Replace self-reported skills with verified competencies
Self-reported skills are assumptions. AI Simulations verify them — through realistic 30–45 minute work scenarios that produce objective, evidence-backed scores. Know what your people can actually do.
The difference between what people claim and what they can do
This is what the same skill looks like in your HRIS — and what it looks like after an AI Simulation. The gap is real. And every talent decision made from the left column is based on a guess.
Unverified
Verified with evidence
Three decisions you’re making with unverified data — right now
Unverified skills data isn’t just inaccurate — it’s the foundation for talent decisions that affect retention, performance, and cost.
Internal mobility based on claimed skills
You promote or transfer people based on their HRIS profile — skills they rated themselves years ago. The wrong person moves into a critical role. You find out months later.
L&D investment targeting assumed gaps
If you don’t know what people can actually do, you can’t know what they actually need. Training budgets go to courses employees don’t need — and skip the gaps that matter.
Succession planning from incomplete bench data
The succession candidates you’ve identified may not have the skills required for the role. You won’t know until you need them — which is too late to fix.
From self-reported to scored — four steps
Verification sits at Phase 3 of the 5-Phase Methodology — after skills are mapped and self-assessed, before intelligence and development decisions are made.
Skills mapped & self-assessed
Auto-extracted from CV, HRIS, and LinkedIn. Employee validates and rates their proficiency level.
AI Simulation assigned
A realistic 30–45 min work scenario. No interviewer. No scheduling. Objective conditions, every time.
Score + cited evidence
Skills rated 0–5. Each criterion shows the specific moment in the simulation that earned the score.
Verified profile updated
Scores feed Workforce Intelligence, development plans, mobility recommendations, and AI Readiness.
Technical and business skills — in any role
AI Simulations test skills in realistic work contexts — not theory, not quiz answers. Both hard skills and the soft skills that are hardest to assess.
Technical skills
- Software engineering — all languages and levels
- Data engineering and ML workflows
- Cloud architecture and DevOps
- Code review, debugging, system design
- AI tooling: Claude Code, Copilot, prompt engineering
- Financial analysis, data modelling, SQL
Business & soft skills
- Sales — discovery, negotiation, objection handling
- Stakeholder management and escalation handling
- Structured thinking and analytical reasoning
- Leadership — coaching, feedback, decision-making
- Customer success and account management
- Communication under pressure and in complex contexts
What verified skills data makes possible
Verification is the foundation. Everything downstream — mobility, succession, development, AI readiness — becomes more accurate the moment skills are confirmed rather than claimed.
Phase 3 of the 5-Phase Workforce Methodology
Verification is the pivot point. Before it: claims. After it: evidence. Every phase downstream depends on getting Phase 3 right.
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Frequently asked
What HR and talent leaders ask before rolling out skills verification.
See what your team’s skills actually look like
Book a demo and run a simulation as a participant yourself. The fastest way to understand what verified skills data looks like — and what it changes.
