Your buyers are no longer starting their research with a Google search. A growing share of B2B purchase journeys now begin with a question typed into ChatGPT, Gemini, Perplexity, or Claude. "What's the best sales intelligence tool for a Series B company?" "Compare Signal B2B vs Apollo." "Which RevOps platforms integrate with HubSpot?" These AI responses - not your homepage, not your G2 reviews, not your ads - are increasingly forming the first impression your prospects have of your company. The problem: most B2B companies have no idea what these AI models are saying about them.
Why What AI Says About You Matters More Than You Think
AI chatbots have become research intermediaries between your brand and your buyers. When a prospect asks ChatGPT to compare CRM tools, they're not clicking through 10 different vendor websites - they're reading a synthesised summary generated by a model trained on everything publicly available about your company: your blog, your G2 reviews, Reddit discussions, press releases, LinkedIn posts, competitor comparisons, and analyst commentary. The model's response isn't neutral. It reflects the weight and sentiment of what's been written about you across the internet.
If most of the indexed content about your company is outdated, sparse, or framed in your competitor's terms, the AI model will describe you in those terms too. And your prospect will form their first opinion accordingly - before they ever visit your site.
The Audit - How to Check What AI Models Say About You
Run this audit across the four major models: ChatGPT (GPT-4o), Google Gemini, Perplexity, and Anthropic Claude. For each, run these five prompt types and record the responses:
- Direct brand query: "What is [Your Company]? What do they do and who is it for?"
- Category comparison: "What are the best [your category] tools for B2B companies?"
- Competitive positioning: "Compare [Your Company] vs [Top Competitor]"
- Use-case query: "What's the best tool for [specific use case you solve]?"
- Sentiment probe: "What are the main complaints or limitations of [Your Company]?"
Document: (a) whether your company appears at all, (b) how it's described and in whose terms, (c) where it ranks in comparison responses, (d) what negatives are surfaced, and (e) which competitors appear alongside or above you.
What to Look For in the Results
Five things to evaluate once you have responses from all four models:
Accuracy. Is the description of your product factually correct? Are outdated product features being cited? Is your pricing described accurately? AI models are trained on historical data - if your product has evolved significantly in the last 12–18 months, older descriptions may persist in model outputs long after you've updated your website.
Framing. Whose language is the model using to describe you? If the dominant narrative about your category was written by a competitor - in their blog posts, their analyst briefings, their G2 battle cards - the model may describe your product in those terms, using their framing rather than yours.
Category placement. Are you named in the right category? An SEO tool that also does content strategy might be described only as an SEO tool, missing half its positioning. Category placement in AI responses directly affects whether you appear in the prompts your buyers are actually asking.
Competitive position. When compared to competitors, are you positioned on the dimensions where you actually win? If you win on integration depth but get compared on pricing, you're being evaluated on a field where you're weaker.
Negative signals. What complaints or limitations does the model surface? These are usually drawn from review sites (G2, Capterra, Reddit), so addressing them on those platforms is the fastest lever for changing what AI says.
How to Influence What Generative AI Says About You
AI models don't have a direct submission process - you can't "submit a correction" the way you can with Google My Business. But you can influence the training and retrieval sources that shape model outputs. The most effective levers:
Publish content that uses your own framing. AI models trained on web content will reflect the dominant language used to describe your category. If you want to be described as a "buyer visibility platform" rather than a "sales intelligence tool," you need to publish enough content using that terminology that it becomes the dominant signal in your category. Blogs, comparison pages, use case guides, and thought leadership all contribute.
Control your review narrative. G2, Capterra, Trustpilot, and Reddit are heavily indexed. A cluster of reviews describing a specific pain point will surface in AI responses describing your limitations. Actively request reviews from happy customers - not to suppress negatives, but to ensure the volume of positive, accurate reviews outweighs the noise.
Get mentioned in third-party publications. AI models weight credibility signals. Being mentioned in industry publications, analyst reports, and high-authority blogs increases the likelihood that a model cites you as a credible solution in category comparison prompts.
Update and expand your comparison pages. When someone asks "X vs Y," the model often synthesises existing comparison content from both vendor sites and third-party sources. Building detailed, accurate comparison pages that present your positioning clearly gives the model better source material than leaving it to reconstruct the comparison from scattered reviews.
Ongoing Monitoring - This Isn't a One-Time Audit
AI model training is continuous. GPT-4o, Gemini, and Perplexity are updated regularly. What the model says about you today may reflect content published 6–12 months ago; what it says in six months will reflect what's being published now. Build a quarterly cadence: run the same five prompt types across all four models, compare to your previous audit, and identify what's drifted. The companies that will own the AI-era brand narrative are the ones actively managing it now - before their competitors realise it matters.
Know When Buyers Are Researching You - Before They Ask an AI
Signal monitors buyer intent signals across the web - including third-party research activity that indicates someone in your ICP is actively evaluating your category. When a prospect starts researching AI tools to compare your competitors, Signal fires a signal so your team can engage before the decision is made.
Book a Demo → See PricingFrequently Asked Questions
How do I find out what ChatGPT says about my company?
The simplest approach is to open a fresh ChatGPT session (log out or use an incognito window to avoid personalisation from prior conversations) and run a series of structured prompts: ask directly what your company does, ask for a comparison between your company and a top competitor, ask for the best tools in your category, and ask about common complaints or limitations of your product. Record the responses verbatim. Repeat this process across GPT-4o, Google Gemini, Perplexity, and Claude to get a cross-model view of how your brand is represented. Variations between models reveal which source content is influencing each platform most heavily.
Can I submit corrections if an AI chatbot describes my company inaccurately?
There's no direct correction submission process for most AI models (though some, like Perplexity, allow you to flag specific responses). The most effective approach is to publish high-quality, accurate content on your own site and on third-party platforms that models index - G2, Trustpilot, industry publications, analyst sites. As models retrain on updated web data, accurate and well-sourced content gradually replaces outdated descriptions. For urgent inaccuracies, contacting the model provider directly is an option, but publishing corrective content at scale is typically faster and more durable.
How often should I audit what AI chatbots say about my company?
A quarterly audit cadence is a reasonable starting point for most B2B companies - run the same set of structured prompts across ChatGPT, Gemini, Perplexity, and Claude every three months and compare results. Companies in fast-moving categories (AI tools, security software, marketing technology) should consider a monthly cadence, as new competitor content, review volume changes, and model updates can shift AI brand narratives quickly. The most important thing is consistency: use the same prompts each time so you're comparing like with like.
Does getting more G2 reviews actually change what AI chatbots say about me?
Yes - significantly. G2, Capterra, and Trustpilot are among the most heavily indexed review sources for AI models building responses about software products. The volume, recency, and sentiment of your reviews on these platforms directly influences how AI describes your product's strengths and limitations. Teams that actively manage their review pipeline - requesting reviews from satisfied customers after key milestones - consistently see more positive AI brand representations than teams that leave reviews to chance. It's one of the highest-leverage actions you can take to improve your AI brand narrative.