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Medium Priority Use Case
ChatGPT Is Describing Your Product Wrong. Here Is How to Fix It.
ChatGPT mentions your brand but puts you in the wrong category, describes features you deprecated a year ago, or shows pricing that has not been accurate since your last funding round. Prospects arrive to demo calls with completely wrong expectations. This is an entity clarity problem, and it is the fastest lever to pull in AI search.
User prompt: "Tell me about [Your Brand]"
[Your Brand] is a project management tool that offers free tier pricing and specializes in team collaboration for small businesses. It was founded in 2019 and is known for its Kanban board features.
5 factual errors in a single response. Your prospects read this before they ever talk to your sales team.
The Damage
Wrong AI Descriptions Cost You Deals Before the Call Starts
This is not a minor annoyance. When ChatGPT or Perplexity describes your product incorrectly, it shapes buyer expectations before your sales team has any opportunity to correct them. The prospect arrives to the demo with a mental model built on wrong information. The call starts from a position of confusion instead of confidence.
Wrong category placement
ChatGPT says you are a "project management tool" when you are actually an analytics platform. Buyers looking for analytics never find you. Buyers looking for project management are disappointed on the call.
Deprecated feature descriptions
AI describes features you removed 18 months ago or a product tier that no longer exists. The prospect expects something you do not offer and the sales call becomes a correction exercise.
Incorrect pricing information
A SaaS founder confirmed ChatGPT was giving prospects wrong pricing, costing them deals before a call even started. The buyer expected one price point and was quoted another.
Competitor confusion
AI conflates your brand with a competitor or describes features that belong to someone else. Your brand identity becomes muddled in the AI model's understanding of your category.
The Root Cause
AI Models Build a Blurry Picture From Inconsistent Signals
ChatGPT and Perplexity do not hallucinate randomly. They build a model of your brand from every signal they can find across the web: your website, review sites, old blog posts, third-party mentions, press releases, directory listings, and cached pages.
When those signals are inconsistent, outdated, or contradictory, the AI model builds a blurry, inaccurate picture. It is not lying about your brand. It is making its best guess from noisy data. And that guess gets served to every buyer who asks.
The technical term for this is poor entity clarity. The AI cannot confidently determine what your product is, what it does, who it serves, and how much it costs because the signals across the web are sending mixed messages.
Outdated content still indexed
Old landing pages, deprecated pricing pages, and archived press releases that describe your product as it was two years ago. AI pulls from these as if they are current.
Inconsistent directory listings
Your G2 profile says one thing, your Capterra listing says another, your LinkedIn description says a third. Every inconsistency adds noise to the AI model.
No structured data on your site
Without JSON-LD schema markup, AI agents have to guess your product category, features, and pricing from unstructured page content. They guess wrong frequently.
Third-party content with wrong information
Old comparison articles, outdated reviews, and cached blog posts that describe your product incorrectly. AI agents treat third-party sources as highly trustworthy.
No llms.txt or AI information page
You have not created an explicit machine-readable page that tells AI agents who you are, what you do, and what information is current. So they guess from everything else.
Common Patterns
What Wrong AI Descriptions Look Like
These are the most common types of AI hallucinations we see affecting B2B SaaS brands. Each one is fixable through entity clarity optimization.
Wrong Category
"[Brand] is a project management tool..."
"[Brand] is a revenue intelligence platform..."
AI places your product in the wrong category entirely. Buyers searching for your actual category never find you in AI recommendations.
Deprecated Features
"[Brand] offers a free tier with up to 5 users..."
"[Brand] starts at $49/month for teams..."
AI describes features or pricing tiers you removed months ago. Prospects arrive expecting something you no longer offer.
Competitor Conflation
"[Brand] is similar to [Competitor] with Kanban boards..."
"[Brand] is an API-first analytics platform..."
AI conflates your brand with a competitor, attributing their features to your product. Your differentiation disappears.
Wrong Audience
"[Brand] is designed for small businesses and freelancers..."
"[Brand] serves mid-market and enterprise SaaS teams..."
AI describes your target audience incorrectly. Enterprise buyers skip you because AI says you are for small businesses.
The Fix
How Entity Clarity Optimization Corrects AI Descriptions
Entity clarity is the fastest lever to pull in AI search. The results are often visible within weeks, not months. Here is the systematic approach we use to fix what AI agents say about your brand.
01
AI Brand Audit
We query ChatGPT, Perplexity, Gemini, and Claude about your brand and document every inaccuracy: wrong category, deprecated features, incorrect pricing, audience mismatch. This becomes the fix list.
02
Signal Source Mapping
We identify exactly where the wrong information is coming from: which web pages, directory listings, cached content, and third-party articles are feeding incorrect data to AI models.
03
JSON-LD and Schema Markup
We implement comprehensive structured data on your site: Organization, Product, SoftwareApplication schema with accurate descriptions, features, pricing, and category signals that AI agents can parse directly.
04
AI Information Page and llms.txt
We create a dedicated /llm-info/ page and llms.txt file that explicitly tells AI agents who you are, what you do, your current features, and your target market. A machine-readable source of truth.
05
Cross-Platform Consistency Audit
We align your brand description, features, and positioning across every web property: G2, Capterra, LinkedIn, Crunchbase, your website, press pages, and all directory listings. Consistency eliminates noise.
06
Third-Party Correction Outreach
We identify outdated comparison articles, old reviews, and cached content with wrong information. Where possible, we get corrections made. Where not, we build new correct content that outweighs the old.
Why This Is the Fastest Win
Entity Clarity Is the Quickest Lever in AI Search
Most AI search optimization takes months to show results. Entity clarity is different. Because you are correcting factual signals rather than building new authority, the changes propagate faster. AI models update their understanding of your brand as they re-crawl corrected sources and encounter consistent new signals.
This is often the first thing we fix in any engagement because it delivers visible improvements quickly and sets the foundation for everything else: citation building, authority growth, and competitive positioning all work better when the AI model has a clear, accurate understanding of your brand.
Typical timeline: Entity clarity fixes begin showing improved AI descriptions within 2 to 6 weeks. Full correction across all AI platforms takes 6 to 12 weeks depending on how many incorrect sources exist.
Fix It Now
Find Out What AI Is Saying About Your Brand
We will query ChatGPT, Perplexity, Gemini, and Claude about your brand and document every inaccuracy. You will see exactly what needs to be corrected and how to fix it.
Cross-LLM brand accuracy audit
Inaccuracy source mapping
Entity clarity fix roadmap
Get Your Free AI Visibility Audit