The Problem
Synthetic data is critical for training robotics and autonomous systems. But there's no standard way to verify its quality.
Teams generate millions of synthetic images, then discover coverage gaps, physics artifacts, and distribution imbalances only after training — wasting GPU-months on datasets with problems they could have caught upfront. Bad synthetic data produces bad models, and the cost of finding out in production is orders of magnitude higher than finding out before training begins.
What Lucitra Does
Lucitra validates synthetic training data before it trains your models.
Upload datasets from any simulation tool — NVIDIA Isaac Sim, Omniverse, Unreal Engine, or custom pipelines. Lucitra returns structured validation reports scoring coverage completeness, physics plausibility, distribution balance, and sim-to-real transfer confidence. Each report includes specific, actionable recommendations so you know exactly what to fix and how much it will improve your dataset.
Our Values
What we believe
Measure what matters
Validation scores should map to real-world model performance. We focus on metrics that predict how well synthetic data will transfer to production.
Fit into workflows
Validation belongs in your existing pipeline — CI/CD, scripted builds, IDE tooling. Not a separate dashboard you have to check manually.
Ship with confidence
When a dataset scores production-ready, teams should be able to trust that score. We document our methodology and publish benchmarks.
Building autonomous systems? Let's talk.
We're working with teams training robots for warehouses, factories, and roads.