People collaborating in a learning workspace with a subtle AI network overlay

Measure and Grow AI-ready workforce skills with realistic work simulations.

We create AI-scaled, case-based simulations that reveal how people use AI, adapt to feedback, and collaborate with others under realistic workplace constraints.

Explore the model

Meet our team

Problem

Workforce decisions need better evidence of real capability.

Employers need to see how people frame problems, use AI, evaluate evidence, adapt to feedback, and communicate decisions in role-relevant work.

AI-readiness is hard to measure and cultivate

Organizations lack reliable behavioral evidence of who can use AI well, learn with it, and improve over time.

Skills-based hiring is too static

Skills are granular and changing quickly, but credentials and job histories often describe the past rather than current capability.

Relational skills are hard to observe at scale

Communication, judgment, trust, and repair matter more in AI-mediated work, but interviews are costly and uneven signals.

Framework

Three competencies define AI-ready work.

Our framework separates practical AI use from the human capabilities that make AI-supported work effective, trustworthy, and learnable.

AI Fluency

Delegate, describe, discern, and act diligently when using AI across individual and team workflows.

Relational Fluency

Build trust, communicate under pressure, repair misunderstandings, and sustain collaboration in AI-mediated work.

Adaptive Flexibility

Pick up new skills, respond to feedback, revise strategies, and improve performance across attempts.

Design principles

Case-based simulations that make work visible.

Each simulation links role-relevant tasks, source materials, AI collaboration, work artifacts, and reviewer evidence into an inspectable assessment record.

AI-Scaled Work Demonstrations

Participants complete realistic, role-relevant cases with context, source materials, constraints, and deliverables.

Granular Skill Evidence

Behavioral evidence is linked to competencies, artifacts, decisions, revisions, and rubric anchors.

AI Structures, Humans Judge

AI helps generate cases, organize traces, and surface patterns while trained reviewers own interpretation.

Validate and Improve

Partners review evidence, calibrate scoring, and refine simulations through pilots and validation studies.

Six practices

A common work pattern reveals AI-ready behavior.

Across roles, simulations follow a shared pattern: plan the work, prepare the evidence, collaborate with AI, adapt through feedback, and deliver a decision-ready artifact.

Plan

Frame the problem, constraints, intended user, and where AI should or should not help.

Prepare

Give AI enough context, criteria, examples, and boundaries to support the work.

Collaborate

Check sources, assumptions, calculations, edge cases, and competing explanations.

Verify

Work within privacy, policy, fairness, and safety requirements.

Adapt

Revise the approach when evidence, feedback, or AI errors change the path.

Deliver

Explain the recommendation, tradeoffs, supporting evidence, and next steps.

Assessment evidence loop

AI scales the evidence. Humans own the decision.

The system is designed around visible behavioral evidence, reviewer calibration, feedback, and partner learning—not opaque scores.

Light assessment evidence loop connecting simulation, evidence, review, and scoring

Growth and validation

Measure capability. Help people improve.

Simulations can support hiring and promotion, but also upskilling, internal mobility, and workforce-wide AI-readiness assessment. Participants receive feedback, try again, and show how quickly they adapt.