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I scanned 35 SaaS products across ChatGPT, Claude, Perplexity, Gemini

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I scanned 35 SaaS products across ChatGPT, Claude, Perplexity, Gemini

I built a scoring system to measure how AI models represent software products when users ask buying questions. The process: I take a product, generate the queries a buyer would ask (category, competitor alternatives, head-to-head), run them through ChatGPT, Claude, Perplexity, and Gemini, then score how prominently the product appears in each response (0-10). Some findings from scanning 35 products: ChatGPT is the biggest blind spot. It scores 0 for most open source challengers, even ones with 30K+ GitHub stars. Incumbents dominate across all models. "Best project management tool" returns Jira, Linear, Asana — never Plane (31K stars on GitHub). Brand-name queries work. "Cal.com vs Calendly" scores 9+ everywhere. But generic category queries ("best scheduling tool") often return 0. Having revenue doesn't help. Trigger.dev raised $16M and scores 0/10 for background job queries. The scoring methodology: each model gets 0-10 per query based on mention position, detail, and recommendation strength. Product consensus = average across all models and queries. Code and methodology are open. Happy to scan any product if you drop it in the comments.

Visit productView on Hacker NewsFeb 25, 2026
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos
96%96% 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
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
26%26% 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 · Strong signals: revenue, saas · Missing: arr, mrr, profit
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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