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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": "Software Engineer",
  "mapped_role_title": "Backend Developer",
  "candidate_email": "example@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": {},
  "session_id": "4e9e55ee-63f3-4c04-87ec-82a1823a538d",
  "candidate_profile": {
    "avatar": null,
    "fullname": "",
    "year_of_experience": null
  },
  "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."
      }
    ],
    "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",
    "scorecard_time": "2025-11-25T10:16:00.000Z",
    "scheduled_time": "2025-11-17T23:15:03.136Z",
    "drop_off_time": "2025-11-17T23:15:03.136Z",
    "realtime_status": "INTERVIEW_COMPLETED",
    "is_terminal": true
  },
  "duration": 1911,
  "interview_signals": {
    "tab_changes": [
      192.173
    ],
    "abnormal_eye_tracking": [],
    "face_out_of_view": [
      184,
      808
    ],
    "avg_latency": 2,
    "external_screens": false
  },
  "black_screen_timestamps": [
    {
      "start": "00:00:01.600",
      "end": "00:00:03.000",
      "duration": 1.4
    },
    {
      "start": "00:01:42.711",
      "end": "00:01:48.411",
      "duration": 5.7
    }
  ],
  "meeting_data": {
    "completed_status": "COMPLETED",
    "start_date_time": "2026-03-10T03:02:01.438Z",
    "end_date_time": "2026-03-10T03:02:01.438Z",
    "chapters": [
      {
        "title": "Explanation of the approach to the first task",
        "start_time": "00:00:17.218",
        "end_time": "00:03:20.830"
      },
      {
        "title": "Explanation of the approach to the second task",
        "start_time": "00:03:20.830",
        "end_time": "00:05:18.888"
      },
      {
        "title": "Key technical areas of expertise",
        "start_time": "00:05:18.888",
        "end_time": null
      }
    ],
    "question_summary": [
      {
        "question": "Could you walk me through your approach and explain how you intended to solve the problem?",
        "summary": "They chose to skip this task.",
        "timestamp": "00:00:17.218",
        "question_score": {
          "agent_summary": "Not evaluated",
          "score": {
            "score": 0,
            "ci_lower": 0,
            "ci_upper": 0
          }
        },
        "skills_score": [
          {
            "name": "Algorithm Design",
            "score": {
              "score": 0,
              "ci_lower": 0,
              "ci_upper": 0
            },
            "summary": "Not evaluated",
            "agents": [
              {
                "name": "Jordan",
                "summary": ""
              },
              {
                "name": "Jordan",
                "summary": ""
              }
            ]
          },
          {
            "name": "Programming",
            "score": {
              "score": 0,
              "ci_lower": 0,
              "ci_upper": 0
            },
            "summary": "Not evaluated",
            "agents": [
              {
                "name": "Jordan",
                "summary": ""
              },
              {
                "name": "Jordan",
                "summary": ""
              }
            ]
          }
        ]
      },
      {
        "question": "Could you briefly describe your approach and the reasoning behind your implementation?",
        "summary": "They chose to skip this question in the interview.",
        "timestamp": "00:03:20.830",
        "question_score": {
          "agent_summary": "Not evaluated",
          "score": {
            "score": 0,
            "ci_lower": 0,
            "ci_upper": 0
          }
        },
        "skills_score": [
          {
            "name": "Algorithm Design",
            "score": {
              "score": 0,
              "ci_lower": 0,
              "ci_upper": 0
            },
            "summary": "Not evaluated",
            "agents": [
              {
                "name": "Jordan",
                "summary": ""
              },
              {
                "name": "Jordan",
                "summary": ""
              }
            ]
          },
          {
            "name": "Programming",
            "score": {
              "score": 0,
              "ci_lower": 0,
              "ci_upper": 0
            },
            "summary": "Not evaluated",
            "agents": [
              {
                "name": "Jordan",
                "summary": ""
              },
              {
                "name": "Jordan",
                "summary": ""
