Training data for the problem your model is failing on.
We generate photorealistic synthetic datasets with annotations derived from the scene itself. The point is coverage — the scenarios, the edge cases and the controlled variations that real-world collection cannot deliver efficiently.
Real-world data has limits. These are the four we run into.
Not every project needs simulation. These are the situations where it consistently beats collecting and labelling more real data — and they're not all the same kind of limit. Some are about how fast you can label what you already have; AI-assisted labelling is closing that gap every quarter, and we say so. Others are about whether the condition was ever captured at all — no labelling tool, however good, can annotate a photo that doesn't exist. That second kind is where simulation keeps mattering regardless of how good general-purpose models get.
Edge cases are too rare to learn from
Defects, failures and rare conditions appear too infrequently in real footage to train on reliably. A 10,000-image dataset may contain fewer than 50 examples of the failure mode that matters. Simulation generates those examples deliberately, and at whatever volume the training plan needs. This is the durable case: no model, however good at labelling, can annotate a photo of a condition nobody has recorded yet.
Collection can't keep up with iteration
Every time the object, the environment or the task changes you need new data. Field capture can take weeks even when labelling itself is fast, so your training cycle ends up paced by field access instead of your model. Simulation decouples data generation from field access — useful even alongside AI-assisted labelling tools, which still need the photo to exist first.
Annotation quality is capping the model
Human labelling at scale introduces inconsistency: masks that don't sit on the object, boxes that vary between annotators, classes that flip under ambiguity. That becomes training noise which is very hard to diagnose later. AI-assisted labelling is narrowing this gap on footage you already have; simulation produces ground truth that's deterministic by construction, which still matters most where you don't have the footage to label in the first place.
You need reproducible test conditions
Evaluating a model under "overcast light", "crowded shelf" or "partially occluded" in the real world means waiting for those conditions and hoping they recur. In simulation each condition is a parameter you set, repeat and compare against — and this holds regardless of which model you're validating, a small model you trained or a fine-tuned foundation model you didn't.
Dataset types.
Each maps to a computer vision task. Most projects combine more than one.
Object detection
Bounding box datasets for detecting and localising objects across variable lighting, occlusion, scale and viewpoint — including the negative and hard cases.
Instance & semantic segmentation
Pixel-level masks derived from geometry rather than traced by hand — accurate object boundaries and consistent class separation by construction.
Classification & long-tail balancing
Labelled images across defined categories, with class frequencies set as an input. Fill the rare classes to a target count instead of accepting the distribution you collected.
Anomaly & defect detection
Defects injected procedurally at controlled severity, size and location. Anomalies that occur once in ten thousand real parts become a measurable part of the dataset.
Multi- and hyperspectral imaging
Custom channel configurations for spectral analysis, agriculture and remote sensing — starting from a handful of visual references rather than a spectral capture campaign.
Visual odometry & trajectories
Simulated indoor and outdoor camera paths with ego-motion metadata — for visual SLAM and pose estimation where you control the trajectory instead of hoping it covers the case.
Evaluation & benchmark sets
Controlled test sets built to validate a model before it goes to production — model-agnostic by design, so they work the same whether you're checking a model you trained from scratch or a fine-tuned foundation model you didn't. Most useful for the rare and safety-critical cases where you can't afford to find out in the field.
From your failure modes to a dataset in training.
Four steps, built to remove technical uncertainty early rather than at delivery.
Diagnosis
We start from what your model gets wrong, not from a data request. What is it missing? Under which conditions? What does your validation set actually cover — and what does it structurally not cover?
- Review of current model performance and error analysis
- Definition of target scenarios, classes and edge cases
- Agreement on annotation format, volume and delivery timeline
Scene design
Your problem is rebuilt as a controlled 3D environment — objects, surfaces, lighting and scene dynamics modelled from your references. The variation strategy is defined here: which parameters change, over what range, in what distribution.
- 3D asset creation or adaptation from your visual references
- Parameter space and sampling strategy per class
- Validation frames reviewed with you before full generation
Generation
The simulation runs at volume. Every label is derived from the scene and stays traceable to the parameters that produced it, so any sample can be explained, reproduced or removed.
- Batch generation at configurable volume
- Automatic annotation — boxes, masks, polygons, trajectories, spectral bands
- Delivered in the format your pipeline already loads
Iteration
You train, we look at the results together. New failure modes surface, scene parameters get adjusted, and the next batch targets those gaps specifically — usually in days.
- Driven by residual error from your real validation results
- Additional batches in days, not weeks
- Stops when the model generalises — not when the invoice clears
Three ways teams work with us.
Every one of them starts with the same thing: a small validation batch you can inspect before committing to volume.
Feasibility batch
A small, targeted dataset scoped to your hardest case. Its job is to answer one question — can simulation produce data that moves your model? Cheap to run, fast to judge, no commitment to volume.
Full dataset delivery
Scene build, variation design, generation at volume and annotation in your format, followed by iteration rounds against your real validation results. The typical shape of a first full engagement.
Data partnership
Your catalogue, classes or product line change on a schedule. We keep the pipeline alive so each change becomes a regenerated dataset rather than a new collection project.
Start with the failure mode, not the data order.
Tell us what the model gets wrong. We'll tell you whether simulation is the right answer for it.