AIQCAT stands for AI Quantitative Capability Assessment Test. It is not an IQ-style intelligence test; it quantifies practical task-solving capability with generative AI.
The CAT in AIQCAT is Computerized Adaptive Testing — the exam adapts to your level in real time, measuring precisely with fewer items. See what AIQ means and how it differs from IQ.
your own exams, tasks, difficulty, and pass criteria — built for your organization.
a proprietary construct graded on real work by a swarm of AI evaluators.
every team and every level on one AI-competency data platform.
AIQCAT is not a one-size-fits-all certificate. The thing it measures — AI competency — is proprietary and rigorous. How it is measured is yours to define: your exams, your tasks, your difficulty, your pass criteria. The result is a single, comparable picture of AI competency across your whole organization.
How it works →Define the test scope, item formats, and difficulty for each role or department. Your bar, not a generic one.
A factory of authoring agents drafts your exam in parallel, then refines it with you through dialogue.
Submissions are graded on real artifacts by a swarm of AI evaluators — consensus scoring, reduced single-model bias.
Authoring, grading, analytics, and delivery connect into one AI-competency data platform — every team, one view.
A comprehensive view of each candidate's performance across all dimensions. Understand ability level, distribution, deviation, and answer spread on a single integrated dashboard.
| Excel | Image | Video | Overall | Status | ||
|---|---|---|---|---|---|---|
| Q2: Sales Analysis | 92/100 | Graded | ||||
| Q2: Forecast Model | 88/100 | Graded | ||||
| Q2: Market Insight | 75/100 | Graded | ||||
| Q4: Executive Deck | 93/100 | Graded | ||||
| Q5: Case Summary | 81/100 | Graded |
Track every deliverable across Excel, PDF, images, and video in a unified matrix. Real-time grading status and scores provide transparency and operational efficiency at scale.
Our AI Assistant evaluates each submission in depth, providing a radar view across dimensions and red-pen style improvement comments directly embedded in the candidate's work.
Improve data cleaning steps and handle missing values more systematically.
Model assumptions are not fully justified. Provide clearer rationale.
Charts could be more effective with better labels and annotations.
Executive summary should highlight key insights and recommendations.
