Rankai – Automated SEO with AI

Hacker News

Hey HN, we are building Rankai ( https://www.rankai.ai ), an AI agent that pushes SEO blogs and code fixes automatically to your site. My teammate previously scaled a product to 4M users via organic traffic and SEO. And we understood the importance of driving SEO traffic for small businesses. But after we talked to a lot of small businesses and startups, we realized that majority are not doing SEO at all. For them, learning and doing SEO weekly is too time-consuming, and no way they'd hire an agency for $4k+. So we tried to automate each step of SEO process in an agentic way using LLM. How it works: 1. Crawl through website pages to understand the business 2. Suggest SEO code fixes based on Google SEO Guide 3. Research target keywords tailored to the business 4. Crafts keyword-optimized SEO content 5. Publishes the code changes and blogs to the website via CMS Getting the blogs written correctly with the right sources and visuals is the hardest part. And tbh the current version isn't ready to be left on auto-pilot, so we recommend doing human reviews and edits before publishing. There's also an editor for you to guide the AI in the outline process. We’re still very early and would love to get your feedback!

Visit product View on Hacker NewsNov 13, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

91%91% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% 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

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.

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

33%33% 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

22%22% 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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