AI-native interview platform
Stop testing puzzles.
Start seeing talent.
Thinqr replaces algorithmic coding interviews with competency-based assessments run inside real developer workspaces. Finally, a signal that predicts job performance.
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Trusted by engineering teams building the next generation of developer tools
Currently in closed beta. Customer logos coming soon.
The problem
The hiring signal stopped working.
AI-era engineers don't solve puzzles — they navigate ambiguity inside real codebases. Your interviews should measure that.
LeetCode is noise, not signal
Inverting a binary tree has never been part of anyone's actual job. You're measuring test-prep, not engineering judgment.
AI tools broke the old test
Candidates already use Copilot and Claude. Banning them is dishonest; allowing them makes traditional puzzles trivial. Either way, your scorecard is useless.
Gut feelings, not evidence
Debriefs collapse into vibes. A week later nobody remembers why the hire was strong — and six months in, you find out they weren't.
Product pillars
Three primitives. One clear signal.
Everything Thinqr does flows from three ideas. No gimmicks, no pretend-workflows.
Realistic workspaces
Full IDE, terminal, Linux desktop. Candidates bring their own tools — Copilot, Claude, whatever they'd actually ship with.
AI-coached interviews
The orchestrator runs structured scenarios, nudges when candidates stall, and adapts depth based on seniority.
Evidence-based scoring
Replay every keystroke, every AI prompt, every tradeoff. Scorecards tied to rubric dimensions, not gut.
How it works
From link to decision in 45 minutes.
No downloads. No IDE wrestling. No 'just a moment while I share my screen.'
- 01
Pick a scenario
Choose from our library of production-realistic tasks, or import your own. Tagged by role family, seniority, and stack.
- 02
Candidate joins workspace
One link. Instant Linux VM with their tools. No setup, no timezone friction, nothing to install.
- 03
Orchestrator runs the interview
Thinqr's AI coach asks, probes, and captures reasoning. You can observe live or review async.
- 04
Debrief with evidence
Every decision scored against a rubric. Timeline replay shows exactly how the candidate thought.
The wedge
The old way vs Thinqr.
Every row is a complaint we heard from engineering leaders. Every row is something we built into the product.
The old way
LeetCode-style
Thinqr
Workspace-native
What it measures
Ability to recall LeetCode patterns under time pressure
Real engineering judgment in a production-like workspace
AI tools
Banned (unrealistic) or allowed (trivial)
Encouraged — we score how well the candidate uses them
Environment
Whiteboard, online pad, or stripped-down IDE
Full Linux VM with the candidate's real toolchain
Interviewer load
Senior engineer pulled off work for 90 minutes
Orchestrator runs it; engineers review the replay
Evidence trail
A scorecard written from memory
Full timeline + signals tied to rubric dimensions
Candidate experience
Anxiety-inducing trivia drill
Realistic work, with coaching, like a paired session
We stopped asking about inverting binary trees three years ago and still hadn't replaced the signal. Thinqr is the first thing that actually measures how our engineers work now that AI is in the loop.
Design partner
VP Engineering, Series B fintech
SOC 2 Type II
In progress — audit report available under NDA
Tenant-isolated
Your code and candidate data never leave your tenant
GDPR compliant
DPA available, EU hosting option on Enterprise
AI policy
No customer code or candidate data used to train models
See it on a real interview
20 minutes.
One real scenario.
We'll run a live scenario with you in the actual product and walk through the evidence trail together. No slides, no pitch deck — just the workspace.
No credit card · Cancel anytime · SOC 2 in progress