What’s Assessed

Most Coding Interviews only grade output. Our AI evaluates how candidates think, communicate, and problem-solve in a live technical conversation — revealing strengths and gaps that static challenges can’t detect.

1. Problem-Solving Approach

We evaluate the candidate’s ability to break down the problem, choose an approach, and adapt when challenged.

Whether they default to brute force or consider optimizations
How proactively they explore edge cases
Whether advanced approaches (e.g., two-pointer, hashing, recursion) arise naturally or only when prompted
How much guidance they require to reach a solution

Signal Example: Candidates who need direct help to discover basic optimizations or implementation steps are flagged accordingly.

2. Technical Skills & Core Fundamentals

We assess language-specific fluency and real engineering competence — not just whether code eventually runs.

Gaps in foundational concepts (e.g., Python slicing, method chaining, list manipulation)
Over-reliance on hints or copy-pasting provided code
Whether they can write idiomatic, clean, correct code independently
Comfort switching between topics, patterns, or difficulty levels

Signal Example: Struggling with basic syntax or leaning on provided snippets triggers a low technical proficiency rating.

3. Communication & Soft Skills

Because real coding interviews are conversational, the AI evaluates how candidates think out loud.

Clarity when explaining reasoning and choices
Willingness to ask clarifying questions
Ability to communicate trade-offs and assumptions
Transparency about confusion or mistakes

Signal Example: Candidates who give minimal commentary or silently copy solutions without acknowledgment produce low communication and integrity signals.

4. Adaptability & Interactive Reasoning

Unlike static tests, the AI actively engages with the candidate.

How well they respond to follow-up questions
Whether they can adjust their approach when nudged
Depth of understanding when concepts are explored further
Ability to handle difficulty increases or topic switches (e.g., from arrays → strings → time complexity)

Signal Example: If they can only describe optimizations after being explicitly asked, the system notes it as reactive rather than proactive reasoning.

5. Debugging & Testing Behavior

We observe real debugging, not after-the-fact autograder fixes.

How candidates investigate failing cases
Whether they think in terms of inputs, outputs, and invariants
Their ability to self-correct without being spoon-fed
How they validate correctness through tests or reasoning

Signal Example: Heavy reliance on interviewer hints or failure to understand error messages reduces the debugging competency score.

A Real Coding Environment

No Downloads Need, Built-in Browser Based Code Editor

See Every Thought Process

Not Just the Final Code

Interview Integrity That Goes Beyond Browser Proctoring

Because our interviews happen through real conversation, not silent coding tests, the AI can spot behaviors that most proctoring tools completely miss.