Retrieves the current status and results of a batch evaluation job

Get Data Quality Batch Job Status

Score between 0-100 for this quality issue.

The higher the score, the greater the likelihood and severity of the quality issue.

Types of quality issues that can be detected in survey responses.

* `Nonsensical` - Response lacks logical meaning or sense. Likely gibberish.
* `Irrelevant` - Response does not address the question.
* `Low Effort` - Respondent did not put in much effort to answer the question. Lacks detail or concrete examples.
* `LLM Generated` - Response appears to be generated by AI.
* `Self Duplicated` - Responses from the same respondent contain duplicated content across multiple answers. Only evaluated when multiple survey entries are provided or if a survey identifier and response identifier is provided.
* `Shared Duplicate` - Responses contain duplicated content across different respondents for the same question. Only evaluated if survey identifiers and question identifiers are provided.

A score between 0 and 100 that represents the overall quality of the survey response.

The score is an average of the quality scores across all survey entries.

The higher the score, the greater the likelihood and severity of data quality issues.

A score between 0 and 100 that represents the overall quality of the survey response.

The score is an average of the quality scores across all survey entries.

The lower the score, the greater the likelihood and severity of data quality issues.

Optional identifier for the survey response. Aftercare can use this to asscociate survey entries for a single response.

If a response identifier is not provided, Aftercare will generate one.

The answer to the question provided by the respondent

Optional string. If provided, Aftercare will check if the response contains any honey pot words.

Optional identifier of a previous question in a follow-up thread

The question that was asked to the respondent

A description of what you are trying to learn from the respondent for this question

Optional unique identifier for the question.

Aftercare will use this to build a data model to track questions across a survey

A score between 0 and 100 that represents the overall quality of the individual survey entry.

The score is a weighted average of the quality issue scores.

The higher the score, the greater the likelihood and severity of data quality issues.

If Aftercare has detected any quality issues for this survey entry

A score between 0 and 100 that represents the overall quality of the individual survey entry. (Deprecated: use demeritScore instead)

Physical key code (e.g., "KeyA", "Enter")

Keydown timestamp (from performance.now())

ApiKeyAuth

Get batch job status

Starter Kit

Introduction

Learn how Aftercare models your survey data and provides analytics

Aftercare Data Model

Languages currently supported by Aftercare

Supported Languages

Get started generating smart followup questions on-the-fly using AI

AI Followups

Our more powerful model. Code complex themes in your survey responses with AI

AI Thematic Coding

Our fastest and most cost effective model. Code brands with AI

AI Brand Coding

Evaluate the quality of your survey responses with AI

AI Data Quality Analysis

Get Started

Generates a contextual followup question based on a survey response

Create AI Followup Question

Evaluates the quality of survey responses for potential issues

Evaluate Data Quality

Asynchronously evaluates the quality of multiple survey responses

Evaluate Data Quality Batch

Analyze survey responses and code them into hierarchical categories

Create AI Coding Batch Job

Retrieves the current status and results of a batch coding job

Get AI Coding Batch Job Results

Analyze survey responses in the context of specified brands and code them into hierarchical categories

API Documentation

AI Followup Generation

AI Data Quality Evaluation

AI Coding

Get Data Quality Batch Job Status

Authorizations

Path Parameters

Response