AI-Powered Test Data Generation

Make test data in seconds.
AI that understands your system.

Generate, seed, and automate realistic test data for databases, APIs, and SAP systems. No more hand-crafted fixtures. No more stale staging data.

DataMaker · ChatAgent mode

Create 50 SAP Business Partners with realistic German addresses and seed them into our system.

Reading SAP OData $metadata

Auto-generating template from entity fields

Generating 50 records (de_DE locale)

POSTing to /API_BUSINESS_PARTNER_SRV

Verifying inserts

Start for free →See how it works ↓

50+

Built-in data types

Database connectors

25+

MCP tools

2 min

Avg. time to first data

Works with the tools your team already uses

PostgreSQLMySQLMongoDBMSSQLIBM DB2OracleSAP S/4HANASAP BTPOData V2/V4PythonOpenAPIREST APIsJSONCSV

Regression-Ready Data

Pull real SAP records. Ready for regression.

Sometimes you don't need new data — you need real records that match the shape of a test case. Hand the agent a longer prompt with explicit filters and field mappings, and it queries your SAP system, returns a typed table, and saves the set for your regression portfolio.

datamaker.automators.com · Chat · regression_bp_deAgent mode

Pull 25 existing SAP Business Partners from
our S/4HANA sandbox for the regression suite.
Filter and field mapping:

  • Country:        DE (90%) · AT (10%)
  • BPCategory:     1 (Person)
  • Industry:       HIGH_TECH or RETAIL
  • BPRole:         FLCU01 (Customer)
  • CreatedOn:      last 90 days

  Required fields: BP ID, Name, Country,
  Industry, TaxNumber1 (USt-ID), IBAN.

Save the set as "reg_bp_de_2026q2".

Inspecting $metadata · 47 fields discovered

Building OData $filter from your mappings

GET /A_BusinessPartner?$filter=…&$top=200

318 matched · sampling 23 DE / 2 AT

Resolved related entities · Address · BankDetails

✓ 25 existing BusinessPartners returned · saved as reg_bp_de_2026q2

Refine the set, add a follow-up entity, or push to another tenant…↑

A_BusinessPartner · 25 rows · reg_bp_de_2026q2Fetched ✓

Fetched from S/4HANAA_BusinessPartner

25returned

318matched

23/2DE / AT

BP ID	Name	Cty	Industry	USt-ID	IBAN	Status
1000147	Lukas Huber	DE	HIGH_TECH	DE 273 612 449	DE89 3704 0044 …	200 OK
1000148	Sophia Weber	DE	RETAIL	DE 119 458 217	DE21 5001 0517 …	200 OK
1000149	Markus Bauer	DE	HIGH_TECH	DE 814 005 933	DE17 7002 0270 …	200 OK
1000150	Anna Schmidt	AT	HIGH_TECH	ATU 6743 1209	AT48 3200 0000 …	200 OK
1000151	Jonas Müller	DE	RETAIL	DE 502 117 826	DE60 4306 0967 …	200 OK
1000152	Lea Fischer	DE	HIGH_TECH	DE 991 423 580	DE12 1003 0500 …	200 OK

+ 19 more rows · all 200 OK · 318 total matched

Full Feature Set

Everything you need to generate quality test data.

DataMaker combines a no-code template builder, an AI chat agent, and a Python scenario engine — so every team member can contribute, regardless of skill level.

AI Chat Interface

Chat to generate data instantly

Describe what you need in plain English. DataMaker's AI agent writes the schema, generates the data, and pushes it straight to your database or API endpoint — all from a single prompt.

User prompt
AI understands schema
Data generated
Exported / seeded

50+ Data Types

Realistic data for every field

Choose from over 50 built-in generators — names, emails, addresses, IBANs, credit cards, SSNs, IMEIs, products. Need something custom? Use AI, Python, or your own regex.

First NameE-MailIBANCredit CardPhoneAddressUUIDCompanyDateBooleanProductJob TitlePasswordIPv4ColorSSN

+ Custom types, AI-generated fields, and Python scripts

Reusable Templates

Build once, generate forever

Save your data schema as a reusable template. Share templates across your team, nest objects for complex JSON structures, and generate any number of records on demand — via the UI, API, or AI agent.

