The world builds with AI
it’s time you hire for it
Anyone can ship what the AI wrote. Strata shows you who catches it when it’s wrong. We score AI judgment, not AI usage.
Scroll to learn moreThe two-minute version
What Strata is, why AI judgment is the signal that survives, and how a seeded flaw becomes a defensible hiring decision.
The interview now grades the wrong thing.
Outdated interviews reward whoever hides AI best, so companies keep hiring people who cracked the test, not people who can do the job.
The AI bubble
Every candidate has the same frontier model, and the output all looks brilliant. An interview that grades the output can no longer tell who understood it.
Old tests, new cheats
People use AI to crack old-fashioned interviews. The take-home, the LeetCode round, the trivia quiz. Each one is a single prompt away from a perfect answer.
The pace has flipped
AI ships whole applications in an afternoon. The interview still asks questions written in 2015, so fast-moving candidates get judged by a slow-moving test.
Stack Overflow Developer Survey, 2025.
Not a test you bolt on. The whole funnel.
Companies hire through Strata start to finish: build, invite, assess, decide, sign off. Assessment is the spine; everything around it is ours too.
Build
01Paste a job description. Strata drafts the workflow: competencies, stages, weights, gates. Edit everything.
Invite
02Bulk-invite by email or open a public link. Invites, reminders and candidate comms all live in the funnel.
Assess
03Candidates move through your gated node graph. Every stage produces evidence, not just a number.
Decide
04Composite + judgment score resolve to GO / NO-GO / REVIEW, each carrying its reasoning trail.
Sign off
05A human confirms or overrides, logged, auditable end to end. Every override sharpens the system.
We score AI judgment, not AI usage.
Your workflow is a node graph
Six node types, colour-coded. Chain them in any order, weight them, gate them. Tap a node to see what it measures.
A real task in a browser IDE where the only AI is one Strata controls, captured and seedable. In sandbox mode its output sometimes carries a plausible, deliberate flaw. We attribute each changed line human vs AI, track AI-reliance %, tool and test runs, then a 12-agent panel scores catch, verify and push-back. This is the signal nothing else measures.
weights sum to 1 gates narrow the funnel before the costly nodes run
The funnel has a memory.
As candidates move through your workflow, everything they produce threads into one per-candidate Context Graph. No stage starts cold: each builds on the evidence before it, and the final decision reasons over the whole graph.
Every stage writes
Every node writes what it produced, and the evidence behind it, into the same record.
Context Graph
One per candidate run
Every later stage reads
The AI Interview
Its questions are generated from the candidate's own earlier work, not a generic bank.
The live interviewer
Opens with a pre-brief on what the candidate already showed, never a blank slate.
The decision
GO / NO-GO / REVIEW reasons over the whole graph, not one stage in isolation.
Plant a flaw. Watch what they do with it.
Candidates are never told which task is seeded, or when. Verifying the model’s output is the job, which is exactly what makes it a fair test.
Plant
The candidate builds a real task in a browser IDE with an AI assistant Strata controls. In a sandbox task, the assistant’s output carries a seeded flaw, a plausible one. An off-by-one. A swallowed exception. An API that doesn’t exist.
Observe
Strata captures provenance, not keystrokes: which lines came from the model, which the candidate wrote, what they ran, what they tested, what they accepted without reading. The flaw either survives to the diff or it doesn’t.
Score
A 12-agent panel judges catch, verify and push-back, each agent scoring one dimension, blind to the others, with mandatory cited evidence. A debrief asks the candidate to explain their own submission back.
From evidence to a decision you can defend
Node scores and cited evidence roll into the Context Graph. A weighted composite and the judgment score resolve to one recommendation, never a verdict, always a trail. A human confirms or overrides, and the override is logged.
Clears composite, judgment, must-have coverage and JD-fit.
Borderline on exactly one criterion. A human looks closer.
Falls short, with the reasoning trail that says why.
what the evidence actually looks like
Every score points back to the moment that earned it.
A seeded flaw, the candidate catching it, and the judgment score that falls out of it. No black box: the diff line, the debrief, and the exact file and line are all right there, so you review a case instead of a number.
Not one black box. Twelve agents.
The AI Dev sandbox is scored by a panel of twelve agents. Each judges a single dimension, blind to the others, and cites its evidence. A deterministic judge weights them into one score, so nothing hinges on a single model's opinion.
Outcome
Did the work meet the task? Completeness, regressions and edge cases, read from the diffs and the tests.
Code quality
Structure, readability, idioms, and how maintainable the shipped code is.
