MeritFirst is a skills-based hiring platform that replaces resumes with role-specific, scenario-based work assessments. Candidates complete functional challenges—like debugging live code or handling sales scenarios—that are scored against structured rubrics, producing a portable performance profile they can share with employers. The platform uses LLM-enabled evaluation to scale consistent test design and scoring while keeping human judgment in final hiring decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional hiring relies on proxies like school names, past employers, and resume keywords, which drown out genuine signals of ability and cause great candidates to be filtered out. Companies miss strong hires, and candidates without prestigious backgrounds are denied fair opportunities to demonstrate what they can do.
Solution
MeritFirst provides a hiring platform where candidates are evaluated by ability rather than pedigree. Candidates complete role-specific, scenario-based work samples—such as debugging live code, handling sales conversations, or working through finance cases—that measure planning, problem solving, learning agility, execution, and domain skills. Each submission is scored against structured rubrics, and LLM-enabled evaluation ensures consistent test design and scoring at scale without turning hiring into a black box. Candidates receive a portable performance profile with their scores and evidence artifacts, which they can share across partner companies as a "common app." Companies access data-driven insights and rankings to inform final hiring decisions while keeping humans in the loop.
Target Audience
Primary customers are companies seeking stronger hiring signal across functions, including startups and VCs, as well as candidates who want to demonstrate capability regardless of educational or employment background.
Features
- Role-specific, scenario-based work assessments that simulate real job tasks across engineering, sales, finance, accounting, and other functions
- Structured scoring rubrics that produce objective, comparable scores across candidates
- LLM-enabled test design, evaluation, and continuous learning for consistent assessment at scale
- Portable candidate performance profile containing scores, rank within cohort, and evidence artifacts
- Capability graph that maps candidate performance across planning, problem solving, execution, and domain skills
- Human-in-the-loop workflow that surfaces data-driven insights to recruiters and hiring managers for final decisions