AI-first candidate assessment

Simulate the job.
Don't just interview for it.

hiAIre assesses candidates through AI-native work simulations that measure judgment, execution, and real-world AI fluency - before you make the hire.

See how candidates actually think, prioritize, and deliver under real constraints.

Join the Waitlist See how it works ↓
●Product Designer ●Product Manager ●Chief of Staff ●AI Fluency Scoring ●Real Work Scenarios ●Dynamic Simulations ●Product Designer ●Product Manager ●Chief of Staff ●AI Fluency Scoring ●Real Work Scenarios ●Dynamic Simulations
The Problem

Interviews measure performance theater.
Not actual work.

Resumes can be polished. Interviews can be rehearsed. Case studies are often too artificial to reveal how someone really operates. But modern work has changed. The best candidates don't just think well - they know how to use AI well. Most hiring processes still don't measure that.

Bad hires are expensive
The cost of a mis-hire compounds through onboarding, ramp-up, team disruption, and eventual replacement.
Interviews miss real execution
Talking about how you'd handle a crisis is not the same as handling one with shifting inputs and a ticking clock.
AI fluency is now role-critical
Candidates who use AI effectively are measurably faster and produce higher-quality work. No interview tests for this.
How it works
01

Simulate

Put candidates into realistic, role-specific work scenarios with real constraints, shifting inputs, and actual deliverables.

02

Observe

See how they break down ambiguity, prioritize, communicate, and use AI tools to move faster without lowering quality.

03

Evaluate

Get a structured scorecard across AI fluency dimensions - so hiring decisions are based on evidence, not gut feel.

Roles

Built for modern
knowledge work.

◐

Product Designer

From ambiguous brief to polished output. Evaluate design thinking, execution speed, and AI-augmented craft.

  • Design under constraints
  • Rapid ideation and wireframing
  • Feedback incorporation
  • QA and detail orientation
◈

Product Manager

See how candidates prioritize, communicate tradeoffs, and make decisions with incomplete information.

  • Backlog prioritization
  • Stakeholder communication
  • Research synthesis
  • AI-assisted analysis
◉

Chief of Staff

Test strategic thinking, speed, and operational judgment when the ask is vague and the clock is moving.

  • Ambiguous problem breakdown
  • Cross-functional coordination
  • Executive-ready writing
  • AI-powered synthesis
What gets measured

The hiring metric most teams still ignore.

AI use is no longer the differentiator. Judgment is. hiAIre measures not just whether candidates use AI, but how effectively they use it across the entire workflow.

  • 1
    Adoption
    Do they use AI when it helps?
  • 2
    Input Quality
    Do they ask good questions and frame problems clearly?
  • 3
    Judgment
    Do they know what to trust, reject, or refine?
  • 4
    Verification
    Do they check outputs before using them?
  • 5
    Integration
    Does the final work improve because of AI?
The goal
The goal isn't to reward dependency. It's to identify candidates who can think clearly and use AI responsibly to produce better work. Great work without AI still scores well. Over-reliance on AI lowers judgment and verification scores.
Under the hood

What data powers the score.

Every score is backed by observable candidate behavior during the simulation.

📝
Outputs and deliverables
What the candidate produced, its quality, completeness, and whether it addressed the actual problem.
💬
AI interaction patterns
Every prompt they wrote, every AI response they accepted, modified, or rejected. How they refined outputs over multiple iterations.
🔄
Reasoning and revisions
How they responded to changing constraints, stakeholder feedback, and new information mid-simulation.
✓
Verification behavior
Whether they caught AI mistakes, cross-referenced claims against source data, and edited before submitting.
What employers get

A complete picture.
Not a gut feeling.

After each simulation, employers receive a structured AI Fluency Scorecard with dimension-level breakdown, behavioral evidence, and a clear hiring recommendation.

No guessing. No interpreting vague interview signals. Just data on how the candidate actually works.

AI Fluency Score 87
AI-Proficient
Adoption
92
Input Quality
89
Judgment
84
Verification
79
Integration
91
Simulation
The Inherited Product
Role
Product Designer
Duration
72 / 90 min
AI Tools Used
5 of 6
Prompt Quality Distribution: High 6, Medium 5, Low 3
Stakeholder Responses: 4/4 replied, avg 3.2 min
Files Reviewed: 6/6 materials accessed
AI Interaction Log: 14 interactions, 8 accepted, 4 modified, 2 rejected
Why this matters

Hire for the way work actually happens now.

The best candidates don't work in isolation. They work with tools, constraints, shifting information, and AI. If your hiring process doesn't reflect that reality, you're selecting for interview skill - not job performance.

"Won't this just reward people who use AI the most?"
No. The system rewards effective use, not maximal use. Over-reliance lowers judgment scores. Unverified outputs lower verification scores. Great work without much AI can still score well. The framework measures quality of thinking, not quantity of tool usage.

Start assessing candidates
through real work.

Join the waitlist for early access and be first to see how AI-native work simulations can improve hiring quality.

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