Embedded conversational AI that personalizes responses on your site

Hacker News

I’ve been building an embeddable AI agent that makes any website interactive and context-aware. It doesn’t just answer questions from your PDFs or docs, it also learns from how visitors interact with your site and adapts its responses or suggestions accordingly. I started this because I noticed that most websites still rely on static FAQs or generic chatbots that treat every visitor the same. That often leads to confusion, lower engagement, and unnecessary support load. This agent runs entirely on your site and: • References your own PDFs, documentation, or content base • Learns from visitor behavior to personalize answers or offers • Works without external API calls or backend dependencies • Can be styled and placed anywhere on the page Early results from SaaS testers: • Trial signups increased by ~25-30% • Support tickets dropped by ~20% • Self-serve setup takes under 2 minutes I’d love feedback on the technical side, how it handles embeddings, personalization logic, or UX flow. Happy to answer any implementation questions.

Visit product View on Hacker NewsOct 20, 2025

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

90%90% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, answers · Missing: mobile apps, ios, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

38%38% 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 · Missing: https docs, excited, just released

37%37% 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, active · Missing: arr, mrr, revenue

27%27% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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