AI Teammate for Recruiters – Automates Job Posts, ATS, Emails and CRM

Hacker News

Hi HN, We’ve been working on an AI-powered recruiting assistant — originally built to conduct structured video interviews. But it evolved into something much more. Now it acts like a true AI teammate for recruiters, automating the entire top-of-funnel workflow: Here’s what it does: Posts jobs Collects and enriches candidate data Conducts contextual AI video interviews Shortlists candidates based on interview + resume signals Sends personalized emails and replies Manages the CRM pipeline end-to-end Recruiters waste hours switching between tools just to manage basic tasks. We built an AI teammate that automates job posts, interviews, follow-ups, and CRM actions — without depending on messy integrations. Live demo (YouTube): https://youtu.be/NMZkM_10QLw Request early access here: https://docs.google.com/forms/d/e/1FAIpQLSfsnJqmnMd-RBzxybsr... We’d love feedback — on the tech, UX, or use cases you think we should support next. Thanks!

Visit product View on Hacker NewsMay 19, 2025

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

85%85% 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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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, pipe, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · 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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