Eval-X is a technical interview platform built for how engineering actually works in 2026. Every engineer uses AI tools daily, but traditional interviews still test memorization, syntax recall, and artificial puzzle-solving. Eval-X fixes this. Candidates work in a browser-based IDE with access to multi-model AI (Claude, GPT-4o, Gemini) on real-world coding challenges. The platform captures everything: every prompt, every edit, every pause, every decision. This behavioral data powers a six-dimension evaluation framework: Problem Framing - Did they understand the problem before writing code? AI Usage Quality - Did they direct the AI strategically or just copy-paste? System Design - Did they make architectural choices or just optimize locally? Code Quality - Does the code survive change and edge cases? Adaptability - How do they handle requirement changes mid-task? Explanation and Ownership - Can they defend their decisions under pressure? The result: a multi-dimensional scorecard that gives hiring teams empirical evidence instead of gut feelings. Every evaluation is backed by timestamped behavioral data, not subjective interview notes. Eval-X replaces broken hiring signals (LeetCode, take-homes, trivia) with evidence-based evaluation of AI-native engineering judgment.