              }
            ]
          },
          {
            "name": "Data Structures",
            "score": {
              "score": 0,
              "ci_lower": 0,
              "ci_upper": 0
            },
            "summary": "Not evaluated",
            "agents": [
              {
                "name": "Jordan",
                "summary": ""
              },
              {
                "name": "Jordan",
                "summary": ""
              }
            ]
          }
        ]
      },
      {
        "question": "Could you share the three to five key technical areas that best represent your expertise?",
        "summary": "They expressed a desire to end the interview.",
        "timestamp": "00:05:18.888",
        "question_score": null,
        "skills_score": []
      }
    ],
    "raw_transcript": "Tracy at Qode: Hello, my name is Tracy. Just before we begin—how's your day been so far?\n\nthanh.le+coding35: Good. Good, Tracy.\n\nTracy at Qode: I'm glad to hear that! Before we continue, please hold your ID card up to the camera so we can take a quick screenshot for verification. Thank you! Great! This will be a technical assessment to evaluate your skills. The assessment will be presented on your screen now. Thanks for confirming you're ready. Here's how the interview will proceed: - There are **2 coding tasks**. - Each task has a **15-minute time limit**. - After the coding tasks, we'll move on to some technical interview questions. - If you get stuck on a task, you can skip it at any time by letting me know. Let's begin. **Task 1 of 2: Special Permutations (Inversions)** You are given an array of the first m natural numbers...\n"
  },
  "overall_score": 70,
  "interview_result": "Pass",
  "count_all_core_domains": 3,
  "count_covered_core_domains": 3,
  "uncovered_domains": [
    "Data & DB",
    "DevOps"
  ],
  "assessment_version": "pf_domain_3p_v4",
  "card_check_image_path": "https://cdn.qode.gg/recording/qode-v3-44e69082-259f-47db-a136-fd92c5d16cc8/passed_1_0.59_card_detected_1772449871458.jpg",
  "demonstrated_skills_across_domains": [
    {
      "domain_name": "Technical Knowledge",
      "domain_category": "Core",
      "observations": 5,
      "domain_weight": 0.4,
      "ci_lower": 70,
      "ci_upper": 75,
      "stability": "Highly Reliable",
      "mean_score_across_skills": 72,
      "skills": [
        "Frontend Development",
        "Backend Development"
      ]
    }
  ],
  "scored_skills": [
    {
      "skill_name": "Frontend Development",
      "pooled_mean_score": 75,
      "ci_lower": 73,
      "ci_upper": 77,
      "stability": "Highly Reliable"
    }
  ],
  "interview_level_bootstrap": {
    "ci_lower": 68,
    "ci_upper": 72,
    "label": "Highly Reliable",
    "interpretation": "The interview outcome is highly precise and reliable."
  },
  "grading_rubric_result": {
    "criteria": [
      {
        "name": "Coding test: 2 tasks",
        "score": 0,
        "weight": 0.4
      },
      {
        "name": "Resume & Technical deep-dive interview with Tracy",
        "score": 0,
        "weight": 0.6
      }
    ]
  },
  "language_proficiency_scores": {
    "coherence_score": 75,
    "fluency_score": 75,
    "grammar_score": 75,
    "pronunciation_score": 75,
    "prosody_score": 75,
    "accuracy_score": 75,
    "completeness_score": 75,
    "summary": "The candidate is proficient in English.",
    "vocabulary_score": 75,
    "language": "en"
  },
  "code_task_result": {
    "tasks": [
      {
        "name": "Task 1",
        "language_name": "Python (3.8.1)",
        "testcase_passrate": "80% (4/5)",
        "code_execution_time": 0.05,
        "implementation_time": 68.4,
        "ram_consumption": 4379,
        "algorithm_complexity": "O(n)",
        "status": "Failed"
      }
    ],
    "coding_score": 0
  },
  "latency_summary": {
    "turn_details": [
      {
        "turn": 1,
        "speaker": "Tracy",
        "latency": 2.5,
        "content": "Hello, my name is Tracy.",
        "start_time": "2025-11-25T10:00:07.933Z",
        "end_time": "2025-11-25T10:00:10.433Z"
      },
      {
        "turn": 2,
        "speaker": "Candidate",
        "latency": 1.8,
        "content": "Hi Tracy, nice to meet you.",
        "start_time": "2025-11-25T10:00:10.433Z",
        "end_time": "2025-11-25T10:00:12.233Z"
      }
    ],
    "median": 2.3,
    "avg_raw": 3.1,
    "avg_clean": 2.4,
    "anomaly_count": 1,
    "anomaly_turns": [
      3
    ]
  },
  "event": "REPORT_GENERATED",
  "timestamp": "2025-11-25T10:16:00.000Z"
}

