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(Almost) instant NAICS, UNSPSC, HS code classification (free webapp)

Hacker News

(Almost) instant NAICS, UNSPSC, HS code classification (free webapp)

I've made a very simple website that tells relevant categories (and their codes) from really any input text description. Categories are of some of the most widely-used industry classification standards like NAICS and UNSPSC How does it work? On the backend, it creates multi-hundred dimension vectors (essentially numbers) and compares those vectors with a vector of each category in said standard. Closes are the most relevant semantically - closest to each other in meaning. As a result, you get instantly what could have taken hours for a single product - searching a relevant category manually. Of course, it can make mistakes i.e. the top result is quite often not the best, but because it shows Top 5, Top 10 or even Top 100 categories if you wish, it is still much easier to find the correct category. And, perhaps more importantly, you can be certain that you didn't miss anything in a classification of 1000s of classes and subclasses.

Visit productView on Hacker NewsJul 27, 2025
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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: single, code · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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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.
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36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Correct prediction on native model

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