Urpal – An AI-powered productivity canvas for file notes and tasks

Hacker News

Hi HN, Built URPAL – an AI assistant that converts emails and call recordings into professional file notes for legal/consulting work. *Tech:* React + Node.js + PostgreSQL + OpenAI GPT-4o + Stripe *Key innovation:* Smart detection of communication direction (did you send/receive the email? make/receive the call?) using pattern matching, not just AI guessing. *Live at:* https://urpal.com.au (14-day free trial, then AUD $15/month) The trickiest part was building bulletproof logic for email/call direction detection – turns out simple rules work better than complex AI analysis for this specific problem. Currently processing real client communications for Australian insurance professionals. Would appreciate feedback on the technical approach and whether this solves a real pain point for other documentation-heavy professions.

Visit product View on Hacker NewsJun 23, 2025

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

77%77% 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: stripe, email, recordings · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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