Define template fields
Save template
Generate N records
JSON · CSV · DB · API

DB & API Connections

Seed databases and APIs directly

Connect to PostgreSQL, MySQL, MongoDB, MSSQL, Oracle, IBM DB2. Or configure REST API endpoints with custom headers and auth — and push generated data straight in.

PostgreSQLMySQLMongoDBOracleDB2REST API

Native SAP OData

First-class SAP integration

DataMaker speaks SAP natively. Connect to any SAP OData V2 or V4 service, inspect entity fields from $metadata, apply OData filters, and POST generated records back — with automatic CSRF token handling.

OData V2 & V4 (A2X)
Auto-CSRF token handling
Fetch, filter & inspect SAP data
SAP-specific MCP tools

Python Scenario Engine

Automate complex data workflows

Write Python scenarios to orchestrate multi-step data generation: call APIs, run validations, chain templates, and seed multiple systems in one automated run. Stored, versioned, executable from UI or agent.

Step 1 — Generate customers (Template A)

Step 2 — Create orders referencing customer IDs

Step 3 — POST to staging REST API

Step 4 — Insert into PostgreSQL

✓ Complete — 1,000 records seeded

MCP Server

Plug DataMaker into any AI agent

DataMaker ships an MCP server with 25+ tools. Connect it to Claude, Copilot, Cursor, or any MCP-compatible agent so your AI coding assistant can generate and insert test data without leaving the editor.

Cursor · Claude · Copilot

↓ MCP protocol

DataMaker MCP Server

generate_from_id · export_to_endpoint · execute_scenario

Your Database · API

Analyse Existing Data

Turn real data into templates automatically

Upload a JSON, CSV, YAML, or OpenAPI file — or point DataMaker at a live database — and let AI reverse-engineer a reusable template from your existing schema. No manual mapping required.

Upload CSV / JSON / OpenAPI
AI analyses column types
Template auto-generated
Generate N synthetic rows

Data Masking & Privacy

Anonymise sensitive data safely

Mask or replace PII fields in your templates. Mark fields as sensitive to prevent accidental export. DataMaker helps you stay GDPR-compliant while still working with realistic, production-shaped data.

Mask PII & PHI fields
GDPR-compliant generation
Prevent accidental export
Production-shaped synthetic data

How It Works

From prompt to production data in three steps.

STEP 01

Describe

Type a prompt or upload your schema. Tell DataMaker what kind of data you need and how much.

STEP 02

Generate

DataMaker's AI builds the template, picks the right data types, and previews the output live. Tweak fields or let the agent iterate for you.

STEP 03

Deliver

Export to JSON, CSV, XLS, or SQL — or push data directly to your database, REST API, or SAP system in one click.

datamaker.automators.com · Templates · German Customer

Field nameData typePreview

"Lukas"

"lukas.huber@example.de"

"Müller GmbH"

"DE89 3704 0044 0532 0130 00"

1485.20

Live PreviewDependencies

results: [
  {
    first_name: "Lukas",
    email: "lukas.huber@…",
    company: "Müller GmbH",
    iban: "DE89 3704…",
    balance: 1485.20
  },
  {
    first_name: "Sophia",
    email: "sophia.weber@…",
    company: "Bauer AG",
    iban: "DE21 5001…",
    balance: 2104.55
  }
]

5 fields · 100 recordsJSON

Templates & Live Preview

Build data schemas visually, preview instantly

The DataMaker template builder gives you a drag-and-drop field editor with live preview. Add nested objects, set distributions, apply custom Python logic per field, and see realistic sample data update in real time.

