Estimating what users ask ChatGPT about your company

Hacker News

OpenAI, Google and Perplexity don't expose user query analytics. It's difficult to tell if and why your company appears in chat responses. Turns out the only way you find out currently if you're cited at scale is if you reverse engineer the prompts users might be asking about your brand and track ChatGPT/Gemini/etc. responses for them. I've built a pipeline, that: 1. crawls your website and your competition 2. analyzes top search keywords from search engines 3. retrieves similar conversations from WildChat (~1M ChatGPT conversations) 4. generates most likely user prompts for your brand. The result is a set of prompts that users are likely to ask about your company or your products and tracking them shows where LLMs suggest you and where do they reference your competition. Repo: https://github.com/syntropicsignal-ai/ai-visibility-audit I'd love feedback on: - whether this methodology makes sense - alternative datasets to WildChat - better ways to estimate prompt distributions

Visit product View on Hacker NewsJun 24, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, perplexity · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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