HelpHub – GPT chatbot for any site

Hacker News

Hi HN, I’m the founder of a SaaS platform called CommandBar (YC S20). We’ve been mucking around with AI-related side quests for a while, but recently got excited enough about one to test it with some customers. Results were surprisingly good so we decided to launch it. HelpHub is AI chat + semantic search for any website or web app. You can add source content in 3 ways: -Crawling any public site via a URL (e.g. your marketing site or blog) -Syncing with a CMS (like Zendesk or Intercom) -Add content manually The chatbot is then “trained” on that content and will answer question’s based on that content only, not referencing directly the knowledge. The output is an embeddable widget the contains two things: the chatbot interface for user’s to ask questions, and a search interface for users to search through the content the bot is trained on directly (as well as view source content). You can play around with a demo on some popular sites here: https://helphub.commandbar.com Some features we added that make it better IMO than just chat: -Suggested questions (based on the page the user is on and their chat history) -Suggested follow-up questions in a chat response -Ask a question about a specific doc -Recommend content based on who the user is and where they are Would love to hear feedback (not lost on me that there are other chatgpt-for-your-site products and we are probably missing a ton of functionality from there) and can also share details about how we built this. It’s not rocket science but does feel magic :) -James

Visit product View on Hacker NewsMay 24, 2023

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%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: excited, ide, io · Missing: https docs, just released, exist

43%43% 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 · Strong signals: saas · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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