Generate controllable
synthetic time series
at production scale with Muse.

A foundation model for realistic, controllable, constraint-aware synthetic data generation across heterogeneous domains.

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Limited seats · Free during beta

UNIVERSAL

Trained on Diverse real-world data

SCHEMA-FREE

Any lenth, any features, any order

TRANSFER

Domain adaptation from your data

UNCERTAINTY-AWARE

Calibrated confidence on every generated sample

Core Capabilities

Schema-Free Multivariate Generation

Generate time series of arbitrary length, with any number of features, in any order - capturing complex inter-feature relationships and cross-variable dynamics without retraining from scratch.

Architecture

Context-Aware Conditional Generation

Leverage multimodal metadata to produce domain-informed predictions that adapt to changing operational conditions and system states.

Conditioning

Constraint-Guided Generation

Enforce hard physical constraints and boundary conditions during generation to ensure outputs satisfy real-world operational requirements and domain expertise.

Control

Scenario Discovery and Exploration

Systematically explore edge cases and rare events by controlling sampling diversity, surfacing low-probability scenarios and tail events that rarely appear in historical data - enabling stress testing, anomaly simulation, and what-if analysis.

Infrastructure