Hiring Expertise
Clients Served
Candidates Hired
Fit Accuracy (FEMQ™️)
A FAANG resume tells you nothing about how someone performs when there's no playbook, no manager, and no safety net.
Leetcode evaluates memory and pattern-matching not the ownership, speed, or product intuition that drives early-stage growth.
Engineers optimized for process and committee decisions will stall your product at the exact moment you need acceleration.
First 5 engineers shape culture, architecture, and trajectory. One wrong hire isn't a setback it's an existential event.
Not a personality test. Not a vibe check. A precision instrument built from 200+ founding-engineer archetypes.
94
Initiative, self-direction, and follow-through without oversight.
88
Speed to ship, ability to de-scope, comfort with imperfect-but-live.
79
Questions requirements, asks about user outcomes, cares beyond the ticket.
85
Response to pivots, ambiguity, and resource constraints.
91
Fluency with agentic AI workflows, RAG, and AI-augmented development.
82
Recovery from setbacks, persistence under pressure, antifragile mindset.
78
Async-first clarity, writing quality, cross-functional alignment.
88
Shared operating style, communication cadence, tolerance for chaos.
Deep contextual assessment of working style, risk tolerance, and orientation. Maps candidates onto 12 founding-engineer archetypes.
Scenario-based challenges modeled after real zero-to-one decisions. Evaluates judgment, framing, and prioritization under constraint.
Time-boxed async deliverable that tests shipping instincts, task decomposition, and quality-speed tradeoff philosophy.
Evaluates user-empathy, feature prioritization logic, and ability to synthesize engineering with product outcomes.
Probes actual fluency with AI-native development prompt engineering, agent use, and AI-augmented architecture decisions
AI synthesizes all six layers into a composite score, archetype classification, and investor-grade hiring report in 48 hours.
Engineers who think in agents, ship in hours, and reframe architecture as the product evolves.
Build founding teams that match your funding narrative and your ambition.
YC, Techstars, and tier-1 accelerators need cohort companies to hire right immediately.
Your first 10 hires are your first product. A shared language for evaluating potential.
Studios building multiple companies need a repeatable hiring intelligence layer.
Team risk is the #1 reason early-stage companies fail. Portfolio-grade hiring intelligence.
We interviewed 60 engineers using traditional methods and made two wrong hires. FEMQ™ identified the issue in both before we did. The Ownership Index alone saved us 6 months.
CEO, AI Infra Startup · YC W24
I'm a technical founder and I still couldn't articulate what I was looking for until I saw FEMQ™'s framework. Founder Compatibility aligned perfectly with our team's style.
CTO & Co-Founder · Seed Round
As a VC, I want our portfolio hiring people who can operate at founder-speed. FEMQ™ gives us a shared language for team quality we recommend it to every new investment.
Partner, Early-Stage VC Fund
Startup-fit prediction accuracy
Faster time-to-hire vs traditional
18-month retention rate
Founding engineers assessed
Your first 10 engineers are the company. Stop guessing. Start measuring what actually predicts founding-engineer success.
