Spok, Our Journey to Building an AI-Powered Marketer

Hacker News

We're a small team of four engineers, and over the past six months, we've been working on something to make digital marketing smarter and more affordable. We've had a lot of fun testing spok out on our respective side hustles, and truly believe that it can help people (even with limited experience like us) make smarter, better, faster marketing decisions. Meet Spok, an AI-powered marketer that combines first-party data, third-API’s, marketing intelligence, and LLMs. A few things we’re proud of: Personalized Onboarding: Analyzes your site to offer tailored marketing strategies. Spok Chat: Get instant marketing advice and feedback. Keyword Discovery: Find and understand search intent with AI-driven insights. Content Strategy: Receive tailored recommendations to craft and distribute engaging content. Check us out at spok.app and let us know your thoughts. We’re eager to hear feedback from the HN community and continuously improve based on your insights.

Visit product View on Hacker NewsAug 21, 2024

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

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

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: ide, io · Missing: https docs, excited, just released

30%30% 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 · Missing: plus, platform, intuitive

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

18%18% 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, smart · 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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