Perfectly
The first AI-native recruiting agency. Fill roles in days.
225 followers
The first AI-native recruiting agency. Fill roles in days.
225 followers
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.
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Launch tags:Hiring
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@victor_luo Congrats on the launch! I really appreciate the Voice-to-Stack feature! I’m curious about this handles roles where the signal is contextual — like early founding engineer hires where culture fit and trajectory matter as much as the tech stack? Does the continuous calibration loop handle that nuance over time, or is there a human-in-the-loop moment there?
@jerrybyday Great question, Jeremiah. For those early founding hires, culture and "trajectory" are often the silent requirements that matter most.
You're spot on about the weighting. During the voice intake, Paul actually uses "comparison cards" to help you map out those specific trade-offs (e.g., culture fit vs. deep technical specialization).
Instead of just sitting in a database, those nuances are baked into the screening process. Paul turns those cultural preferences into targeted questions for candidates to ensure their goals and "vibe" actually align with the founding team's vision. It’s less about a human-in-the-loop and more about Paul acting as a mirror to your specific hiring philosophy.
@victor_luo Hey, congrats on this to you and your team! I have one question tho; how does Perfectly handle niche roles like sales leaders for B2B startups; does it capture founder-specific "vibes" from voice briefs as well as it does technical stacks, and what's one quick calibration hack for first-time users?
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?
@svyat_dvoretski Hi Sviatoslav,
That’s a great observation! You hit the nail on the head regarding the TikTok comparison, which is the core of the product is indeed that continuous calibration loop.
As for cold-start problem:
Deep Alignment: You’re spot on. By leveraging LLMs' internal knowledge, Perfectly can effectively bootstrap from the job description and the hiring manager’s voice brief alone.
Broader Signals: Beyond the brief, the agent pulls from extensive web data and market signals to establish a high-quality baseline before the first piece of feedback from hiring manager even hits the system.
Snippets AI is cool btw!
"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?
@jinhao_bai2 Hey Jinhao, good catch.Actually we use the voice brief and the JD as the primary sourcing "soul," but we also layer in web data to enrich the profile based on what your company actually does.
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!
@jacklyn_i Great question, Jacklyn. The short answer is: a 3-person team can absolutely get value from day one.
We actually built Paul to be "cold-start" native. Because we combine the JD and your voice brief with extensive web data and the LLM's own internal knowledge, Paul doesn't need a mountain of historical data to understand what you're looking for. It hits a high-quality baseline almost immediately.
It also helps that my co-founder, @huimin_xie is an ex-TikTok MLE who specifically specialized in solving the cold-start problem for recommendation engines. We’ve baked that expertise directly into how Paul calibrates with very little feedback.
The ex-TikTok recommendation MLE background shows in the matching approach - treating hiring like a recommendation engine rather than keyword matching is exactly the shift needed. The 10x volume with 2x interview pass rate suggests strong precision-recall balance. Curious about the voice brief capture - how do you handle implicit cultural fit signals that founders often struggle to articulate? Also interested in the feedback loop mechanism that helps Paul calibrate to each company's hiring bar.
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?