See how they actually work.
Not how they interview.
AI-native work trials that predict on-the-job performance — with evidence. Stop guessing. Start knowing.
Trusted by founders from YC, SPC, and more
Watch the demo
Hiring is broken.
Everyone knows it.
You've felt it. That sinking feeling three months in when your "great interview" turns into a performance problem. The process is designed to produce false positives.
Resumes lie
Credentials and keywords don't predict performance. You're hiring based on marketing, not capability.
1-hour interviews are theater
Candidates perform. They've practiced the questions. You learn who interviews well, not who ships.
LeetCode is cargo cult
Inverting binary trees has nothing to do with building products. You're testing memorization, not problem-solving.
AI changes everything
Your best hire in 2025 uses AI to 10x their output. Traditional interviews penalize this. You're selecting for the past.
"The best predictor of future performance is past performance — in similar conditions."
So why are we still using artificial conditions to predict real work?
Four steps to certainty
A week-long work trial that shows you exactly who someone is — not who they pretend to be in an interview.
Instrumented Sandbox
Real environment. Real tools.
Candidate gets SSH access to a fully-equipped VM with their choice of AI tools — Claude Code, Cursor, whatever they use. Just like their actual job.
Ambiguous Assignment
Designed to reveal agency.
A real-world project with incomplete requirements. We test how they handle ambiguity, make decisions, and turn chaos into shipped code.
Build a support inbox triage system. Ingest tickets, cluster by topic, propose auto-replies, admin UI for review.
"Requirements intentionally incomplete. Ask questions. Make decisions. Ship something real."
Observer Agent
Intelligent probing. Not surveillance.
Our AI observes commits, commands, and tool usage. At key moments, it asks surgical questions to understand their thinking.
Evidence-Based Report
Signal, not vibes.
A comprehensive hiring report with rubric scores, evidence clips, timeline analysis, and a clear recommendation you can act on.
Decisions backed by evidence
Not a score. Not a vibe. A comprehensive report that makes the hiring decision obvious — with receipts.
Candidate Evaluation Report
Full-Stack Engineer • Jan 2025
14.2 hours
47
12
Rubric Scores
Key Evidence
14:23 — "Chose to ship auth-less MVP first, documented security as Day 2 priority. Explicitly traded speed for completeness."
16:45 — When tests failed, diagnosed root cause in 8 mins using Claude Code. Fixed without introducing regressions.
What we measure
Eight dimensions that actually predict on-the-job success. Each scored 1–5 with specific evidence.
Problem Framing
Did they define success clearly before building?
Plan Quality
Milestones, sequencing, realistic scope
AI Leverage
Uses tools well, avoids hallucination traps
Engineering Fundamentals
Testing, correctness, architecture decisions
Speed to Value
Ships a working v0 quickly
Iteration & Debugging
How they react when stuck
Communication
Clarity, tradeoffs, stakeholder handling
Ownership & Agency
Proactive decisions, escalates with options
Built for the real world
Not another coding test. A fundamental rethinking of how to predict who will ship in your environment.
Tests ambiguity tolerance
Real work is messy. We give incomplete specs and shifting requirements. You see who thrives in chaos and who freezes.
Measures actual agency
Do they wait for permission or make decisions? Do they escalate blockers with options? Agency is observable, not self-reported.
Observes AI usage (doesn't ban it)
Your best hire uses AI as a force multiplier. We measure how well they leverage tools — not whether they can work without them.
Week-long signal vs 1-hour snapshot
See how they work over time. Energy management, iteration cycles, how they handle getting stuck. Not a performance, a pattern.
Defensible hiring decisions
Every recommendation backed by timestamped evidence. No more 'gut feeling' regret. Show your board exactly why you hired them.
Works with AI-native candidates
Traditional interviews penalize AI users. We're designed for 2025: the best engineers use every tool available. So should the evaluation.
Traditional hiring vs. Polymath
- ✕Resume screening (marketing document)
- ✕LeetCode (tests memorization)
- ✕Behavioral interviews (rehearsed answers)
- ✕"Culture fit" (vibes-based)
- ✕Decision made on 3 hours of performance
- ✓Real work trial (actual output)
- ✓Ambiguous problems (tests thinking)
- ✓Observed behavior (not self-reported)
- ✓Evidence-backed rubric (defensible)
- ✓Week-long signal (patterns, not moments)
For teams who can't afford to guess
Bad hires cost 6–12 months. The wrong engineer can kill a startup. If that risk keeps you up at night, we built this for you.
Startup Founders
Seed to Series B
You can't afford a bad hire. Every engineer either accelerates or drags. Get certainty before committing $200K+/year.
"We've been burned twice by 'great interviews.' Never again."
Hiring Managers
Engineering leads
Your gut says hire, but can you defend it? Get a report that makes the decision obvious — and backs you up later.
"Finally, something that shows me how they actually work."
Technical Recruiters
In-house teams
Resumes all look the same. LeetCode scores don't predict success. Give your hiring managers signal they can act on.
"The report sells itself. Hiring managers trust it."
Currently onboarding pilot partners
We're working with a small group of startups to refine the evaluation process. If you're hiring engineers in the next 3 months, let's talk.
Become a Pilot PartnerJoin the waitlist
Be among the first to transform how you evaluate candidates. We're accepting a limited number of pilot partners.