Solutions · Synthetic data generation

Synthetic data for the cases you can't capture

Turn a spreadsheet of scenarios into reviewed synthetic video and images — plus synthetic text and audio — to cover rare, dangerous, and private events that real-world collection can't reach.

A lidar point cloud of a city intersection at night, seen from a self-driving car's roof: road, crosswalk, parked cars, a cyclist, and pedestrians traced in points

The problem

Why this is hard to do well

01

Rare events

You can't film a break-in, a near miss, or a storm at sea on demand — but your model has to recognize them.

02

Realism and continuity

Generated clips have to look real and stay consistent from shot to shot, or they teach the model the wrong thing.

03

Cost that compounds

Every render costs money. Without review and limits, failed generations quietly eat the budget.

How MLtwist does it

Platform and people, together

Our platform does the repeatable work; our team, yours, or both handle the judgment calls. Every version ships with a Data ID Card.

Scenarios to scenes
Hundreds of scenario rows are grouped into a few dozen scenes, and a person approves the grouping before anything renders.
Controlled prompts
A fixed camera angle and style applied to every scene, with per-scene changes for light, weather, and action.
Long, continuous video
Short clips are chained end frame to start frame until each scenario reaches the length you need.
Human review and spend limits
Reviewers approve every take, and each scenario has a spending limit so a hard case gets flagged, not retried forever.
Text and audio
Synthetic documents, dialogue, and prompt variants written with LLMs, and synthetic speech and sound for audio models.

Talk to us about synthetic data generation

Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.

Also available through Carahsoft and Google Cloud Marketplace.