How it works
Experts, organizations and AI buyers each start from a different door. The steps behind them are the same.
Experts describe what they know. Organizations name the roles and knowledge they’re willing to offer. Buyers describe what they need evaluated.
Transparent matching, credential checks, consent and permissions are settled before any work starts.
Experts are paid for evaluation. Buyers receive results and governed access. Organizations approve every stage.
One profile, honest matches and a job-search assistant that only drafts. You approve every submission.
Experts →Paid work for staff who opt in, and a readiness plan before anything else. No sensitive uploads.
Organizations →Matched expert teams, clear protocols and results, without unnecessary source data.
AI Buyers →Plain words first. Here is what they mean in industry terms.
Also called a human rater or RLHF contributor: someone who scores or ranks AI answers so a model can be measured or improved.
Deliberately trying to make an AI system fail safely, so problems are found before real people meet them.
A privacy-governed set of realistic, synthetic situations used to test or train AI. Sometimes called an evaluation dataset.
A fixed, versioned test suite with scoring rules, so models can be compared over time.
A hosted practice world where an AI agent performs a workflow and is scored at each step. Used for agent evaluation and reinforcement learning.
The agent that finds matches and drafts applications from your profile. It never submits outside your settings.