AI Interviews & Simulations
Run realistic AI conversations. Keep decisions human.
Give every candidate a structured opening, probe their answers with bounded follow-ups, and preserve the conversation as evidence a named evaluator can review against your rubric.
SkillCort evaluates what candidates say and do—not facial expressions, emotion, accent, or personality.
Choose the conversation format that matches the evidence you need
Start every candidate from the same approved question.
Authors define role-relevant opening questions and the rubric used to review them, so the interview begins from a consistent standard rather than improvised small talk.
- Same core question
- Role-relevant prompt
- Shared evaluation criteria
- Author-controlled assessment context
Probe the substance of what the candidate said.
Follow-ups respond to the candidate's answer while staying bounded by the interview instructions and configured turn limit. The AI asks for depth; it does not judge or decide during the conversation.
- Answer-aware follow-ups
- Configured turn limit
- Role-bounded conversation
- No in-conversation verdict
Collect an answer without making a microphone the gatekeeper.
In the turn-based format, candidates can answer by voice or use the typing fallback. Spoken questions include subtitles, and speech is transcribed into the evidence record.
- Spoken candidate turns
- Typing fallback
- Question subtitles
- Stored transcript evidence
Run a realtime conversation when the assessment calls for it.
Eligible interview configurations can be delivered as a live realtime voice or talking-avatar conversation while keeping the authored structure and review trail.
- Realtime conversational delivery
- Voice or talking-avatar presentation
- Authored interview instructions
- Conversation retained for review
Define the situation before the conversation begins.
A simulation carries the scenario, the role the candidate plays, the AI persona, the objective, and the opening message as part of the authored task.
- Scenario context
- Candidate role
- AI-played persona
- Explicit task goal
Make the case respond to the candidate's choices.
The persona continues the scenario turn by turn, creating evidence of how the candidate communicates, prioritizes, de-escalates, or handles an objection.
- Turn-by-turn role play
- Scenario-bound persona
- Candidate-led response path
- Reviewable conversation
Use text, voice, or a live conversational presentation.
Simulation authors can configure spoken interaction and, on eligible plans, live realtime delivery with voice or a talking avatar. A voice fallback protects the live experience if the avatar cannot start.
- Text-based simulation
- Spoken interaction when enabled
- Live voice or talking avatar
- Automatic avatar-to-voice fallback
Score what happened in the conversation.
The stored dialog can be reviewed against the simulation's rubric. AI may draft criterion suggestions, but the evaluator records the scores and your team makes the decision.
- Dialog stored as evidence
- Criterion-level rubric review
- AI suggestions kept separate
- Named human scoring
Keep the automation visible, bounded, and reviewable.
AI disclosed to candidates
The candidate sees that the interviewer or persona is AI and receives the relevant processing notice before participating.
Transcript as evidence
The conversation is preserved so reviewers can read what was actually said instead of accepting a hidden interview score.
Content—not affect
Evaluation uses the candidate's words and task evidence, never facial expression, inferred emotion, accent, or personality analysis.
Human-owned scoring
AI can draft rubric suggestions from the dialog; a named evaluator applies or replaces them before they count.
Provenance retained
Assisted evaluation records the model and version behind the generated output for later inspection.
No automated outcome
The conversation never shortlists, rejects, or records a hiring decision without your team.
Turn a conversation into evidence your team can inspect.
The value is not an AI-generated personality score. It is a structured exchange: authored context, candidate answers, relevant follow-ups, and a transcript tied to a rubric.
Reviewers can read the dialog, inspect any AI suggestion against the candidate's own words, and record a criterion-level judgment that flows into the same evidence and decision workflow as every other task.
See evaluation and evidenceAuthor the structure
Define the opening, scenario or persona, competency boundaries, and rubric.
Run the conversation
Use turn-based or configured live delivery to gather role-relevant answers.
Preserve the dialog
Store the candidate's words and conversation context as reviewable evidence.
Score with a person
Have a named evaluator confirm or replace suggestions and own the judgment.
Conversation formats for evidence that a static form cannot gather
Structured interviews
Ask the same approved opening and use bounded follow-ups to gather comparable depth.
Responsive simulations
Let an AI persona react inside an authored customer, stakeholder, or operational scenario.
Stored transcripts
Keep the full exchange available in the review workspace behind evaluator scores.
Human-scored rubrics
Review the candidate's words against explicit criteria without an automated shortlist or rejection.
Use AI to conduct the conversation—not to become the hiring manager
Black-box interview automation
- Opaque fit or personality score
- Face, emotion, or voice-style inference
- Different unbounded conversations
- Transcript hidden behind a ranking
- Automatic screening outcome
AI conversations in SkillCort
- Authored role-relevant structure
- Bounded follow-ups and scenarios
- Candidate words kept as evidence
- Shared rubric and named evaluator
- Final decision made by your team
A useful AI interview creates more evidence at a consistent first stage. It does not replace the hands-on work sample or the people responsible for the outcome.
Candidate transparency is part of the product flow.
Candidates are told when an AI interviewer or persona is involved and how their answers are processed. The review focuses on the substance of the response, with the transcript available to the evaluator.
- AI identity disclosed
- Processing notice before participation
- Typing fallback in turn-based interviews
- Subtitles for spoken questions
- No face or emotion scoring
- No automatic candidate decision
AI interview and simulation questions
An AI interview asks structured role-relevant questions and follows up on the candidate's answers. A simulation places the candidate inside an authored scenario where an AI persona plays the customer, stakeholder, or counterpart. Both preserve the conversation for rubric-based human review.
No. Interview and simulation evaluation is grounded in the candidate's words and the task rubric. SkillCort does not infer personality, emotion, or fit from a face, accent, or vocal style.
Turn-based voice interview tasks include a typing fallback and show spoken questions with subtitles. Live realtime formats have their own configured presentation requirements, so teams should choose a format that matches the candidate population and task.
No. AI conducts the configured conversation and can draft criterion-level review suggestions. A named evaluator records the scores, and your team makes every advance, reject, or other candidate decision.
Use a structured interview or simulation when the evidence is verbal reasoning, situational judgment, objection handling, or communication inside a changing scenario. Use a work sample when you need to inspect a real output. Strong assessment flows can use both without treating either as an automatic verdict.
Build one conversation around a real role.
Bring a screening question or customer scenario to a demo. See the candidate exchange, transcript, rubric review, and human-owned outcome in one flow.