AI-native efficiency
The prompt-to-diff trajectory: steering the assistant and recovering from bad output.
Prompt craft
The prompts themselves: specificity, context, constraints, and how the ask was decomposed.
Process & decision
The debugging path, the sequencing, and the trade-offs visible in the order of events.
Verification rigor
How well they tested: edge cases exercised, and whether a green run actually meant something.
Debrief & understanding
Do the debrief answers show real understanding of the code they shipped, matching the diff?
Seeded-error judgment
Did they grasp why the planted flaw was wrong, reason about its impact, and fix it properly?
Security hygiene
Injection risks, secret handling, input validation, and authorization mistakes in the diff.
Performance sense
Algorithmic and resource efficiency: needless N+1s, unbounded loops, obvious hot-path waste.
Attribution audit
Human vs AI line provenance, checked against the diff, flagging wholesale paste and misattribution.
Integrity
Idle-then-perfect anomalies and off-task behaviour. Clean by default, unless the evidence says otherwise.
The judge is deterministic.
The twelve verdicts roll up by rubric weight into the node score, the same way every time. No single model gets the final say, and a human can override it with a logged reason.
One dimension each
Every agent judges a single thing, blind to the other eleven, so nothing cascades.
0 to 4, always cited
Each level quotes a timestamp or a line from the session. No number without evidence.
Weighted, then rolled up
A deterministic judge applies the rubric weights the same way every time.
And this is only the dev panel. Across Strata, 20+ agents parse the job description, draft the workflow, run the interviews and reason out the final decision, each accountable to cited evidence and human-overridable.
Everyone captures the session. We prove the score.
Legacy tools test AI-solvable syntax. The new wave measures AI-era building but can’t prove it predicts performance. Strata does both, feature-for-feature, then more.
Real features on a real codebase in a browser IDE, one stage of a full hiring workflow
Algorithmic or project-based tasks in a sandbox
Standardized coding tasks in a sandbox
AI-resistant puzzles + DSA problems
Competitor rows reflect publicly described capabilities as of mid-2026.
Not a score. A picture of how they engineer.
Every stage produces evidence, and the composite decision is built from it, auditable end to end.
A cited evidence trail, not a number
Every score links to the moment that earned it: the diff line, the transcript timestamp, the test run. You review a case, not a black box. Session replay is built in.
A judgment score that predicts
Catch, verify, push back. Measured, not inferred, and calibrated against real outcomes.
Defensibility, built in
GO / NO-GO / REVIEW with a human override always available and logged. EEOC four-fifths and per-group analysis ship with the platform, on consented data decoupled from scoring.
Zero interviewer hours
Fully async. Candidates run themselves through the funnel; results land in hours, not weeks.
Paste a JD, get a workflow
The architect drafts competencies, stages, weights and gates from the job description. You edit everything. It just skips the blank page.
A fair candidate experience
Real work instead of trivia, and no surveillance theatre. Integrity signals are advisory, shown with evidence. Nothing auto-rejects a person.
The signal that survives
We score AI judgment, not AI usage
Six node types, one composite. Every stage narrows the funnel and adds evidence, until the decision is one you can defend.
Test the judgment, not the typing.
Paste a job description and Strata drafts the workflow. Or start from a template and change everything. You see how they engineer before you make the call.
Built on Reskilll's 6M-developer network.
Capturing a session and scoring it is table stakes. Proving the score predicts who actually performs takes a network no assessment startup can build overnight. Strata is built on Reskilll: 6M+ developers and 2,000+ hackathons of real outcome data.
Distribution plus longitudinal outcome data is a compounding advantage: candidate acquisition cost near zero, real performance labels to prove the score, and a pool of pre-assessed builders on tap. It’s the one thing a well-funded clone can’t ship next quarter.
Proven, not asserted
Anyone can capture a session and score it. Strata’s validity engine measures Pearson r, AUC and top-quartile lift against real outcomes, and recalibrates GO thresholds where validity actually holds, sliced by role and by judgment dimension.
A warm talent pool
Assessments can draw on builders from the network who’ve opted in, already assessed, already judgment-scored. Supply shows up on day one; you’re reaching people who have demonstrated the exact skill, not buying a cold top-of-funnel.
A compounding loop
Run judgment sandboxes across the base → candidates earn a credential → employers generate inbound → we gather more outcome data → the score gets sharper. Features get cloned. This loop doesn’t.
Priced to the funnel, not the seat.
Building workflows and designing assessments is free. You pay per candidate-node consumed, so cost tracks the funnel, never a seat you forgot to cancel.