The best people aren’t looking.
They’re listening.
Agents map everyone who could do the job, read who is actually open to move, and write from your team’s seats. You approve every message.
Live in an afternoon, working by the morning
No migration, no new pipeline to learn. The agents plug into what you already use and start on the role you already have open.
Connect your tools
Your ATS, your LinkedIn seats, your note-taker. Read-only until you say otherwise, and you pick which tools the agents may call.
Define what great looks like
One call turns your criteria into a scorecard. Every candidate is then measured against it, not against a keyword.
Agents work, you decide
Sourcing, screening and booking run every day. Drafts wait for your approval, and the whole trail stays on the record.
The surfaces your team lives in
The work a recruiter does, done on the record
Source and map talent pools
The whole market for a role, not a keyword list — with a mobility read on every relevant profile, so you see who is actually open before anyone is contacted.
Rediscover people you already have
Agents re-read every past applicant in your ATS, find what changed since, and flag the ones who fit the role you opened today.
Pick up where the agent left off
Step into any conversation and everything the agents learned is already on the candidate. No thread to re-read before your call.
Run screening conversations
Notice period, comp, must-haves — and the technical questions you could not ask yourself. Hiring a data scientist without being one, the agent probes the models they shipped and how they validated them.
Who is open, not just who fits
Tenure, switch cadence, comp gap, relocation history. You decide what counts for this role — the read updates as you change your mind, and nobody is contacted on a guess.
Try it: turn a factor off and watch the read move.
Know who is actually interested
A reply is not a yes. Agents turn each one into the facts that decide your next move — available, open to relocate, wants a time — so you know who to call first and nobody is asked the same question twice.
Every hire makes the next one faster
An agency starts each search from zero. Every reply your agents get is labelled and kept, so the second role in a market is quicker than the first — and the third is quicker again.
13,083 messages sent so far — the same output as two or three recruiting firms, turned into an asset that keeps working after the hire is closed.
Hire from the AI you already talk to
Ask in Claude or ChatGPT, or let it come to you in Slack. The agents read your pool, check who is open, and draft — you approve.

Agents that ask permission, not forgiveness
Automation is only useful if you’d defend it to a candidate. Limits you set, actions on the record.


+1Candidates hear from your team, not an agency
Agents draft; the message goes out from your CEO, VP or hiring manager once you approve it. That’s why good people reply.

Aggregate across 9,242 candidates messaged. The higher figures come from deployments where the founder seat is live and the scorecard is tight.
More capacity, no more headcount
Fewer admin calls, richer context
“We are currently scaling really fast and we need to recruit the best. Umamy enables to do it ourselves and to keep the control on our hirings!”
“We have various needs, and we have tried going through headhunters in the past. We were lacking quality and transparency. With Umamy we get it all back and more.”
“I didn’t have time to review every profile and felt like I was missing out on great people. Umamy scores everyone fairly and surfaces the best candidates for me.”
“We kept interviewing the wrong people and wasting hours on mismatched candidates. Umamy lets us control quality from the start and only meet the right profiles.”



Flat pricing. No success fees.
Run the agents yourself, or hand the roles to our team.
See pricing and what is includedEngineers who have hired. Recruiters who ship software.
We build the agents ourselves and we have filled the roles they work on. That is why they behave like recruiters instead of chatbots.


Two-time founder, exits in 2016 and 2021. Invested in 17 early-stage companies.

Closed €10M+ enterprise deals at Google and Salesforce.

ML engineer, UIUC-trained. Builds systems that ship fast and stay measurable.

Engineer turned product. Ex-Pong.ai, 7 years in NYC, Apple Best App winner.
Give your agents something to do this week
Connect your ATS, write one hiring goal, approve the first messages.