LLM Insight
Track & optimize how AI search engines talk about your brand
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三宅晋太郎

LLM Insight - Track & optimize how AI search engines talk about your brand

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LLM Insight monitors how ChatGPT, Gemini, Perplexity, and Google AI Overviews mention your brand. Track citation rates, analyze source domains AI pulls from, and get actionable recommendations to improve your visibility in the new AI search landscape.

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三宅晋太郎
Maker
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Hey Product Hunt! 👋 I'm the founder of LLM Insight, and I built this because of a pattern I kept seeing with clients: their SEO rankings were stable, but organic traffic was quietly dropping. The culprit? AI search. ChatGPT, Gemini, Perplexity, and Google AI Overviews now answer user queries directly — often without sending a single click to the websites they pull information from. For brands that depend on organic search, this is a slow-moving crisis that most analytics dashboards can't even detect. 【The core problem】 When someone asks ChatGPT "best project management tool" or Perplexity "top CRM for startups," your brand is either mentioned — or it's not. And right now, most companies have zero visibility into where they stand. 【What LLM Insight does】 📊 AI Brand Monitoring — Track how often (and how) your brand gets mentioned across ChatGPT, Gemini, Perplexity, and Google AI Overviews. See mention rates, ranking positions, and sentiment in real time. 🔍 Citation Source Analysis — Discover exactly which domains and pages AI models are pulling from when generating answers in your category. Understand why competitors get cited and you don't. 📈 Organic Impact Measurement — Quantify the traffic you're losing to AI-generated answers. Put a number on the opportunity cost so you can justify action to your team. 🎯 GEO Recommendations — Get specific, actionable steps to improve your AI visibility. Not generic SEO advice — targeted optimizations for how LLMs retrieve and rank information. 【Why now?】 We're at the beginning of a massive shift. "Generative Engine Optimization" (GEO) is becoming as important as SEO was 15 years ago. The brands that move first will have a compounding advantage — AI models tend to reinforce existing citation patterns. We're live at https://llm-insight.com/en/ — would love your feedback! What questions do you have about AI search visibility? Happy to answer anything here.
Naveed Adam

Congratulations on the launch!

A lot of teams are still celebrating stable SEO rankings while traffic quietly declines, and AI answers are probably a big part of that. I like that LLM Insight focuses not just on mentions, but also on citation sources and actual organic impact. That makes it easier to justify action internally.I'm curious how you handle variability in AI responses. Since models like ChatGPT and Gemini can generate different answers for similar prompts, do you run multiple prompt variations to calculate mention rates more reliably?

三宅晋太郎
Maker

@naveed_ratansi 

Great question — and honestly, this is one of the hardest parts of building in this space.

You're right that AI responses are non-deterministic. The same query can produce different answers depending on timing, phrasing, and even session context. A single snapshot tells you almost nothing.

Our approach has a few layers:

  1. Multiple prompt variations per query category. For each topic we monitor, we don't just run one prompt — we test a range of natural phrasing patterns (e.g., "best CRM for startups," "top CRM tools," "which CRM should I use," etc.). This gives us a more realistic picture of how real users would encounter your brand.

  2. Repeated sampling over time. We run these queries on a regular cadence, not as one-off checks. This lets us calculate mention rates as a statistical trend rather than a single data point. A brand showing up in 3 out of 10 queries today vs. 6 out of 10 next month — that's a meaningful signal.

  3. Cross-model comparison. Since each AI model (ChatGPT, Gemini, Perplexity, Google AI Overviews) has different retrieval mechanisms and training data, we track each separately. A brand might be well-cited on Perplexity (which pulls live sources)

but invisible on ChatGPT. That gap itself is actionable insight.

Happy to dive deeper into the methodology if you're interested!