Generate realistic, semantic-aware test data for modern applications — from traditional databases to RAG systems and agentic AI.
The only semantic-aware, multi-agent test data generator built for modern applications
Generate realistic names, addresses, and domain-specific data — not random gibberish. Powered by advanced LLMs.
Automatically preserve referential integrity across complex, multi-file datasets with FK detection.
Native support for RAG Q&A pairs, agentic conversation flows, and ML evaluation datasets.
Profile existing production data, learn patterns, and generate statistically similar synthetic data.
Expand beyond your input data — turn 4 product categories into 50 realistic variations.
Comprehensive quality reports with agent reasoning, confidence scores, and compliance checks.
From input to production-ready test data in 4 simple steps
Drag-drop files or describe requirements in plain text
AI agents analyze patterns, relationships & constraints
Multi-agent pipeline creates realistic synthetic data
Download, push to database, or cloud storage
Whether you're starting fresh or working with existing data
Building from scratch? Generate complete test datasets with realistic data, proper relationships, and edge cases — all from a simple schema or description.
// Generated e-commerce data
{
"users": [
{ "id": 1, "name": "Sarah Chen",
"email": "sarah.c@example.com" }
],
"orders": [
{ "order_id": "ORD-001",
"user_id": 1, "amount": 129.99 }
]
}Start free. Scale as you grow.
“DataEcho cut our test data preparation time by 90%. The semantic awareness means we catch bugs that random data never would.”
“Finally a tool that understands RAG testing. The Q&A pair generation with ground truth is exactly what we needed for our eval pipeline.”
“The multi-agent transparency is incredible. We can see exactly why data was generated the way it was — no more black boxes.”
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