Get interview details

curl --request GET \
  --url https://turing.gomu.ai/api/v1/integrations/candidates/interviews/{interview_id} \
  --header 'Authorization: Bearer <token>'

{
  "role_title": "Frontend Engineer",
  "candidate_email": "thanhlp18@gmail.com",
  "candidate_id": "f60c8c7b-a3a6-4d2f-af0c-8950cc437ddd",
  "interview_id": "c912a118-08d8-49aa-bf9f-602ec5aea395_f60c8c7b-a3a6-4d2f-af0c-8950cc437ddd",
  "iframe_url": "https://turing.gomu.ai/interview/fbSGW",
  "interview_type": "TURING_STEM",
  "sub_interview_type": "Software Engineer",
  "request_metadata": {},
  "summary": {
    "skill_summaries": [
      {
        "skill_name": "Technical Knowledge",
        "summary": "The candidate mentioned experience with front-end, back-end, and database design."
      }
    ],
    "highlights": [
      {
        "highlight_name": "Experience with Data Annotation",
        "description": "The candidate has some experience with data annotation for a vehicle dataset."
      }
    ],
    "reason_to_hire": [
      {
        "reason_name": "Experience with Distributed Systems",
        "description": "The candidate describes experience building and scaling a communications platform."
      }
    ],
    "areas_of_expertise": [
      {
        "skill_name": "Full-stack development"
      },
      {
        "skill_name": "Kafka"
      },
      {
        "skill_name": "Redis"
      }
    ],
    "interview_transcript_line": [
      {
        "speaker_name": "Turing",
        "transcript": "Hello, my name is Tracy. This will be a short conversation..."
      },
      {
        "speaker_name": "Lav Shah",
        "transcript": "I do have experience with front-end development as well."
      }
    ]
  },
  "agent_response_summary": [
    {
      "question": {
        "text": "How did you design the retry mechanism for failed messages?"
      },
      "agents": [
        {
          "id": "ai_researcher_1",
          "name": "Chris",
          "role": "AI researcher",
          "total_weighted_score": 72,
          "maximum_score": 90,
          "criteria": {
            "retry_mechanism_design": {
              "score": 4,
              "weight": 6,
              "weighted": 24,
              "maximum_score": 30
            },
            "problemsolving_skills": {
              "score": 4,
              "weight": 4,
              "weighted": 16,
              "maximum_score": 20
            }
          },
          "step6_summary": {
            "summary": "The candidate provided a detailed explanation of the retry mechanism.",
            "retry_mechanism_design": "They mentioned using two retry consumers after fifteen minutes."
          }
        }
      ],
      "normalization": {
        "maximum_possible": 540,
        "final_scores": {
          "ai_researcher_1": 72,
          "ai_researcher_1_max": 90,
          "ai_researcher_2": 72,
          "ai_researcher_2_max": 90
        },
        "ai_researcher_normalized": 0.8,
        "ai_researcher_1_normalized": 0.8
      },
      "mapping": {
        "agent": {
          "ai_researcher": "AI researcher",
          "subject_matter_expert": "Subject Matter Expert"
        },
        "criteria": {
          "retry_mechanism_design": "Retry Mechanism Design",
          "problemsolving_skills": "Problem-Solving Skills"
        }
      }
    }
  ],
  "assets": {
    "pdf_report_url": "https://cdn.qode.gg/assessment-pdf-turing/93381e1c_assessment_profile.pdf",
    "video_url": "https://cdn.qode.gg/recording/plDsMyRn7kWgRloewoDN/video_record.mp4",
    "transcript_url": "https://cdn.qode.gg/recording/plDsMyRn7kWgRloewoDN/chapter.json"
  },
  "metadata": {
    "start_time": "2025-11-25T10:00:07.933Z",
    "end_time": "2025-11-25T10:15:18.870Z",
    "completion_status": "REPORT_GENERATED",
    "scheduled_time": "2025-11-17T23:15:03.136Z",
    "drop_off_time": "2025-11-17T23:15:03.136Z"
  },
  "interview_signals": {
    "tab_changes": [
      192.173
    ],
    "abnormal_eye_tracking": [],
    "face_out_of_view": [
      184,
      808
    ],
    "avg_latency": 2,
    "external_screens": false
  },
  "overall_score": 70
}

