Platform
One platform for the entire physical AI lifecycle
From synthetic data generation to production deployment — Lucitra connects every stage of the autonomous systems development pipeline.
Lucitra Validate
Certify training data quality
Score synthetic datasets for coverage, physics plausibility, and sim-to-real transfer confidence. API, CLI, dashboard, and simulation plugins.
Lucitra Studio
Orchestrate AI development
Desktop mission control for managing agent teams, worktree isolation, and automated development workflows. Built on Claude Code.
Lucitra CLI + MCP
Integrate everywhere
Command-line tools and Model Context Protocol server for embedding validation into CI/CD pipelines, IDE plugins, and AI agent workflows.
Lucitra Validate
Real scores from real pipelines
These are actual validation results from our pipeline — not fabricated demos.
Warehouse Robotics
Isaac Sim · 1,240 scenes
63
Overall
74
Coverage
42
Physics
72
Distribution
60
Sim-to-Real
Key gaps identified:
CARLA-COCO
CARLA · 5,000 scenes
51
overall
SynTable
Isaac Sim · 3,200 scenes
58
overall
Validation Engine
Know what's in your training data
Coverage Analysis
Detect gaps in object classes, spatial coverage, viewpoint diversity, and scale distribution before they become blind spots in production.
Physics Validation
Flag floating objects, interpenetrating meshes, unrealistic shadows, and other physics violations that degrade model performance.
Sim-to-Real Scoring
Quantify how well your synthetic data will transfer to real-world conditions with a single confidence score.
Structured Reports
Get JSON reports with per-category breakdowns, severity-ranked violations, and specific recommendations to improve your dataset.
How It Works
From simulation to production
Generate
Create synthetic datasets in Isaac Sim, Omniverse, or any simulation tool. Export as COCO, KITTI, or USD.
Validate
Run Lucitra Validate via API, CLI, or CI/CD. Get scored reports on coverage, physics, and sim-to-real confidence.
Iterate
Use gap analysis to fill coverage holes, fix physics violations, and improve distribution balance. Re-validate until your data meets the bar.
Deploy
Ship certified datasets to training. Track data provenance, generate compliance reports, and monitor for drift in production.
See It In Action
From simulation to validated dataset
Generate synthetic data, validate it against production standards, and review detailed reports — all in one pipeline.
Generate
Render synthetic training data in Isaac Sim, CARLA, or any simulation environment.
Validate
Run lucitra validate via CLI, API, or GitHub Action. Get scored results in seconds.
Review
Browse detailed reports with coverage gaps, physics violations, and improvement recommendations.
Integrations
Works with your simulation tools
Upload datasets from the tools you already use. COCO, KITTI, USD, and custom formats supported.
NVIDIA Isaac Sim
Robotics Simulation
NVIDIA Omniverse
Digital Twins
Unreal Engine
Game Engine
Universal Scene Description
USD / OpenUSD
Use Cases
Built for teams training robots
Warehouse Robotics
Validate pick-and-place training data for object recognition, spatial coverage, and lighting variation across warehouse environments.
Autonomous Vehicles
Score synthetic driving datasets for weather diversity, pedestrian coverage, edge-case representation, and sensor simulation fidelity.
Factory Automation
Ensure assembly line training data covers all part variants, orientations, defect types, and conveyor positions.
Ready to certify your training data?
Stop discovering dataset gaps after training. Catch them before you burn GPU hours.