NVIDIA stack
Built around Isaac Sim, OpenUSD, Replicator, DGX/HGX, Jetson, TensorRT, and NIM.
Physical AI data factories
Physical-AI data factories for robots that must work in reality.
SimMint mass-produces site-faithful, perfectly-labeled robot training data and closes the sim2real gap with a generate → calibrate → evaluate → curate loop.
Warehouse AI CoNorthstar OEMVector RoboticsAtlas FulfillmentKinetic LabsAisleWorks
SimMint system
The robot-data operating system enterprises expected from simulation.
SimMint adapts Flank’s dark editorial confidence to Physical AI infrastructure.
Calibrated worlds
Scenes match customer layout, lighting, sensors, SKUs, motion, and edge cases.
Born labeled
RGB, segmentation, depth, force, trajectories, and failure metadata without manual annotation.
Workflow
Closed-loop robot data operations
SimMint replaces one-off data projects with an operating loop that continuously improves robot models.
Generate
Produce perfectly labeled synthetic scenes.
Calibrate
Match sites, sensors, light, materials, and motion.
Evaluate
Score transfer and classify failures.
Curate
Choose the next scenarios for maximum policy lift.
92%
average scene-match calibration score
48M
perfect labels produced per monthly factory
31%
policy lift from curriculum-led regeneration
SimMint system
Warehouse is the beachhead because every failure is visible.
Totes, bins, forklifts, pallets, changing SKUs, bad lighting, and dense human motion create the long tail robots must master.
Robot startups
Move faster than physical data collection without hiding transfer risk.
OEM validation
Create repeatable evidence packs for sensors, grippers, and robot cells.
Warehouse automation
Launch skills with scenarios matched to real aisles, bins, totes, and exceptions.
Product gallery
Dark operational interfaces for robot data teams.
CSS-drawn panels visualize scenario inventory, drift, transfer gates, and curriculum decisions.
Technical stack
NVIDIA stack alignment
Every layer is designed for GPU-scale simulation, training, and deployment evidence.
| Simulation | Isaac Sim, Omniverse Replicator, OpenUSD scene graphs |
|---|---|
| Compute | DGX/HGX, GB200, RTX/OVX for data generation |
| Deployment | TensorRT, Triton, NIM, Jetson Orin, Fleet Command |
| Training | TAO, NeMo, RAPIDS, NVIDIA AI Enterprise |
SimMint system
Evidence teams receive
Artifacts help engineering, safety, and procurement move together.
Dataset manifest
Every scene, label, and scenario variant tracked.
Calibration report
Site-match score, drift notes, and capture plan.
Transfer gate
Failure taxonomy and policy readiness score.
SimMint system
Procurement path
Start focused and expand when evidence is clear.
One workflow readiness report
Designed for robotics teams that need repeatable evidence, not more demo data.
Monthly generation and scoring
Designed for robotics teams that need repeatable evidence, not more demo data.
Multi-site libraries and GPU capacity planning
Designed for robotics teams that need repeatable evidence, not more demo data.
FAQ
Questions teams ask before trusting synthetic robot data.
We calibrate scenes from real site evidence, evaluate transfer, and regenerate missing scenarios until models clear gates.
No. Real data anchors calibration and evaluation; synthetic data expands coverage, labels, and edge cases.
We start with warehouse perception and manipulation, then adapt to sensors, end-effectors, SKUs, and policies.
Deploy with evidence
Turn your next robot rollout into a measured sim2real program.
Bring one site, one workflow, and one target model. SimMint returns a calibrated data-factory plan.