GET

integrations

candidates

interviews

{interview_id}

Get interview details

curl --request GET \
  --url https://turing.gomu.ai/api/v1/integrations/candidates/interviews/{interview_id} \
  --header 'Authorization: Bearer <token>'

{
  "role_title": "Frontend Engineer",
  "candidate_email": "thanhlp18@gmail.com",
  "candidate_id": "f60c8c7b-a3a6-4d2f-af0c-8950cc437ddd",
  "interview_id": "c912a118-08d8-49aa-bf9f-602ec5aea395_f60c8c7b-a3a6-4d2f-af0c-8950cc437ddd",
  "iframe_url": "https://turing.gomu.ai/interview/fbSGW",
  "interview_type": "TURING_STEM",
  "sub_interview_type": "Software Engineer",
  "request_metadata": {},
  "summary": {
    "skill_summaries": [
      {
        "skill_name": "Technical Knowledge",
        "summary": "The candidate mentioned experience with front-end, back-end, and database design."
      }
    ],
    "highlights": [
      {
        "highlight_name": "Experience with Data Annotation",
        "description": "The candidate has some experience with data annotation for a vehicle dataset."
      }
    ],
    "reason_to_hire": [
      {
        "reason_name": "Experience with Distributed Systems",
        "description": "The candidate describes experience building and scaling a communications platform."
      }
    ],
    "areas_of_expertise": [
      {
        "skill_name": "Full-stack development"
      },
      {
        "skill_name": "Kafka"
      },
      {
        "skill_name": "Redis"
      }
    ],
    "interview_transcript_line": [
      {
        "speaker_name": "Turing",
        "transcript": "Hello, my name is Tracy. This will be a short conversation..."
      },
      {
        "speaker_name": "Lav Shah",
        "transcript": "I do have experience with front-end development as well."
      }
    ]
  },
  "agent_response_summary": [
    {
      "question": {
        "text": "How did you design the retry mechanism for failed messages?"
      },
      "agents": [
        {
          "id": "ai_researcher_1",
          "name": "Chris",
          "role": "AI researcher",
          "total_weighted_score": 72,
          "maximum_score": 90,
          "criteria": {
            "retry_mechanism_design": {
              "score": 4,
              "weight": 6,
              "weighted": 24,
              "maximum_score": 30
            },
            "problemsolving_skills": {
              "score": 4,
              "weight": 4,
              "weighted": 16,
              "maximum_score": 20
            }
          },
          "step6_summary": {
            "summary": "The candidate provided a detailed explanation of the retry mechanism.",
            "retry_mechanism_design": "They mentioned using two retry consumers after fifteen minutes."
          }
        }
      ],
      "normalization": {
        "maximum_possible": 540,
        "final_scores": {
          "ai_researcher_1": 72,
          "ai_researcher_1_max": 90,
          "ai_researcher_2": 72,
          "ai_researcher_2_max": 90
        },
        "ai_researcher_normalized": 0.8,
        "ai_researcher_1_normalized": 0.8
      },
      "mapping": {
        "agent": {
          "ai_researcher": "AI researcher",
          "subject_matter_expert": "Subject Matter Expert"
        },
        "criteria": {
          "retry_mechanism_design": "Retry Mechanism Design",
          "problemsolving_skills": "Problem-Solving Skills"
        }
      }
    }
  ],
  "assets": {
    "pdf_report_url": "https://cdn.qode.gg/assessment-pdf-turing/93381e1c_assessment_profile.pdf",
    "video_url": "https://cdn.qode.gg/recording/plDsMyRn7kWgRloewoDN/video_record.mp4",
    "transcript_url": "https://cdn.qode.gg/recording/plDsMyRn7kWgRloewoDN/chapter.json"
  },
  "metadata": {
    "start_time": "2025-11-25T10:00:07.933Z",
    "end_time": "2025-11-25T10:15:18.870Z",
    "completion_status": "REPORT_GENERATED",
    "scheduled_time": "2025-11-17T23:15:03.136Z",
    "drop_off_time": "2025-11-17T23:15:03.136Z"
  },
  "interview_signals": {
    "tab_changes": [
      192.173
    ],
    "abnormal_eye_tracking": [],
    "face_out_of_view": [
      184,
      808
    ],
    "avg_latency": 2,
    "external_screens": false
  },
  "overall_score": 70
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

interview_id

string

required

Response

200 - application/json

Interview details retrieved successfully

role_title

string

Example:

"Software Engineer"

candidate_email

string

Example:

"thanhlp18@gmail.com"

candidate_id

string

Example:

"5d19f4ff-1574-4aa6-a017-60f927f7740a"

interview_id

string

Example:

"123cb1b6-0cc1-4ab6-aa3c-5e0c75da6c6f_5d19f4ff-1574-4aa6-a017-60f927f7740a"

iframe_url

string<uri>

Example:

"https://turing.qode.world/interview/fbSGW"

request_metadata

object

Custom metadata object passed during interview creation.

interview_type

enum<string> | null

Type of AI interviewer used for this interview.

Available options:

TURING_STEM,

TURING_NON_STEM,

DOCKER_INTERVIEW,

SOFTWARE_ENGINEER

Example:

"TURING_STEM"

sub_interview_type

string | null

Grouped job title for TURING_STEM and TURING_NON_STEM interview types.

Example:

"Project Manager"

summary

object

Show child attributes

summary.skill_summaries

object[]

Show child attributes

summary.skill_summaries.skill_name

string

Example:

"Quantitative Analysis and Statistical Techniques"

summary.skill_summaries.summary

string

Example:

"Demonstrated strong proficiency in quantitative analysis, particularly with financial data..."

summary.highlights

object[]

Show child attributes

summary.highlights.highlight_name

string

Example:

"Nuanced Model Comparison"

summary.highlights.description

string

Example:

"Provided a clear and insightful comparison between statistical and ML models..."

summary.reason_to_hire

object[]

Show child attributes

summary.reason_to_hire.reason_name

string

Example:

"Quantitative Rigor in Financial Modeling"

summary.reason_to_hire.description

string

Example:

"Demonstrates a deep understanding and practical application of quantitative analysis..."

summary.areas_of_expertise

object[]

Show child attributes

summary.areas_of_expertise.skill_name

string

Example:

"Credit Risk Modeling"

summary.interview_transcript_line

object[]

Show child attributes

summary.interview_transcript_line.speaker_name

string

Example:

"Turing"

summary.interview_transcript_line.transcript

string

Example:

"Hello, my name is Tracy..."

assets

object

Show child attributes

assets.pdf_report_url

string<uri>

Example:

"https://cdn.qode.gg/assessment-pdf-turing/123cb1b6-0cc1-4ab6-aa3c-5e0c75da6c6f_assessment_profile.pdf"

assets.video_url

string<uri>

Example:

"https://cdn.qode.gg/recording/tpb6JAKot5e7ldzZgu19/video_record.mp4"

assets.transcript_url

string<uri>

Example:

"https://cdn.qode.gg/recording/tpb6JAKot5e7ldzZgu19/chapter.json"

metadata

object

Show child attributes

metadata.drop_off_time

string<date-time>

Example:

"2025-11-07T13:51:42.740Z"

metadata.scheduled_time

string<date-time>

Example:

"2025-11-07T13:51:42.740Z"

metadata.start_time

string<date-time>

Example:

"2025-11-07T13:51:42.740Z"

metadata.end_time

string<date-time>

Example:

"2025-11-07T15:10:43.000Z"

metadata.completion_status

enum<string>

Available options:

SCHEDULED,

STARTED,

NO_SHOW,

CANDIDATE_DROP_OFF,

TECHNICAL_GLITCH,

INTERVIEW_COMPLETED,

REPORT_GENERATED

Example:

"STARTED"

interview_signals

object

Show child attributes

interview_signals.tab_changes

number<integer>[]

Timestamps (in seconds) where the candidate switched browser tabs.

Example:

[12, 40, 120]

interview_signals.abnormal_eye_tracking

number<integer>[]

Timestamps (in seconds) where abnormal eye-tracking was detected.

