I built an AI Bureaucratic Advisor for U.S.

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Hi HN, Over the past eight months, I've been working on an AI-powered bureaucratic advisor called "Clerkly" during my spare time. It's designed to help users navigate nearly all bureaucratic issues in the U.S., making it easier to deal with government processes. The aim is to make this technology accessible to a wide audience and empower citizens in their interactions with various government agencies. What Can Clerkly Do? Clerkly is knowledgeable about U.S. regulations, data from government websites, and much more. Whether you need help with tax accounting, understanding public benefits, or other bureaucratic hurdles, Clerkly aims to assist you. Technical Details: *How Does It Work?* The service utilize forrest of agent LLMs, reranking models, and graph databases to offer solutions that are both affordable and user-friendly. *Data Sources* All data is open-source, primarily acquired through the U.S. Open Data initiative at data.gov. I would love for you to check it out, give it a try, and share your feedback. Your insights will be invaluable in pushing this project forward and ensuring it meets the needs of everyday users dealing with bureaucratic challenges. Thanks for your time and attention!

Visit product View on Hacker NewsAug 16, 2024

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best fitHighest predicted score across all platforms for this description.

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

88%88% 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: lua, ide, io · Missing: https docs, excited, just released

44%44% 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 · Strong signals: friendly, users · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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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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