in the wrong one, they make the shortlist and you don't.

The CMO's problem: you're investing in content, SEO, and positioning, but the AI tells prospects you do something else. Google Analytics won't show you this gap.

How to diagnose if your brand is miscategorized#

Before you commission content to fix this, confirm the problem exists. Have your SEO lead run this diagnostic across ChatGPT, Claude, Perplexity, and Google AI Overviews.

Step 1: Build a prompt set#

Use 40-80 high-intent prompts that reflect how buyers search. A strong prompt is specific: "What are the best customer onboarding platforms for a B2B SaaS company that needs to reduce time-to-value for enterprise accounts?" That level of context produces a shortlist and shows whether your brand is tied to the right use case.

Step 2: Test category accuracy#

Ask each model directly: "What category does [your brand] belong to?" Then ask it to list competitors in that category. If your brand is missing, or lands in a different category when you ask "What does [your brand] do?", you have a miscategorization problem.

Step 3: Check competitive proximity#

Look at who appears alongside you. If you're an identity and access management platform and the model lists you next to CI/CD tools, you're in the wrong consideration set.

Step 4: Audit source preference#

Check which of your pages AI search cites most. If it pulls from an integrations blog post instead of your product pages, it's reading category signals from the wrong content.

Step 5: Verify framing accuracy#

Compare the AI's description of your company with your own. Outdated product descriptions, wrong founding dates, and deprecated use cases all erode trust before a conversation begins.

What to check and how to score it#

Record results for each platform against this checklist.

Success criteria:

• Citation presence: citation rate rising against your baseline on every platform
• Category accuracy: AI describes your category consistently
• Competitive positioning: direct competitors appear, not adjacent categories
• Source preference: product and solution pages cited at least as often as blog content
• Framing accuracy: positioning language aligns with your brand messaging
• Temporal accuracy: current features, dates, and use cases

If you fail several of these criteria, you likely have a systematic miscategorization problem rather than a one-off error.

How to fix miscategorization with entity clarity and third-party consistency#

You can't retrain the model, but you can change what it learns from and retrieves. The fix is entity clarity on your owned properties and information consistency across third-party sources.

On your site: use explicit category language in your H1s, meta descriptions, and product page copy. State it directly instead of leaving the model to infer it from feature lists. Gemma 4 placed Okta under CI/CD, so "Okta is an identity and access management platform" beats a vaguer line like "Okta helps developers secure access."

Use structured data: Google uses structured data to understand the content of a page, so schema gives search systems an explicit statement of what your brand is. Use Organization schema with a precise description plus sameAs links to your official profiles. On product pages, set the Product schema category property to your exact category.

On third-party sources: AI search engines lean heavily on third-party sources. If your category is right on your site but wrong on Reddit or review sites, those conflicting signals can pull the answer off course.

Fix comparison content: if category roundups list you under the wrong category, those associations can shape how models categorize you. Correct the record where models learn, beyond your own domain.

How Discovered Labs helps#

Discovered Labs runs SEO and AI search as one operation. Our analysis of 144,000 AI citations found Reddit referenced in roughly 27% of ChatGPT's search results, which is why we treat off-page consistency on platforms like Reddit as citation infrastructure. Our brand recall study adds a direct check: after major changes, ask each assistant how it categorizes your brand.

We helped incident.io raise AI visibility from 38% to 64% with CITABLE content built around clear entity relationships, a consistency audit of legacy content, and Reddit mentions in the subreddits its buyers already used. The result was +22% organic meetings booked.

How to brief your content team#

Once you've confirmed miscategorization, brief four workstreams. None of them requires a rebrand.

1. Audit your existing content for category clarity. Does every product page state your category in the first 100 words? If not, rewrite the opening. Use the CITABLE framework: Clear entity and structure, Intent architecture, Third-party validation, Answer grounding, Block-structured for RAG, Latest and consistent, Entity graph and schema.

Your help center is often the highest-leverage place to start this audit. See our piece on help center as AI memory asset.