Example:

[12, 140, 220]

interview_signals.face_out_of_view

number<integer>[]

Timestamps (in seconds) where the candidate's face was out of camera view.

Example:

[12, 410, 520]

interview_signals.avg_latency

number

Average audio/video latency during the interview (in seconds).

Example:

3

interview_signals.external_screens

boolean

Whether external monitors/screens were detected during the session.

Example:

false

agent_response_summary

object[] | null

Multi-agent assessment results for each interview question. See Multi-Agent Assessment for detailed schema documentation.

Show child attributes

agent_response_summary.question

object

Show child attributes

agent_response_summary.question.text

string

Full question text presented to the candidate.

Example:

"How did you design the retry mechanism for failed messages?"

agent_response_summary.agents

object[]

All evaluator personas and their scores for this question.

Show child attributes

agent_response_summary.agents.id

string

Index string of the agent, can use mapping object to map.

Example:

"ai_researcher_1"

agent_response_summary.agents.name

string

Agent persona name.

Example:

"Chris"

agent_response_summary.agents.role

string

Evaluator persona type (e.g., AI Researcher, Subject Matter Expert, Project Manager).

Example:

"AI researcher"

agent_response_summary.agents.total_weighted_score

number

Final score for this agent for this question: Σ (score_k × weight_k)

Example:

72

agent_response_summary.agents.maximum_score

number

Maximum possible score for this question: Σ (weight_k × 5)

Example:

90

agent_response_summary.agents.criteria

object

Scores for each rubric criterion for this agent.

Show child attributes

agent_response_summary.agents.criteria.{key}

object

Show child attributes

agent_response_summary.agents.criteria.{key}.score

number

Agent's rating on a 0–5 scale (0 = no evidence, 5 = strongest demonstration).

Example:

4

agent_response_summary.agents.criteria.{key}.weight

number

Relative importance of the criterion in the rubric.

Example:

6

agent_response_summary.agents.criteria.{key}.weighted

number

Computed as: score × weight

Example:

24

agent_response_summary.agents.criteria.{key}.maximum_score

number

Computed as: 5 × weight

Example:

30

agent_response_summary.agents.step6_summary

object

Human-readable interpretation of the agent's evaluation.

Show child attributes

agent_response_summary.agents.step6_summary.summary

string

High-level summary of the candidate's performance on this question.

agent_response_summary.agents.step6_summary.{key}

string

agent_response_summary.normalization

object

Aggregates all agent-level scores for this question.

Show child attributes

agent_response_summary.normalization.maximum_possible

number

Total possible score across all agents for this question.

Example:

540

agent_response_summary.normalization.final_scores

object

Each agent's actual weighted score and maximum possible score.

Show child attributes

agent_response_summary.normalization.final_scores.{key}

number

agent_response_summary.normalization.{key}

number

agent_response_summary.mapping

object

Translation layer between internal keys and human-readable labels.

Show child attributes

agent_response_summary.mapping.agent

object

Maps internal agent-role identifiers to human-readable labels.

Show child attributes

agent_response_summary.mapping.agent.{key}

string

Example:

{
  "ai_researcher": "AI researcher",
  "subject_matter_expert": "Subject Matter Expert",
  "project_manager": "Project Manager"
}

agent_response_summary.mapping.criteria

object

Maps internal criterion keys to their display names.

Show child attributes

agent_response_summary.mapping.criteria.{key}

string

Example:

{
  "retry_mechanism_design": "Retry Mechanism Design",
  "problemsolving_skills": "Problem-Solving Skills"
}

overall_score

number | null

Aggregated score across all questions and agents (0–100 scale).

Example:

70

event

enum<string>

Available options:

SCHEDULED,

STARTED,

NO_SHOW,

CANDIDATE_DROP_OFF,

TECHNICAL_GLITCH,

INTERVIEW_COMPLETED,

REPORT_GENERATED

Example:

"started"

timestamp

string

event timestamp in ISO 8601 format

Example:

"2025-11-11T08:04:04Z"

Create a candidate interview session Iframe Embedding

⌘I

Getting Started

Integration API

Embedding

Get interview details

Authorizations

Path Parameters

Response