Li

LinkStorm – AI-powered internal link building SEO tool

Hacker News

LinkStorm – AI-powered internal link building SEO tool

I had the vision to build an SEO tool for content publishers that would solve the pain of building internal links. To validate the concept, I developed a basic tool using ElasticSearch and sophisticated search queries to identify link opportunities through content matching. I believe most of my competitors are using a similar approach. This initial tool provided a one-time fee report of potential link opportunities. The tool was very basic, so making a few sales was enough for me to prove the idea, and I proceeded to build a dedicated team and develop its full-fledged version. LinkStorm was recently launched. It employs AI to identify semantically relevant internal link opportunities. It works across all platforms, not just WordPress. There's no need for installations as it crawls the content. It delivers highly relevant opportunities when sufficient content is available. While link opportunities are the main feature, LinkStorm also offers several other reports to improve interlinking, such as link issues and anchor distribution. LinkStorm is my third SEO tool (and SaaS), and it is the culmination of 10 years of making tools for the SEO industry. I’m curious to hear your thoughts or ideas for further improvements.

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: using · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
51%51% 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
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
9%9% 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
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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