Describe the data. Get the rows.

Synthetic Data home →

Every company job, newest first, with its target, a live status and rows generated against rows requested. Quick generate and New dataset sit top right.

New dataset: target →

Choose an existing table, a new table, or a dataset file in JSONL or CSV, with the training pairs preset one click away.

New dataset: existing table →

Pick any table you can access and see its schema, with media columns flagged as not generatable.

New dataset: schema →

Propose a schema from the description, then refine it column by column.

Add column →

Name, type, description, allowed values and a required flag, with name checks as you type.

Quick generate →

Up to 50 rows on screen in seconds, nothing stored, download as CSV or JSONL.

Dataset page: generating →

A progress bar, active workers, the time of the last progress, and every batch with its status and rejected count.

Dataset page: review →

Staged rows in a typed grid, batch by batch, with Regenerate batch, Append more rows, Discard and Approve & write.

Row detail →

Every value of one row, with its column type, and an Exclude row button.

Append more rows →

Add rows to a job; the new ones reuse its schema and skip what it already made.

invoice_no,customer,amount,currency,status
INV-20418,Fenwick Supply,1290.50,USD,Paid
INV-20419,"Ortega, Lane & Co",0.00,USD,Void
INV-20420,Kestrel Labs,-45.00,EUR,Credit
INV-20421,Blue Fern Cafe,18990.00,USD,Overdue

Finished: file or table →

Export the dataset file through a temporary link, or open the filled table.

Statuses and shortfalls →

Nine plain statuses, and a list of reasons whenever a job finishes with fewer rows than requested.

# 1. Draft columns from a description (creates nothing)
autessa synthetic-data propose-schema \
  --description "Customers of a project-management SaaS, with realistic signup notes"

# 2. Start a job: a JSONL dataset file by default, staged for review here
autessa synthetic-data create \
  --name "Demo customers" \
  --description "Customers of a project-management SaaS, with realistic signup notes" \
  --rowCount 500 \
  --writeMode REVIEW \
  --columnDefinitions '[{"columnName":"company","columnType":"SHORT_TEXT","required":true},
    {"columnName":"plan","columnType":"SINGLE_SELECT","allowedValues":["Starter","Team","Business"]},
    {"columnName":"signup_note","columnType":"LONG_TEXT"}]'

# 3. Poll until AWAITING_REVIEW, then review, approve and download
autessa synthetic-data get --jobId <jobId>
autessa synthetic-data preview --jobId <jobId> --batchIndex 0
autessa synthetic-data approve --jobId <jobId>
autessa synthetic-data export --jobId <jobId>

curl https://api.autessa.com/clients/synthetic-data/generate \
  -H "Authorization: $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "description": "Support tickets for a meal-kit delivery company",
    "rowCount": 10,
    "columnDefinitions": [
      {"columnName": "subject", "columnType": "SHORT_TEXT", "required": true},
      {"columnName": "channel", "columnType": "SINGLE_SELECT",
       "allowedValues": ["Email", "Chat", "Phone"]}
    ]
  }'

# → { "rows": [ ... ], "rowCount": 10, "rejectedRowCount": 1 }

Engineering and QA

Seed staging tables and generate fixture files that match your schema, including the awkward edge cases.

AI and agent teams

Create prompt and response training pairs, and realistic test conversations for assistants.

Sales and solutions

Fill a demo environment with believable customers, tickets and orders in minutes.

Product and design

Prototype apps and dashboards against real-looking data instead of lorem ipsum.

Data and analytics

Exercise pipelines and reports with varied rows before real data arrives.

Training and enablement

Build practice datasets for onboarding without exposing customer records.

Integrations

Where Synthetic Data plugs in

Your Autessa database tables, JSONL and CSV files, the Autessa CLI and REST API, and the model your company chooses, including your own provider connection.

Security

Your data rules apply to test data too

Jobs are scoped to your company. Descriptions, sample rows and staged rows are encrypted with your company key, table writes use your own permissions, and company PII and PHI stripping applies to prompts.