◆ Now in Public Beta

The Synthetic Data Universe

Generate entire datasets that never existed. Realistic user behavior, market scenarios, and training data — on demand.

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10B+

Data points generated

99.7%

Statistical accuracy

150+

Enterprise clients

What We Create

Datasets Built From Scratch

Realistic synthetic data designed for modern AI teams and data-driven companies.

⬡

User Behavior Data

Simulate clicks, sessions, purchases, and engagement patterns that mirror real-world user activity.

◧

Market Scenarios

Generate financial, economic, and market condition datasets to stress-test models and strategies.

◉

AI Training Datasets

Build labeled, structured datasets at scale so you can train machine learning models safely and efficiently.

datasets generated 2,847,391
rows today 184M
active users 12,408
API calls/min 34,200
uptime 99.99%
countries 89
avg generation 0.4s
schemas 5,230
datasets generated 2,847,391
rows today 184M
active users 12,408
API calls/min 34,200
uptime 99.99%
countries 89
avg generation 0.4s
schemas 5,230
generate.py
from omex import SyntheticEngine
# Initialize the generator
engine = SyntheticEngine(api_key="omx_live_...")
# Define your schema
schema = engine.schema({
"user_id": UUID,
"age": Range(18, 65),
"purchases": Poisson(λ=4.2),
"churn_risk": Beta(2, 5),
"segment": Choice(["new", "active", "vip"]),
})
# Generate 1M rows in seconds
dataset = engine.generate(
schema=schema,
rows=1_000_000,
format="parquet"
)
print(f"Generated {dataset.rows} rows")
# → Generated 1,000,000 rows in 0.8s
// SDK

Generate a Million Rows in 4 Lines of Code

Our Python SDK lets you define schemas with statistical distributions and generate massive datasets instantly. No data cleaning, no privacy reviews, no waiting.

Supports Parquet, CSV, JSON, and direct streaming to your data warehouse via API.

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Trusted by teams at
Why Omex

Built for the AI Era

Companies use Omex to move faster, stay compliant, and unlock possibilities that real data can't provide.

▣

Privacy-First

No real user data needed. Stay compliant with GDPR, CCPA, and HIPAA by design.

↯

Instant Scale

Go from 1,000 to 1 billion rows in minutes. No collection, no cleaning, no waiting.

◎

Statistically Accurate

Our models preserve distributions, correlations, and edge cases from the real world.

⟁

API-First

Integrate seamlessly into your data pipelines with our REST and streaming APIs.

◇

Simulate the Future

Generate data for scenarios that haven't happened yet to prepare for what's next.

⬢

Enterprise-Grade

SOC 2 compliant, SSO, role-based access, and dedicated infrastructure.

Platform Growth

Numbers That Speak

Omex is growing fast — and so are the teams building with our data.

0
Billion data points generated
+340% YoY
0
Active users worldwide
+180% YoY
0
Statistical fidelity score
Industry leading
0
Seconds avg generation time
−62% since v2
User Growth

Exponential Adoption Since Launch

From 120 beta users in Q1 2024 to over 12,000 active teams today. Every quarter, more companies discover the power of synthetic data.

120
480
1.2K
2.8K
4.5K
6.9K
9.1K
12.4K

103×

User growth in 2 years

89

Countries represented

47%

Are Fortune 500 companies

92%

Monthly retention rate

Overall Satisfaction 97%
Data Quality Rating 99%
Would Recommend 94%
API Performance 98%
What Users Say

Loved by Data Teams

Engineers, data scientists, and product leaders trust Omex to power their workflows.

■■■■■
We replaced 6 months of data collection with a single Omex API call. Our ML pipeline went from blocked to production in two weeks.
MK
Maya Kim
ML Lead, Fintech Startup
■■■■■
The statistical fidelity is remarkable. Our fraud detection model trained on Omex data performs within 2% of models trained on real data — with zero privacy risk.
DR
Daniel Reeves
Head of Data, Banking Corp
■■■■■
We use Omex to populate our staging environments with realistic data. QA cycles are 10× faster and we never worry about exposing customer data.
AS
Aisha Saeed
VP Engineering, E-Commerce

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Join 150+ companies already using Omex to power their AI with synthetic data.

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