Mock Interview Platform

The Mock Interview Platform is decision architecture applied to a concrete, high-stakes moment: the technical interview. Its premise is that feedback only helps if it points at the reasoning, not just the outcome.

Problem

Most interview practice tools optimize for a number. The candidate finishes, sees a score, and learns almost nothing about why. Where did the approach go wrong? Which assumption was unfounded? Which fork led somewhere better? The verdict arrives without the map.

Feedback that cannot be acted on does not improve performance, it just produces anxiety.

Strong interviewers evaluate reasoning, not just answers. Practice tools that only grade the answer teach candidates to guess at what graders want.

Architecture

The platform captures and analyzes the path, not just the destination.

• A scenario engine runs structured, repeatable interview formats.
• Reasoning capture records the candidate’s decision path during the session.
• Decision-path analysis compares that path against strong approaches and scores where they diverge.
• Feedback generation turns the divergence into specific, actionable next steps.

The legibility principle here is the same one that runs through the rest of the systems: make the reasoning inspectable so it can be improved.

Where it’s going

The scenario engine is built. The active work is reasoning capture - recording how a candidate actually arrived at an answer, which is harder and far more useful than recording the answer itself.

From there, feedback depth: turning a captured decision path into a precise account of what to practice next. Practice should compound, and it only does when feedback tells you exactly where to aim. See the wider thinking in the atlas.