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GraceLitRev (Research Assistant Tool)

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GraceLitRev (Research Assistant Tool)

During my doctoral journey and article writing as a researcher (including systematic literature reviews), reviewing literature by themes was always challenging. So, we developed GraceLitRev, an AI-powered research assistant tool that lets users upload research papers, extracts 28 metadata variables, generates downloadable graphs, and exports data to MS Excel and RIS files. GraceLitRev helps researchers quickly identify gaps in theory, methodology, data analysis, and future studies. Unlike other research tools that search the internet without user control, our tool gives users full control: upload your documents, review extracted data, and generate themes. It doesn’t write for you but saves hours of reading by extracting key information.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
83%83% 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: user · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
33%33% 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
9%9% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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