AI-native expert sourcing

Source from the work.
Hire by evidence.

Every shortlist comes with verified expertise, proof of work, and candidates already open to talking.

§ 01 · Method

Search by evidence, not pedigree.

Most agencies start with credentials and keywords. We start with the work — and use AI to find it across the open web.

01
Map

Break the role into signals

We break the role into signals — domain depth, published work, network adjacency, communities — that keyword search misses.

02
Search

Look beyond LinkedIn

We map the open web — papers, repos, technical writing, niche communities. Where experts leave proof of what they know.

03
Verify

Human-vet the shortlist

You don't get a scraped list. We read every signal ourselves, then ship candidates with evidence, fit rationale, and verified interest.

§ 02 · Example search

What you actually get.

A shortlist clear enough to know who's worth a call.

Candidate snapshot
Dr. M. Chen
Mathematical Reasoning Expert
PhD applied math · 8 years research · published on logic & reasoning
Interested
ReadinessReady this week
CompIn range
Why they fit

Recent PhD with published work on chain-of-thought reasoning; already contributed to two RLHF projects.

Evidence

Published papers, eval frameworks on GitHub, math reasoning community engagement.

Next step

Ready for a first conversation this week.

Our note

Strong fit for graduate-level reasoning data and formal logic eval. Less production ML exposure, but project framing won't require it.

§ 03 · Case study

A search firm for roles where normal recruiting breaks.

Not more resumes. Better evidence, faster signal, candidates already open to talking.

The problem 8 months of failed search
The ask 10+ QA automation engineers
The result Delivered in 30 days
The brief

Eight months stalled. One month delivered.

Client needed QA automation engineers across three time zones — fast. We screened 150 globally and moved the strongest into their interview pipeline within a month.

Client response
We spent eight months looking for the right candidates. Staffron found them, and helped us put them through our pipeline within a month.
Head of Recruiting · Series C scale-up
§ 04 · Founders

Two founders. One inbox.

You always talk to the people running the search.

D
Daniel Kovari
Co-founder · Formerly Head of Growth at a YC company
LinkedIn →
B
Bernat Nacsa
Co-founder · Formerly VC and VC-backed founder
LinkedIn →
§ 05 · FAQ

Frequently asked.

Specialized hires for AI training and eval companies — domain experts (PhDs, professionals), ML researchers, and the contractor networks behind RLHF and SFT data. Wherever the strongest candidates aren't visibly on the market.
Flat fee, transparent — roughly 30% less than traditional agencies. No hidden costs or surprise percentages.
Yes. Every shortlisted candidate is vetted for technical depth, relevant work, and readiness to engage.
Typically 1–2 weeks after the intake call. We prioritize speed without sacrificing quality.
*
§ 06 · Send the role

Have a role your current search can't solve?

Send it over. We'll show you how we'd search — and whether we can find the right people.

Send us the role Free search assessment · No commitment