Solutions · Synthetic data generation
You define the scene. We bring it to life.
Tell us the scenarios your model needs to see — the rare, dangerous, and private events real-world collection can't reach. Our team generates them as reviewed synthetic video, delivered in your format with a Data ID Card on every file.
Overview
When generated data earns a place in your training set
Synthetic data is training data that’s generated rather than recorded: video, images, text, or audio produced by models to show situations you need but can’t easily capture. It exists because real-world collection has limits. Some events are rare enough that you’d wait years for enough examples. Some are dangerous to stage. Some can be filmed but not used, because the footage reveals where people live, work, or travel.
The catch is that generated data can teach a model the wrong things as easily as the right ones. A clip where the camera wanders, a car changes color between frames, or rain falls from a clear sky is worse than no clip at all. Behavior matters too: a route that looks realistic but brakes at impossible speeds will mislead any model that learns motion from it. And because every render costs money, a program without limits can spend most of its budget failing on a handful of hard scenarios.
That’s why we treat synthetic data as a production job, not a prompt. You tell us the scenarios you need. We group them into scenes, lock one look across each scene, and vary only what should vary — light, weather, and action. Short generated clips are chained into continuous video, a person approves every finished video, and each file is recorded as synthetic on its Data ID Card. The work runs on the MLtwist platform; here’s how the workflow runs.
Synthetic data works best alongside real data, not instead of it. A few real samples show what the generated set should look like and how it should behave; generation then fills in the conditions and events those samples don’t cover. If you still need the real samples, start with data collection, or read how synthetic routes were built for autonomous utility trucks.
The problem
Why this is hard to do well
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. Waiting for real examples to accumulate can take years, and some events should never be staged at all.
Realism and continuity
Generated clips have to look real and stay consistent from shot to shot. If the camera drifts, objects change shape between frames, or the lighting ignores the weather, the model learns artifacts of the generator instead of the world.
Cost that compounds
Every render costs money, and hard scenarios often fail several times before one take is usable. Without review and limits, retries on a few difficult cases quietly eat the budget meant for the rest of the set.
How it works
You describe it, we make it
You tell us what your model needs to see. We handle the generation, the consistency, and the review, and deliver finished video you can train on.
- 01
Describe the scenes
Send the scenarios you need, as a spreadsheet or a list — the setting, the action, and the conditions. A few real photos or clips from your camera help us match the view your model will see in production.
- 02
We plan the set
We group your scenarios into scenes that share a setting and agree the plan with you before anything is generated, so the budget goes to the scenes you actually want.
- 03
We lock the look
Each scene starts from one reference image, matched to your camera's viewpoint. Variations change only what they should: light, weather, and the action — so differences in the data are differences you chose.
- 04
We generate and assemble
Generators produce short clips. We chain them, each continuing from the last frame of the one before, fix takes that drift, and join them into video of the length each scenario needs.
- 05
We review and deliver
A person approves every finished video before it counts. Approved videos are delivered as a versioned set in your format, each recorded as synthetic on its Data ID Card.
Case study · Technology company
Synthetic commutes in sun, fog, and rain, from a handful of real traces
A major technology company needed commute data that looked and behaved like real travel — weekday and weekend patterns, morning and evening peaks, different parts of a city — to train mobility and navigation models. It had only a few real traces, and it couldn't use identifiable location data.
MLtwist built a pipeline that generated varied routes from those samples, applied rain, fog, snow, sunrise, and night lighting through controlled transformations, and checked every route for logical speed patterns, timestamps, and consistency. New route types and areas were added as the client's modeling grew.
The client received high-fidelity synthetic commutes that captured real-world complexity without exposing personal information. Its team spent less time fixing data issues, model development moved faster, and consistent weather and lighting variations made its transportation models and simulations more robust.
Read the case studyWhat you get
Synthetic data you can put in a training set
Generated data gets the same review and paperwork as real data.
-
Reviewed, full-length video
Every video is approved by a person, at the length and resolution you asked for. Rejected takes never reach your set.
-
Controlled variation
Light, weather, and time of day are varied on purpose, scene by scene. You know what each video covers, so you can balance the set instead of guessing.
-
In your format
Delivered to your storage, named and tagged the way your spreadsheet says, with its metadata carried through.
-
A Data ID Card
Each file is recorded as synthetic, with the scenario, scene, and prompts behind it. Real and generated data can sit in the same training set without anyone losing track of which is which.
FAQ
Common questions
When should we generate data instead of collecting more?
When the event is rare, dangerous, or private — a near miss, a break-in, a storm at sea — or when you need controlled variations of scenes you already have. If the condition happens often and safely, collecting it is usually simpler.
Can synthetic data be mixed with real data?
Yes, and that's the common case. For one autonomous trucking company, a few real neighborhood routes became a broad synthetic set with morning light, midday glare, fog, and rain added. The real samples anchor what the generated routes should look and behave like.
What do you need from us?
The scenarios you need and the conditions your model has to handle, ideally as a spreadsheet with one row per video. Example photos or clips from your own cameras help us match the look.
How do you keep costs predictable?
Scenarios are grouped into scenes and the plan is agreed before anything is generated. Every scene, frame, and clip gets a set number of attempts, so a hard case is flagged for a decision instead of burning budget on retries.
How do you know the generated data is realistic?
A person reviews every finished video before it's delivered. In our synthetic mobility programs, automated checks also tested behavior as well as appearance — speed patterns, timestamps, stop frequency, direction, and route consistency. Data that looks right but moves wrong doesn't pass.
Does it protect privacy?
Synthetic routes follow real movement patterns without copying real trips, so models can train without identifiable location data. Both of MLtwist's synthetic mobility programs were built around that requirement.
Can you generate more when our needs change?
Yes. The pipelines are built for iteration. In past programs, new route types, areas, and neighborhood types were added as the client's models evolved.
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.