Seofor.ai – Track what all AI models say about your brand on one page

Hacker News

Hi HN! I'm Silver and i built SEO for AI ( https://seofor.ai ). It helps you track on one page what all the different LLM's out there say about your brand, company or product. I realized with all the LLM's out there that we won't be googling much anymore. Most of the answers to even our product questions will likely come from LLM's. It's super annoying to go search your company on like 5-6 sites (chatgpt, bing ai, bard etc.) and even more annoying to do it regularly, so I made it super easy by putting it all on one site and automating the detection of changes in answers. I initially focused on the personal data angle and people "googling themselves" and built HIBE ( https://haveibeenencoded.com ) and posted that on Hacker News as well: ( https://news.ycombinator.com/item?id=35557548 ) but obviously the usecase for businesses for tracking brands seems more important given how people will likely learn about your product from AI chatbots in the future. I think the honest answer is that no one knows right now how this LLM wave will change the search landscape and the only obvious part is that it certainly WILL change search and it's time to start tracking and keeping an eye on what the AI's are saying about your brand and that's why I built this baby. It's FREE to use for one company, but if you're serious about tracking multiple keywords and companies, then I obviously want your help with my openai api bills. :) Hope you like it and let me know what would be cool to add in this early version?

Visit product View on Hacker NewsJun 9, 2023

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

88%88% 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: model, new, models · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, 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.

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

25%25% 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

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.

Correct prediction on native model

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