Inject real customer scenarios into a live coding session. Record every decision. Let Claude analyze how candidates wield AI as a superpower — and score them against a structured rubric before you make a costly hire.
35%
improvement in hire quality
vs. traditional interviews
2.4×
more productive hires
AI-fluent vs. non-fluent engineers
$287k
avg cost of a mis-hire
salary + recruiting + replacement
How it works
No setup required from the candidate. Just a link and a real problem to solve.
Choose from a library of real customer-context scenarios — or author your own. Set the time limit and send an invite link. No installations, no accounts required for candidates.
Customer-context briefing, starter code, sandbox environment — all in the browser.
rrweb captures every keystroke, AI prompt, terminal run, and iteration. We record how candidates use their tools, not just the final result.
Full DOM replay + code snapshots every 30s + sandboxed code execution logs.
Claude Opus analyzes the full recording against a structured 5-category rubric: prompt engineering, solution quality, iteration patterns, debugging approach, and creativity.
Detailed per-category scoring with evidence from the candidate's actual session.
The problem
Leetcode and system design interviews were built for a world before AI tooling. Today, a candidate who can prompt Claude effectively, iterate rapidly, and chain tools together will outperform a "brilliant" engineer who can't — by 2–3×. You have no way to measure that today.
Sample Maite Score Report
84
/ 100 — AI Tooling Score
"Strong prompt iteration patterns — rewrote the initial Claude prompt 3 times with increasing specificity. Effectively used the sandbox to validate edge cases. Missed error handling on malformed input records."
Return on investment
We modeled the ROI using published bad-hire cost data, GitHub's productivity research, and McKinsey's 2024 AI impact study. The assumptions are conservative.
Model assumptions
$185k/yr
Avg engineer total comp
Levels.fyi 2025
46%
Industry bad hire rate
LinkedIn Talent Trends 2024
+35%
Maite hire quality lift
Conservative estimate
2.4×
AI-fluent productivity gain
McKinsey / GitHub 2024
Cost per mis-hire: $185k salary + $37k recruiting (20% TC) + $28k onboarding + $37k replacement recruiting = $287k per bad hire. Productivity value of additional high performers: (2.4 − 1) × $185k = $259k/engineer/year incremental value delivered.
Startup
8 hires/year
Starter pack ($99/assessment)
Annual net value
$1.1M
91,819× ROI
$99/assessment · 12 assessments/yr
Scale-up
30 hires/year
Growth pack ($79/assessment)
Annual net value
$3.0M
83,979× ROI
$79/assessment · 45 assessments/yr
Enterprise
120 hires/year
Scale pack ($59/assessment)
Annual net value
$11.9M
112,216× ROI
$59/assessment · 180 assessments/yr
Assumes 1.5 candidates assessed per hire (light pre-screening before Maite). ROI figures are illustrative projections based on published research. Actual results vary.
Customer stories
Series B FinTech
120 engineers
"We were hiring FDEs who looked great on Leetcode but froze when a customer's data pipeline broke at 2am. Maite showed us which candidates could actually use Claude and Cursor to debug fast. Our 90-day regret hires dropped from 3 to 0 in the first cohort."
0 regret hires in 12 hires
— VP Engineering
Enterprise SaaS (Series D)
400 engineers
"We run Maite scenarios that mirror real implementations we do for customers. Candidates who score above 75 ship their first feature in week 1. Below 60, it's usually month 2. It's the single best predictor we've found."
3× faster ramp time for top scorers
— Director of Talent Engineering
AI-native startup
18 engineers
"At our stage, one wrong hire is existential. Maite lets us assess whether a candidate actually treats AI as leverage or as a crutch. There's a huge difference and it's almost impossible to see in a 45-minute interview."
Avoided 2 mis-hires, saved ~$574k
— CTO
Features
rrweb DOM replay captures every keypress, AI prompt, and iteration — not just the final code.
Claude Opus analyzes the full session against your rubric and returns evidence-backed per-category scores.
E2B-powered sandboxes let candidates run and iterate on real code. No install required.
Pre-built scenarios modeled on real customer problems across data engineering, APIs, and systems.
RLS-enforced org isolation. Each team sees only their candidates and results.
WorkOS-powered SAML/OIDC integration with Okta, Azure AD, and Google Workspace.
Candidates receive a magic link. Results are emailed automatically when scoring completes.
Weight categories by what matters for your role — FDE, IC, staff, or specialized tracks.
Webhooks and API to push scores directly into Greenhouse, Lever, or Ashby.
Pricing
You only pay when you actually assess a candidate. Buy a pack and use it as you hire — credits never expire.
One Maite assessment costs as little as $59. One bad engineering hire costs $287,000. That's a 4,864× downside if you skip it.
| Pack | Assessments | Per assessment | Pack total | Savings vs. on-demand | |
|---|---|---|---|---|---|
| On demand | 1+ | $149 | — | — | Talk to sales |
| Starter | 20 | $99 | $1,980 | Save $1,000 | Talk to sales |
| Growth Popular | 50 | $79 | $3,950 | Save $3,550 | Talk to sales |
| Scale | 100 | $59 | $5,900 | Save $9,100 | Talk to sales |
| Enterprise | 500+ | Custom | — | Max discount | Talk to sales |
All packs include: session recording, Claude analysis, email delivery, scenario library, admin dashboard, and API access. Enterprise adds SSO, audit logs, SLA, and custom rubrics.
Every engineering hire you make without Maite costs you a coin flip on AI fluency. At $287k per mis-hire, that's an expensive habit.
We'll get back to you within one business day.
$3.0M
net value for a 30-hire team
83,979×
return on investment at $79/assessment
$59
minimum cost per assessment (Scale pack)