AI Cold Email for Recruiting Agencies
Founder of Link AI. Writes about voice agents, cold email, and the operational reality of running them in Hebrew and English.
Recruiting outreach has two failure modes: candidate emails that look like spam, and client emails that look like a sales pitch. Mailer fixes both by reading the actual context — a candidate's recent project, a client company's recent hiring patterns — and writing the email around that fact. Recruiters using Mailer typically book 3-5x more candidate intros and 2-3x more client meetings per week than they did with template-based sequences.
Why recruiting outreach is broken
Candidates get 30+ LinkedIn messages per week, most of which are clearly templated. The ones that get replies reference something specific — a project the candidate shipped, a paper they co-authored, a talk they gave. Recruiters know this but don't have time to write 40 emails like that per day.
What Mailer does for recruiting
Mailer takes a role description and an ideal-candidate profile, finds real matches via LinkedIn and GitHub, enriches each with their recent work (commits, papers, talks, projects), and writes a per-candidate email that references something concrete and connects to the role. For client outreach the same engine reads the company's recent hiring signals (job postings, headcount growth, leadership moves) and writes a per-client pitch.
- Candidate enrichment from LinkedIn, GitHub, papers, talks
- Per-candidate emails that reference real work, not generic flattery
- Client outreach using hiring signal data
- Two-touch follow-up cadence
- Hebrew and English support
- Reply routing to the recruiter
How Mailer writes a candidate email
Example: a senior backend role at a Series-B fintech. Mailer finds 50 matches, reads each candidate's recent GitHub activity, identifies that one of them maintains an open-source library on the same primitive the hiring company uses, and writes the email around that. The candidate replies because the message is clearly not a blast — even if it's algorithmic, it's reading something they did.
ROI math for a recruiting agency
A 4-recruiter boutique agency typically sends 200-400 outreach messages per recruiter per week. Template quality lands a 4-7% reply rate, ~20 conversations per recruiter per week, ~3 candidate intros. Mailer's per-lead approach lifts replies to 12-18%, ~50 conversations, 10-15 intros — and similar lift on the client side.
Frequently asked questions
Does Mailer scrape LinkedIn?
Mailer uses LinkedIn's public data through approved providers. We do not run scrapers against LinkedIn directly.
Can it read GitHub profiles?
Yes. GitHub public data is part of the enrichment layer — recent commits, owned repos, contributed projects.
What about candidates without a public profile?
If we can't find enough signal to write a real personal email, Mailer flags the lead for manual handling rather than writing a generic email.
Does it work for client outreach too?
Yes. The same engine reads company hiring signals (job postings, funding, headcount) and writes per-client pitches.