Documentation Index

Fetch the complete documentation index at: https://docs.qode.world/llms.txt

Use this file to discover all available pages before exploring further.

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

Query Parameters

​
session_id
string

Response

200 - application/json

Interview details retrieved successfully

​
role_title
string
Example:

"Software Engineer"

​
mapped_role_title
string

Mapped role title derived on the role title and interview transcript.

Example:

"Backend Developer"

​
candidate_email
string
Example:

"example@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"

​
session_id
string | null

ID of an interview session

Example:

"4e9e55ee-63f3-4c04-87ec-82a1823a538d"

​
summary
object
​
assets
object
​
metadata
object
​
duration
number | null

Duration of the interview in seconds

Example:

1800

​
interview_signals
object
​
black_screen_timestamps
object[]

Time ranges where a black screen was detected during the interview.

Example:
[
  {
    "start": "00:00:01.600",
    "end": "00:00:03.000",
    "duration": 1.4
  },
  {
    "start": "00:01:42.711",
    "end": "00:01:48.411",
    "duration": 5.7
  },
  {
    "start": "00:01:52.878",
    "end": "00:01:53.911",
    "duration": 1.033
  },
  {
    "start": "00:09:42.366",
    "end": "00:09:48.533",
    "duration": 6.167
  },
  {
    "start": "00:09:49.343",
    "end": "00:09:51.043",
    "duration": 1.7
  }
]
​
meeting_data
object
​
candidate_profile
object
​
agent_response_summary
object[] | null

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

​
overall_score
number | null

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

Example:

70

​
interview_result
string

Overall interview result (e.g., "Pass", "Fail", "Not enough interviews (minimum 10 required) for this role to evaluate Pass/Fail.").

Example:

"Fail"

​
count_all_core_domains
integer | null

Total number of core domains assessed in this interview.

Example:

2

​
count_covered_core_domains
integer | null

Number of core domains that were covered during the interview.

Example:

2

​
uncovered_domains
array

List of domains that were not covered during the interview for that role title.

Example:
["Data & DB", "DevOps"]
​
language_proficiency_scores
object

Language proficiency assessment scores (Only available for BUSINESS_ANALYST, BUSINESS_ANALYST_MCQ and MULTI_BUSINESS_ANALYST)

​
code_task_result
object

Result of code task assessment including individual tasks and overall coding score. (Only for SOFTWARE_ENGINEER_CODING)

​
card_check_image_path
string | null

Path to the identity screenshot

​
demonstrated_skills_across_domains
object[] | null

Skills demonstrated across different domains with confidence intervals and stability metrics.

​
scored_skills
object[] | null

Individual skills with their pooled mean scores and confidence intervals.

​
interview_level_bootstrap
object

Bootstrap confidence interval and interpretation for the overall interview score.

​
grading_rubric_result
object

Result of the grading rubric assessment.

​
assessment_version
enum<string> | null

Version of the assessment.

Available options:
no_ci_v1,
ci_v2,
pf_domain_v3,
pf_domain_3p_v4
​
latency_summary
object

Latency analysis summary for the interview session.

​
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"