A People Search Engine with Face Recognition

Hacker News

Hey there HN! I'm Vignesh, and I'm excited to launch Introthem.com — a people search engine that uses facial recognition to provide in-depth, accurate summaries of individuals, assist with HR screening, research prospects, and analyze brands. The Problem: Researching individuals - whether for hiring or personalizing outreach - is a time-consuming challenge. While RAG-based search engines can help summarize someone's online presence, they have significant limitations. When multiple people share the same name, these engines often mix their information together, creating inaccurate profiles. Even worse, if someone shares a name with a celebrity or public figure, meaningful research becomes nearly impossible as the well-known person's results overshadow everything else. The Solution: Introthem solves this using facial recognition to accurately classify and organize information by individual. Simply select the specific person you're interested in, and our engine will generate a comprehensive profile. But that's not all – remember how we typically perform multiple queries to look up someone? For example, if a person founded a company, we then look up how that company is doing. Introthem handles this in-depth research automatically. It generates additional queries based on the first summary the engine produces – what I internally call Content-aware query generation. This helps you conduct thorough research about someone just by their name. Try it now at https://introthem.com Would love to hear your feedback, HN! Demo: Link 1: https://introthem.com/search?uuid=51d6bc6a-08ad-464e-b4f1-16... Link 2: https://introthem.com/search?uuid=9f3ad850-1c72-4e8c-ad36-07... Link 3: https://introthem.com/search?uuid=3f31072e-bf74-4ff2-b1ef-ee...

Visit product View on Hacker NewsNov 20, 2024

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

87%87% 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

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

53%53% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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.

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