I made an iOS app that pulls data about people and companies you meet

Hacker News

Not sure if this is the perfect place for it, but I wanted to share a tool I built. Whenever you meet someone new, you usually have to research who they are and where they work. LinkedIn helps, but info is scattered across multiple sources. I never enjoyed that process, so with the rise of LLMs, I built Crilo: click a button and get the research you need. What it does • Calendar & contact integration (optional): Crilo links to your Google or Outlook calendar via Apple Calendar. • Instant research: Tap an event (or contact), and Crilo finds as much info as it can—no extra input needed. • Context blending: Merges those findings with any notes you’ve already added to the invite. • Quick ideas: Suggests icebreakers and agenda starters so you never blank. • Source transparency: See exactly where each insight came from if you want to dive deeper. Tech & timeline • Built in 3 months as a personal side project • Stack: SwiftUI frontend + Python backend on AWS for data gathering and AI summarization • Model: Freemium—a free trial followed by a month‑to‑month subscription Try it out 1. On your iPhone, go to Settings > Calendar > Accounts and add Google or Outlook (if you haven’t already). 2. Download Crilo from the App Store (no sign‑up or email required): https://apps.apple.com/app/apple-store/id6738512640?pt=12712...

Visit product View on Hacker NewsApr 18, 2025

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3points

Did not reach leaderboard

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

91%91% 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: model, apple, google · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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