We model your search before we launch it
Real talent pool size, expected response rates, and where you'll need to flex — all before day one.
AI-native recruiting for VC-backed startups
The recruiting firm
founders actually trust.
Run by senior operators who've built the teams you're hiring for. Every candidate we put in front of you is worth the conversation.
AI does the volume. Humans make the judgment call.
Trusted by founders backed by a16z, Sequoia, General Catalyst, and Y Combinator
The Problem
Most agencies hand you a pipeline. We hand you a hire.
The standard motion: source to a generic brief, flood your inbox, hope something sticks. You end up doing the filtering yourself, which defeats the whole point.
How it works
AI for scale.
Senior operators for judgment.
The system handles the volume work
Multi-channel sourcing, automated personalized outreach, full pipeline visibility. Built in-house — no tools to stitch together.
Every candidate gets a senior screen
A phone or video screen with someone who's actually built the team you're hiring for. The judgment call gets made by someone who's made it before.
The Alloyed platform
AI, data, and human process in one operating system.
Our platform maps the market, runs multi-channel outreach, and shows exactly where candidates are moving. Then senior operators use that signal to decide who is actually worth your time.
Total market mapped
412enriched candidates
Highest performing channel
60%LinkedIn outreach
Founder-ready
20approved candidates
Live search command center
Every source, stage, and conversion visible.
See where the market is responding, where the funnel is leaking, and what the team should change next.
Candidate funnel
| Stage | Count | ||
|---|---|---|---|
| All candidates | 412 | | |
| Disqualified | 4 | | 1% |
| Email outreach | 152 | | 37% |
| LinkedIn outreach | 248 | | 60% |
| Declined | 27 | | 11% |
| Approved | 20 | | 8% |
LinkedIn outreach
| Stage | Count | ||
|---|---|---|---|
| Queued | 160 | | |
| Invited | 248 | | |
| Messaged | 59 | | 24% |
| Declined | 27 | | 11% |
| Approved | 20 | | 8% |
Email outreach
| Stage | Count | ||
|---|---|---|---|
| Leads | 152 | | |
| Sent | 138 | | 91% |
| Replied | 19 | | 13% |
| Declined | 7 | | 5% |
| Approved | 4 | | 3% |
AI finds the surface area
Map more of the market without lowering the bar
The system enriches profiles, scores fit, and launches personalized outreach across the right channels.
Data shows the truth
Know what is working before opinions take over
Funnel data makes response rates, channel performance, and approval rates visible from day one.
Humans make the call
Senior operators turn signal into judgment
Our team reviews the signal, screens candidates, and only advances people who are worth a founder conversation.
Candidate intelligence, built in
Every candidate is scored against your job, not a generic profile.
We combine enriched candidate data, job-specific criteria, and AI scoring to show why someone fits — and where they may not. Senior operators review the evidence before anyone reaches your inbox.
Job-specific AI score
Out of 100 for this exact role, company stage, location, tech stack, and must-have constraints.
Fit score
90Evidence-backed recommendations
Each criterion includes the profile signal behind the recommendation, not a black-box ranking.
Criteria
7Operator-calibrated judgment
AI ranks the profile, then a senior Talent Strategist checks fit, gaps, and readiness before handoff.
Reviewed
1:1 Illustrative candidate scorecard
AI-generated evidence, reviewed by an operator.
A score is only useful if you can see the reasoning. Each recommendation includes strengths, gaps, criteria weights, and evidence pulled from the candidate profile.
90
/ 100
Senior Backend Engineer NYC Startup fit
NYC-based CS graduate with strong distributed systems leadership and a current small-startup role, with partial AI-agent evidence to validate in screen.
Strengths
Deep distributed systems work — S3-compatible storage, RocksDB, Mongo-c-driver, 95% latency reduction
Clear leadership trajectory — Software Engineer → Team Lead → Solutions Architect
Stage-relevant startup signal — Current founding engineer at a 2-10 person AI startup
Gaps to verify
AI-agent evidence is partial — LLM/GEO product context, but no explicit agent framework listed
Frontend stack needs validation — Strong backend profile; React/TypeScript evidence is light
Earlier startup exposure is recent — Most prior tenure was at larger infrastructure companies
Scoring breakdown
| Criterion | Fit | Score | Evidence |
|---|---|---|---|
| High-growth startup background | +35 | Founding engineer at Topify.ai, a 2-10 person AI startup | |
| Relevant technical depth | +20 | Distributed systems, storage engines, C++, Python, MongoDB | |
| Computer science foundation | +15 | CS degree plus NYU MEng Computer Engineering | |
| Technical curiosity / side projects | +10 | Plog rescue/parser tooling and custom storage systems | |
| AI agent building experience | +0 | AI product context present; no explicit agent implementation found | |
| NYC location Hard | +0 | New York, NY | |
| Not at excluded companies Hard | +0 | Current employer is not on the exclusion list | |
| Total | 90 | ||
What founders say
"Every candidate was worth the conversation."
"The amount of recruiting work that came off my plate was unbelievable. I now have a true partner that understands my talent bar."
"I'm involved where I need to be, but can trust the rest is being handled expertly. The quality of talent I speak with is consistently top tier."