belongin.ai measures AI Fluency and Agentic AI readiness from observed behaviour — not surveys, not self-assessment. Before and after training. At team and individual level. And the junior you hire from a belongin.ai-assessed cohort arrives with verified judgment your onboarding programme used to produce in year two.
How the candidate naturally frames problems before AI enters the picture — Insight-First, Evidence-First, Process-First, or People-First. Derived from prompt sequencing, resource timing, and stakeholder consultation patterns across three simulations. Activates at MVP alongside Career Direction.
Deploying AI tools with no way to measure whether behaviour is changing. belongin.ai provides baseline and post-training measurement at team and department level. And for hiring: entry-level roles are scarce, but candidates with a verified belongin.ai profile arrive with demonstrated AI judgment — the capability that used to take two to three years of employment to develop.
Need behavioural credentials employers trust more than completion certificates. The belongin.ai profile becomes part of the graduate credential — a verified signal of AI judgment that travels with the candidate into the job market.
Coursera, LinkedIn Learning, internal academies — want to show their content moves AI Fluency scores. belongin.ai becomes the measurement standard their training is validated against.
Candidates complete a 25-minute simulation working with Anthia — a live AI assistant that makes one precisely designed error per session. The scoring is entirely behavioural: did they catch it, how fast, and what did they do next? No questions. No survey. Just work.
Each level is scored from observed behaviour across three simulations — not self-assessment, not a test score. The level describes a real pattern of AI judgment your team member or candidate brings to every AI-integrated task.
Engages with AI as a tool but does not yet verify outputs before acting. Accepts AI recommendations without checking source data. The starting point — present in most new hires and early-career candidates.
Needs verification habit-building. Can complete AI-assisted tasks but requires structured check-in on output quality.
Checks AI outputs against source data. Errors in figures and facts are catchable. Verification is present — the development edge is consistency across different error types.
Can operate independently on data-heavy AI tasks. Will catch obvious errors. Needs support on reasoning and framing errors.
Brings independent judgment to AI outputs — verifies before prompting, challenges reasoning, and does not defer to outputs that do not hold up. Selective trust applied consistently.
Operates with minimal supervision on AI-integrated work. Catches reasoning errors, not just data errors. Ready for AI-responsible roles.
Catches reasoning errors across three different error types. Consistent verification instinct — before prompting, during the task, and in how outputs are used. Independent judgment maintained throughout.
Ready to work with agentic AI systems. Suitable for roles with AI-output accountability. Can develop AI judgment in others.
Every dimension is derived from telemetry — the sequence of actions the candidate took, not what they said about themselves. Five dimensions are scored at AI Fluency level today. Eight activate with full dimension scoring at MVP.
Book a demo. We'll run a pilot cohort and show you the before/after profile shift in 30 days.