textcustomer-supportconversationsintent-classificationsentiment-analysissynthetic-datatraining-dataai-agentsndjson

Labeled Customer Support Conversations

Fixed price

Sample structure: 100 / 100

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Seller: Cipher

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Have an API key? Use the REST API →

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Not documented — confirm reuse terms with the seller
Source / creator: Not documented
Collection method: Supplier description: 10,000 labeled conversation items across 25 support intent categories. NDJSON format with conversation_id, turns (customer/agent dialogues), intent, sentiment, and resolution fields. Synthetic dataset for training and evaluating customer support AI agents. PII-free — all conversations are synthetically generated. License: CC BY - freely usable for commercial purposes. Does not include real customer data; generated for model evaluation and training purposes.
Coverage start: Not documented
Coverage end: Not documented
Data last updated: Not documented
Update schedule: Not documented

Rows are evenly spaced within the inspected input, subject to the preview byte budget; this is not a new random sample of the full dataset. The supplier describes synthetic or modeled records. These should not be treated as verified real-world observations.

Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

Check	Points	Evidence
Populated cells	50 / 50	50 of 50 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types	30 / 30	50 of 50 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape	20 / 20	10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.

Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

Field	Missing cells	Most common type	Other populated types
conversation_id	0 / 10	string	0 / 10
turns	0 / 10	array	0 / 10
intent	0 / 10	string	0 / 10
sentiment	0 / 10	string	0 / 10
resolution	0 / 10	string	0 / 10

How the score is calculated, its limitations, and how to correct an assessment →

About this data

Synthetically generated multi-turn dialogues with intent, sentiment, and resolution labels across 25 support categories. Designed for training and evaluating customer support AI agents.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py dd4a238a-b2ed-4905-9971-dd0eedf8915a --output dataset.bin

Full supplier documentation

Original supplier listing: Labeled Customer Support Conversations - 10K Multi-turn Dialogues with Intent & Resolution 10,000 labeled conversation items across 25 support intent categories. NDJSON format with conversation_id, turns (customer/agent dialogues), intent, sentiment, and resolution fields. Synthetic dataset for training and evaluating customer support AI agents. PII-free — all conversations are synthetically generated. License: CC BY - freely usable for commercial purposes. Does not include real customer data; generated for model evaluation and training purposes.

Schema

Name	Type	Description
conversation_id	string
turns	array
intent	string
sentiment	string
resolution	string

Sample Data

Download a free sample to inspect the data before purchasing.

Public sample only. Sign in to retrieve the full dataset, including free datasets.

For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Labeled Customer Support Conve" })
// Found: dd4a238a-b2ed-4905-9971-dd0eedf8915a
buy_now({ dataset_id: "dd4a238a-b2ed-4905-9971-dd0eedf8915a" })  // uses your saved card

Via REST API

# Authenticate with your API key:
curl https://api.databazaar.io/datasets/dd4a238a-b2ed-4905-9971-dd0eedf8915a \
  -H "Authorization: Bearer YOUR_API_KEY"

# Purchase:
curl -X POST https://api.databazaar.io/datasets/dd4a238a-b2ed-4905-9971-dd0eedf8915a/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "dd4a238a-b2ed-4905-9971-dd0eedf8915a"}'