We

We built pitchpilot, an AI synthesizer to tailor presentations

Hacker News

We built pitchpilot, an AI synthesizer to tailor presentations

Hey HN, We’re Nick and Roman. We’re solutions engineers who spend a lot of time prepping for and delivering product presentations and demos. We realized that we were spending too much time tailoring scripts for our client presentations and sales demos, practicing our pitch and (sometimes) nailing our delivery. So we built pitchpilot to save time customizing the pitch and adding the right voice to it. You can also download and share your presentation in no time. We hope this tool will be as useful for you as it is for us. Let us know what you (don’t) like and how you use it for work, fun, etc. Cheers, Team pitchpilot

Visit product View on Hacker NewsJul 17, 2024

Actual performance

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Launch Intel predictions

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

87%87% 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 · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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