50+ built-in data types
Nested JSON objects & arrays
AI-generated fields
Distribution controls (Gaussian, weighted random)
Python script per-field generation
Custom & reusable data types

datamaker.automators.com · Scenarios · seed_orders.py Run

1from datamaker import DataMaker
2import requests
3
4dm = DataMaker()
5customers = dm.generate_from_id(
6    template_id="cust_de", count=500)
7
8for c in customers:
9    requests.post(
10        "https://staging.api/orders",
11        json={"customer": c["id"]})
12
13print(f"✓ seeded {len(customers)} orders")

Output (Live):25% height

→ Generating 500 customer records (template: cust_de)…→ POST https://staging.api/orders ✓ 200 OK ×500✓ seeded 500 orders

Scenario Engine

Automate multi-step data pipelines with Python

DataMaker's scenario engine lets you write Python scripts that orchestrate complex data flows — generate data, call APIs, seed databases, run assertions — and save them as reusable, shareable scenarios. Execute on demand or trigger via API.

Full Python environment
Access DataMaker API from within scripts
Workspace file storage (upload CSVs, reference data)
Real-time log streaming during execution
AI agent can write and debug scenarios for you

datamaker.automators.com · Connections · API_BUSINESS_PARTNER_SRV

SAP

API_BUSINESS_PARTNER_SRV

OData V2 · S/4HANA Cloud sandbox

Connected

Endpoint

/sap/opu/odata/sap/
API_BUSINESS_PARTNER_SRV

Auth

Basic · sandbox-user

CSRF token

Auto-fetched · valid

Entity sets

A_BusinessPartnerA_BPAddressA_BPBankDetails

Last run · seed_business_partners.py10 / 10 ✓

POST/A_BusinessPartner201

POST/A_BPAddress201

+ 5 more · all 201

SAP & Enterprise Systems

Purpose-built for SAP environments

Unlike generic data tools, DataMaker understands SAP OData services out of the box. Connect your SAP BTP or S/4HANA system, fetch entity metadata, apply OData filter queries, and POST generated test data back — including automatic CSRF token negotiation.

OData V2 & V4 (A2X)
Auto-CSRF token handling
Fetch, filter, and inspect existing SAP data
Export generated data to SAP entities
SAP-specific MCP tools for agent-driven workflows

SAP + AI Agent

Seed SAP test data with a single agent prompt.

Here's how DataMaker's AI agent fills a SAP S/4HANA system with realistic Business Partners, Sales Orders, or any OData entity — end to end, hands-free.

01"Create 10 German Business Partners in SAP"
02Agent reads SAP OData $metadata
03Template auto-generated (name, address, IBAN…)
0410 realistic records generated
05CSRF token fetched automatically
0610 × HTTP POST → SAP OData endpoint
07Business Partners live in your SAP system

DataMaker AI Agent · 5 MCP Tool Calls

fetch-endpoint-fields

Reads $metadata from your SAP OData endpoint — discovers fields, types, and required flags automatically.

get_templates / create_template_from_json

Checks for an existing SAP Business Partner template or auto-creates one mapping OData properties to DataMaker types.

generate_from_id

Generates 10 realistic, locale-aware Business Partner records using German names, postal codes, and IBANs.

save_scenario

Writes a Python scenario that loops through records and POSTs each one to the SAP OData endpoint with CSRF token handling.

execute_scenario

Runs the scenario in DataMaker's Python worker. Live logs stream back to the chat UI in real time.

Pricing

Start free. Scale when you're ready.

Free

Generous free tier
3 templates
JSON & CSV export
1 project
Community support

Start for free →

Pro

Unlimited generation
Unlimited templates
All export formats
Unlimited projects
Email support
DB & API connections
Scenario engine

Start for free →

Enterprise

Custom limits
Unlimited templates
All export formats
Unlimited projects
Dedicated support
SAP integration
Agent mode + MCP

Talk to sales →

What Teams Say

Trusted by developers & QA teams.

We cut our test data setup time from half a day to under 10 minutes. DataMaker's agent just… gets it.

— QA Lead, FinTech startup

Finally a tool that understands SAP OData. We seed 500 Business Partners into our S/4 sandbox before every sprint demo.

— SAP BTP Consultant

The MCP integration means our Cursor-based AI can generate and insert test data without ever leaving the editor.

— Senior Backend Engineer

FAQ

Frequently asked questions.

Ready to stop writing test data by hand?

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