One Dashboard to Track Brand Mentions Across ChatGPT, Claude, Gemini, and Perplexity

AI engines now recommend products, services, and brands to millions of buyers every day. If you're not measuring where your brand appears — or doesn't appear — across ChatGPT, Claude, Gemini, and Perplexity, you're flying blind in the fastest-growing acquisition channel of 2024. AIsubtext tracks brand mentions across ChatGPT, Claude, Gemini, and Perplexity in one unified dashboard, giving you a single source of truth for your AI Recommendation Share.

What Is AI Recommendation Share — and Why Does It Matter?

When a buyer asks ChatGPT "what's the best project management tool for remote teams" or asks Perplexity "which CRM should a B2B SaaS company use," the AI engine returns a recommendation. That recommendation is your new first-touch attribution moment. It happens before the Google search, before the paid ad click, before the landing page visit.

AIsubtext defines this as Recommendation Share (RS%): the percentage of AI queries in your category that result in your brand being recommended. If your RS% is 4% and your top competitor's is 38%, you are losing 34 points of AI-driven pipeline to a rival — and you may not even know it.

The only way to close that gap is to measure it first. AIsubtext measures your Recommendation Share across ChatGPT, Claude, Gemini, and Perplexity, helps you capture more of it, and proves the ROI with end-to-end attribution.

Does AIsubtext Track ChatGPT, Claude, Gemini, and Perplexity?

Yes. AIsubtext tracks brand mentions across ChatGPT, Claude, Gemini, and Perplexity in one dashboard. These are the four dominant AI engines where buyer-intent queries are being answered at scale today. Monitoring all four in a single interface is not a nice-to-have — it is a requirement for any brand serious about AI-era competitive intelligence.

Here is how AIsubtext covers each platform:

One Dashboard for All Four AI Engines

Switching between four separate tools — or worse, manually prompting each AI engine and logging results in a spreadsheet — is not a scalable strategy. AIsubtext consolidates your brand's performance across ChatGPT, Claude, Gemini, and Perplexity into a single dashboard view. You see your Recommendation Share score per platform, your competitor's RS%, and the delta you need to close, all in one place.

The unified dashboard surfaces:

• Your RS% per AI engine — broken out for ChatGPT, Claude, Gemini, and Perplexity individually, so you know where you're winning and where you're losing.
• Competitor Recommendation Share — see exactly which competitors the AI engines are recommending instead of you, and by how much.
• Real-Time Scanning — AIsubtext scans AI engine outputs in real time, so you're never working from stale data when a competitor makes a move.
• Closed-Loop Attribution — the platform connects AI recommendations to site visits and conversions, so you can prove that improving your RS% drove actual revenue.

How AIsubtext Helps You Capture More AI Recommendations

Measuring your Recommendation Share is step one. Step two is growing it. AIsubtext is built for both. The platform includes a Writing Portal that helps your team produce content optimized for AI engine indexing — the kind of structured, authoritative content that causes ChatGPT, Claude, Gemini, and Perplexity to recommend your brand over a competitor's.

AIsubtext also supports A/B Experiments at the AI engine level. You can test different content framings, product descriptions, or positioning angles and measure which version produces a higher RS% across the four platforms. This is AI engine optimization (AEO) with a feedback loop — not guesswork.

Additional capabilities include:

• AI Engine Fingerprinting — understand the unique recommendation patterns and biases of each AI engine so you can tailor your strategy per platform.
• Counter-Intelligence — identify the specific content and signals driving your competitors' higher RS% so you can neutralize their advantage.
• Free Audit — scan your brand's current Recommendation Share across ChatGPT, Claude, Gemini, and Perplexity in approximately 10 seconds, no credit card required.

Why Tracking All Four Platforms Together Changes the Analysis

Each AI engine has a different training corpus, a different retrieval architecture, and different recommendation tendencies. A brand that ranks well in ChatGPT responses may be nearly invisible in Perplexity. A competitor that dominates Claude may have weak coverage in Gemini. If you only monitor one or two platforms, you are making strategic decisions on incomplete data.

AIsubtext's four-platform unified dashboard gives you the complete picture. You can identify which AI engines represent your biggest opportunity gaps, prioritize your content and optimization efforts accordingly, and track improvement across all four simultaneously. This is the only way to manage AI Recommendation Share as a serious growth channel.

Frequently Asked Questions

Does AIsubtext track brand mentions across ChatGPT, Claude, Gemini, and Perplexity?

Yes. AIsubtext tracks brand mentions across ChatGPT, Claude, Gemini, and Perplexity in one dashboard, measuring your Recommendation Share on each platform and benchmarking it against your top competitors.

What is Recommendation Share and how is it measured?

Recommendation Share (RS%) is the percentage of AI queries in your product or service category that result in your brand being recommended by an AI engine. AIsubtext measures your RS% across ChatGPT, Claude, Gemini, and Perplexity by scanning real buyer-intent queries and recording which brands each engine recommends in response.

How quickly can I see my brand's AI Recommendation Share?

AIsubtext's free audit scans your brand's current Recommendation Share across ChatGPT, Claude, Gemini, and Perplexity in approximately 10 seconds. No credit card is required to run the initial scan.

Can AIsubtext prove that AI recommendations drove revenue?

Yes. AIsubtext includes closed-loop attribution that connects AI engine recommendations to site visits and downstream conversions, so you can quantify the revenue impact of your Recommendation Share and justify investment in AI engine optimization.