2. Build category-specific comparison content. If the model thinks you're a CI/CD tool and you're an identity platform, publish content that contrasts the two: "Identity and access management vs CI/CD: what's the difference?" This gives the model a clear signal about category boundaries.

3. Fix third-party inconsistencies. Find every place your brand is described incorrectly (Reddit, review sites, industry publications, partner pages) and correct it. This is slow, manual work. It's also how you change what models learn and retrieve.

4. Use structured data to reinforce entity clarity. Schema markup won't fix miscategorization on its own, but it gives search systems an explicit signal about what your brand is. Use it on every product page.

We built the free AEO (answer engine optimization) content evaluator for the first workstream. It grades your content against all seven CITABLE dimensions, including entity clarity and extractable facts. If you want to run the category audit yourself, start there, then map your category signals across Reddit, review sites, and industry publications. Models learn from all of it, so correct all of it.

What to do next#

Category errors follow predictable patterns, so you can test for them and correct them. If you sell into a regulated vertical or build developer tools, run the category accuracy test before anything else.

Run the diagnostic above across ChatGPT, Claude, Perplexity, and Google AI Overviews. If you find category errors, focus on entity clarity and third-party consistency before you add content volume.

We run this diagnostic as part of our Search Visibility Diagnostic, a €4,370 one-off audit that's 100% credited to your first retainer month if you sign within 14 days. You get a full diagnosis across Google, ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. It comes with a prioritized 90-day plan plus a 60-minute findings walkthrough with a co-founder.

Book a call and we'll tell you honestly whether we're a fit before you commission content.

If you want to run the audit yourself, start with the AEO content evaluator to score your existing content. Then map your category signals across Reddit, review sites, and industry publications. Models learn from all of it, so correct all of it.

FAQs#

What is AI model brand miscategorization?#

AI model brand miscategorization is when a large language model places your brand in the wrong product category. In Discovered Labs' study of 1,000 B2B SaaS brands, Gemma 4 placed 194 of 981 brands (19.8%) in a different category from ground truth. Regulated verticals hit 37%, while IT and internal operations hit 0%.

Why do LLMs miscategorize B2B brands?#

LLMs miscategorize brands when a wrong category label sits closer to the brand name in the model's stored associations than the right one. In Discovered Labs' brand recall study, Akamai, placed under CI/CD instead of CDN, is one example. Relation extraction research describes a related effect called entity bias.

How do I know if AI is miscategorizing my brand?#

Run 40-80 high-intent prompts across ChatGPT, Claude, Perplexity, and Google AI Overviews. Ask each model what category your brand belongs to, then check which competitors appear alongside you. If you're listed next to adjacent categories instead of direct competitors, you're miscategorized.

Discovered Labs runs this check in its Search Visibility Diagnostic.

Can you fix AI brand miscategorization?#

Yes, through entity clarity on your owned properties and information consistency across third-party sources. State your category explicitly in product page copy and use structured data. Then correct inaccuracies on Reddit, review sites, and industry publications.

Discovered Labs' brand recall study recommends choosing one primary category. Keep that description consistent across your homepage, product pages, help center, and partner pages.

How long does it take to correct AI miscategorization?#

There's no fixed timeline, because each AI platform updates on its own schedule. Discovered Labs' brand recall study recommends checking how each assistant categorizes your brand after major content changes.

Key terms glossary#

Entity bias: Spurious correlations a model learns between entity names and the relations attached to them. Relation extraction research documents it, and brand-category errors follow a similar pattern.

Category accuracy: Whether an AI model places your brand in the correct product or service category when generating answers.

Information consistency: The degree to which accurate claims about your brand appear consistently across independent sources (your site, Reddit, review platforms, industry publications).

Citation rate: The percentage of relevant queries where an AI model cites your brand in its response, measured across multiple platforms.

Stored associations: Patterns learned by neural networks during training that link concepts based on co-occurrence in text, rather than explicit category definitions.