SaaS that turns your idea into a perfect 5-minute pitch

Hacker News

Hi Hacker News, We’ve built Pitch Forge, an AI-powered SaaS tool designed to help startups create investor-ready pitch decks in minutes. How it works: 1. Input your startup’s name, description, target audience (buyer persona), and desired pitch length (1, 3, 5, or 10 minutes). 2. Pitch Forge generates: A professionally written script tailored to investors. A PPT deck designed for clarity and impact. An audio pitch of the script with emphasis on key points to help you practice or even use directly. Why we built this: Creating a pitch deck is stressful, especially for early-stage founders who may lack experience or design skills. We wanted to make the process fast, effective, and accessible for everyone. What’s next? We’re currently validating demand and gathering feedback. Sign up for early access on our waitlist: https://pitch-forge.com . If you're a founder, investor, or just curious, we’d love to hear your thoughts! How can we make this more helpful for you? Thank you!

Visit product View on Hacker NewsJan 18, 2025

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% 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

26%26% 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

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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