Perfectly is an AI-native recruiting agency that automates sourcing, outreach, screening, and qualification. Our agent Paul delivers interview-ready candidates directly to Slack and gives every candidate white-glove treatment to improve close rates. Built by ex-TikTok recommendation MLE for startups that need to hire fast.
Hey Product Hunt! We're the team behind Perfectly, the AI-native recruiting agency.
Our launch today, Paul, replaces the entire recruiting function with one agent that understands candidates deeper to make more reliable matches.
Since starting YC, we've filled 4x faster for top startups like Corgi, Giga, LlamaIndex, Porter, and Mintlify. By removing the human bottleneck, we provide candidates at up to 10x the volume. The amazing thing is, our candidates are 2x more likely to pass interviews.
Paul is an AI-native recruiting agent that treats hiring like a matching engine:
- Voice-to-Stack: Give Paul a 5min voice brief. He captures the "vibe" and technical nuance human recruiters miss.
- Zero-UI Workflow: Paul sources, screens, and nurtures candidates autonomously. Interview-ready talent just drops into your Slack.
- Continuous Calibration: Like a recommendation system, Paul learns from your feedback to sharpen every subsequent match.
We know the struggle of hiring great talent, having conducted 600+ technical interviews as founding ML Scientist at TikTok.
We’re here for the ultimate "stress test" from this community. Ask us anything about the workflow, our ML approach, or how we’re killing the "recruiting tax."
Let’s build (and hire) faster!
— Victor & the Perfectly Team
Sometimes the best candidates are the ones that aren't actively searching right now but could be potentially "poached". Does Perfectly consider these types of candidates as part of its "Outreach" process? Or are the candidates only the ones that are actively searching?
Hey! Can Paul handle hiring for multiple roles at once, or just one at a time?
Framing recruiting as a recommendation engine problem rather than a search problem is a meaningful distinction — the continuous calibration loop from interview feedback is exactly how TikTok's content matching works, and it makes sense applied to hiring. How does Paul handle the cold start problem for a new client with zero historical feedback — does it bootstrap from the voice brief alone, or is there a broader signal it pulls from?
The “recruiting tax” framing hits hard — every founder I know has lost weeks to sourcing, screening, and chasing candidates
Question: at what company size does Paul perform best right now? Curious if the calibration model needs a certain volume of interview feedback before the matches get really sharp, or if even a 3-person team can get value from day one.
Congrats on the launch!
"Voice-to-Stack" sounds like a massive time-saver. Does Paul actually draft the JD based on that 5-minute brief, or just use it for internal search parameters?
