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Built for how engineering teams actually work.

Explore the tools that help organizations use AI-powered software engineering simulations to measure engineering judgment under the same constraints engineers will face on the job.

Assessment in progress34:12

Incident Resolution

A spike in checkout failures is hitting the west region, causing backlogs and timeouts. Investigate the issue and mitigate customer impact.

Judgment profile
Engineering judgment dimensionsScores from 0 to 100, with optional benchmark comparisons.FIASCTCICC
Candidate
Internal Avg
Engineering judgment dimensions: data table
DimensionCandidateInternal Avg
FI8573
AS6572
CT8068
CI7071
CC7574

01How a Simulation Actually Works

The simulation doesn't wait for you. It runs on its own clock, just like production.

Consequences unfold, they don’t resolve

Take the obvious action and it can appear to work, then regress minutes later. The candidate has to notice, not just react once.

Pressure escalates if you stall

Stakeholders aren’t scripted to wait patiently. Go quiet on an update and someone interrupts asking for status, exactly like a real incident channel.

Every scenario has an “if you do nothing” branch

The situation gets worse on its own timeline if the candidate never acts, not only if they act wrong. Passivity is itself a signal.

One real run, not a script.
T+0Alert fires
T+5Candidate asks the wrong question
T+10A teammate interrupts for a status update
T+15Candidate tries the obvious fix
T+16It appears to work
T+17It quietly fails again

02Five dimensions. What strong and weak actually look like.

These aren’t personality traits. They’re observable moves inside a live scenario, and here’s the difference between a candidate who has the judgment and one who doesn’t, on the same incident.

Engineering judgment dimensionsScores from 0 to 100, with optional benchmark comparisons.FIASCTCICC
Candidate
Internal Avg
Engineering judgment dimensions: data table
DimensionCandidateInternal Avg
FI8573
AS6572
CT8068
CI7071
CC7574
DimensionWeak looks likeStrong looks likeScore
Frame Interrogation (FI)

Jumps straight to the first plausible fix.

Asks whether this is actually the problem it looks like before touching anything.

82/100
Assumption Surfacing (AS)

Accepts “the timing lines up, so it must be the deploy” at face value.

Checks whether the timing is causation or coincidence before acting on it.

75/100
Consequence Tracing (CT)

Restarts the service without asking what else depends on it.

Flags the downstream backlog before it becomes its own incident.

92/100
Constraint Integration (CI)

Pushes for the fastest fix regardless of who owns the system.

Works within access boundaries and escalates to the right owner instead of overstepping.

95/100
Communication Calibration (CC)

Goes quiet for ten minutes while heads-down on the problem.

Sends a plain-English update before anyone has to ask for one.

94/100

03Trust Calibration

We put a real AI assistant inside the simulation and measure whether a person can be trusted to work with AI in the loop. The copilot is genuinely helpful, and at a scored moment it is confidently wrong. What the candidate does next is the signal.

Verified

Trust cues are explicit: can explain a human or system is the source, and weighs strong evidence over weak.

Propagated

Carries an unverified claim into a stakeholder update or decision, unchecked. The failure mode AI makes cheap.

Ignored

A clear flaw was missed or dismissed, noticed only after acting at the wrong time due to over-trust or inattention.

The Trust Calibration Index

The Trust Calibration Index sits alongside the five judgment dimensions we already score. It spans verified, over-trusted, and ignored behavior in the AI working with the person. Every classification is backed by a verbatim quote from the transcript, so you see exactly where trust was earned or missed.

04Realistic Pressure

Every candidate faces the same authored evidence and constraints.

Timed events

New facts and stakeholder pressure arrive at authored moments in the incident.

The incident unfolds

Consequences land

Decisions change what happens next while the underlying evidence remains fixed.

Comparable by design

The same scenario version, rubric, and evidence boundaries apply to every candidate.

05No Answer Key

There’s nothing to leak, and nowhere to look it up.

Candidate’s Browser
  • Scenario
  • Slack
  • Docs
  • Metrics
Not sent to browser
  • Hidden facts
  • Rubric
  • Scoring criteria
  • Future events
Secondframe Servers
  • Hidden state
  • Rubric
  • Scoring
  • Evaluation logic
  • Future events

06A Defensible Score

A score you can defend in a hiring meeting.

Candidate Session
“Let’s verify whether timing actually implies causation.”
Independent AI Grading
92
89
91

Multiple passes, independently scored

Median Score

(used in report)

Evidence in Report
“Let’s verify whether timing actually implies causation.”
Candidate, 00:07:12

Verbatim quotes from the session back every claim.

07Internal Benchmark

Your team sets the bar.

Run your own engineers through a scenario first, so their scores become the internal benchmark: a P50 line grounded in the people already succeeding in the role, not an industry guess.

Your Team (Internal)
P50
0255075100

You’ve seen how it works. See what it finds.

Early access is openNo credit card requiredTransparent pricing