Lightfield
Product HuntAI-native CRM that builds itself and does work for you
Lightfield reads your emails, meetings, and calls to build your CRM automatically. No manual data entry — ever. Connect your inbox, upload a spreadsheet or CSV from your old CRM, and everything is recreated in less than five minutes. Ask it anything in plain English: who needs follow-up, what objections keep coming up, how has our ICP shifted — answered from your actual conversations. Then put it to work to draft follow-ups, create board decks, build proposals, and more.
Share cardActual performance
902upvotes
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Made the leaderboard
Traction signals
Makers1
Launch Intel predictions
Analyze your own launch →Product HuntOn track for Day 1 leaderboard · Strong signals: inbox, email, plain · Missing: mac, agents, macos89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive46%46% 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, personal44%44% 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 released43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue27%27% 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, crypto20%20% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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