DataMakerby Automators

Describe the data. We seed the system.

An AI agent that reads your schemas, generates valid records, and seeds them straight into SAP, your databases and your APIs. Nothing leaves your network.

See DataMaker in action
50+

Built-in data types

90+

MCP tools for your agents

29

PII classes the masker knows

2 min

Avg. time to first data

SAP S/4HANASAP BTPOData V2/V4Tricentis ToscaJira CloudXrayPostgreSQLMSSQLOracleMySQLREST APIsOpenAPIPythonJSONCSV

One agent for the whole test-data lifecycle.

Generation is the easy part. DataMaker also finds the schema, respects the rules, moves the data into the system under test, and proves what landed.

Describe it

Chat a prompt, or point it at a real table and let it write the template for you. Build a schema once and reuse it across every environment.

Chat promptSchema discoveryReusable templates

Deliver it

Native SAP OData, your databases, your endpoints. It moves real data between systems and reconciles what arrived.

SAP OData V2/V4Postgres · MSSQL · OracleREST

Govern it

Runs inside your infrastructure. Anonymise production data and keep it joinable, with an audit trail for every run.

Stays in your network29 PII classesAudited

Drive it from your own tools

An MCP server any coding agent can call, and your own model behind it, including one running locally.

MCP serverBring your own LLMPython scenarios

This is what your team actually works in.

Four screens from a real DataMaker workspace: the template, the systems it talks to, the scenario a pipeline triggers, and the data it left behind.

Build the schema once, see a record before you generate one.

Every field is a typed, locale-aware generator. Flag a field as sensitive and DataMaker masks it at generation time. The live preview updates as you edit.

50+ data typesSensitive fieldsLocale-awarePython per field
DataMaker template builder: the German Customer template with e-mail and IBAN flagged as sensitive, and a live preview of one generated record

SAP, databases and APIs, connected once and used by name.

An SAP S/4HANA system next to a PostgreSQL database and a REST endpoint. Scenarios and the agent reference them by name; credentials never leave your network.

SAP OData V2 & V4PostgreSQL, MSSQL, OracleAny REST APITosca, Jira
DataMaker connections: an SAP S/4HANA QAS system next to a staging PostgreSQL database and a REST orders API

Python scenarios your pipeline triggers, with every run on record.

A scenario generates from the saved templates and seeds the systems behind them. DataMaker draws the flow from the code so a tester can read it without opening the script. Trigger it from CI or Tosca through the API with per-run variables; the history shows who ran it and with which values.

Diagram from the codeFull Python + SDKREST, MCP, chatPer-run variables
A DataMaker scenario named seed_orders shown as a flow: customer template, generate customers, generate orders, push orders to API, insert customers to database, run summary; below it the output of a run GitHub Actions triggered through the API

What was delivered, kept as a locked snapshot.

Generated records are saved as sets: 2,000 business partners loaded into QAS client 300, locked so the regression suite gets the same data next week.

Locked snapshotsJSON, CSV, SQLPush to any targetShared with business users
A locked DataMaker set of 2,000 German business partners with credit limits, loaded to SAP QAS client 300

Stop writing test data by hand.

We wire DataMaker into one of your real systems and show you the data landing where it needs to land.

Discuss a pilotQuestions first? Read the FAQ