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InterviewFlowAI – AI phone and Meet interviews for fast screening

Hacker News

InterviewFlowAI – AI phone and Meet interviews for fast screening

Hi HN, I’ve been working on InterviewFlowAI, a tool that automates the first-round hiring workflow for teams that spend too much time on initial screening. It handles resume scoring, public job links, candidate applications, and full interviews conducted over phone or Google Meet. I built this after spending years interviewing candidates as a Head of Engineering and realizing that most of the bottleneck happens before an engineer ever gets involved. Too many resumes, too many unqualified applicants, and a ton of repetitive phone screens that rarely lead anywhere. Here’s what the system does today: • Generates a public job link so candidates can apply directly • Scores resumes based on the job description and required skills • Lets you accept or reject candidates instantly • Runs a live interview (phone or Google Meet) using an AI agent • Produces a structured scorecard, transcript, and recording Technical details for those interested: • Uses OpenAI real-time API for conversation flow • Voice handling and telephony via Vapi • Speech → text → scoring pipelines using AssemblyAI and custom logic • Resume scoring uses embeddings + rule-based signals to reduce LLM hallucination • All interviews are stateless interactions stored securely for evaluation Pricing is $0.50 per interview, so teams can screen at scale without high per-candidate costs. I’d love feedback from the HN community, especially around accuracy, bias concerns, architecture, and scaling. Happy to answer any questions about design decisions or what’s still rough.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, google, openai · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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 · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, pipe, io · Missing: https docs, excited, just released
39%39% 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
39%39% 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
27%27% 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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