Ingest
Pull in video, sensor, depth, and log files from any capture setup.
RGB / stereo video
IMU, force, and telemetry
Depth and pose captures
ROBOTICS DATA INFRASTRUCTURE
Stop cleaning data. Start training models.
StarkzAI automates the messy path from raw captures to structured, labeled, training-ready datasets - so your team spends less time on one-off scripts and more time shipping models.
Less manual data cleaning
Faster path from capture to training
Reusable schemas across experiments
Standardized exports every time
Runs on your machinesPython-nativeCLI / API friendly
Pipeline
Raw input to training-ready export
Local-first Python pipeline
Video
front_rgb.mp4
Sensor
imu.csv
Depth
realsense.bag
Logs
robot_run.log
01
Input data
02
Structuring
03
Labeling
04
Export
RLDSHDF5LeRobot
Input data -> Structuring -> Labeling -> Export
Strategic Vision
Building the standard data layer for physical AI.
Starting with robotics motion and multimodal training data, StarkzAI is becoming the structuring layer that makes physical-world datasets reusable, consistent, and production-ready.
Pipeline
One pipeline. Raw capture to trainable dataset.
Ingest once, keep the structure consistent, and produce exports your training stack can actually use - no custom preprocessing scripts required.
Structure
Align timelines, normalize formats, and apply reusable schemas automatically.
Synchronized timelines
Consistent metadata
Reusable schemas
Label
Add action segments, behavioral metadata, and training annotations.
Action segments
Behavioral metadata
Training annotations
Export
Ship datasets in RLDS, HDF5, or LeRobot - formats your stack already supports.
RLDS
HDF5
LeRobot
Why Starkz
Built for robotics workflows, not generic data tooling.
The goal is not another analytics dashboard. StarkzAI is being shaped around the practical path from raw collection to repeatable dataset preparation for robotics and physical AI teams.
Your data never leaves your machines.
No uploads, no cloud dependencies - full control over sensitive captures and annotations.
Python-native tooling.
Fits the data and training stacks your team already uses.
CLI and API friendly.
Built for batch jobs, automation, and reproducible dataset prep.
Modular format support.
Adapts to evolving sensor, log, and export requirements.
Designed for engineers who ship.
No dashboards, no fluff - just tools that get data ready.
Starts focused, expands over time.
Motion and demonstration data today, broader physical AI data tomorrow.
Where Starkz Helps
Built for the workflows that matter most.
Starkz focuses on the problems robotics teams actually hit: preparing demonstrations, syncing multimodal captures, and getting research data into a shape that survives production.
Imitation learning teams
Sync demonstrations, segment actions, and export training-ready examples - without rebuilding your pipeline every collection run.
Demonstration capture workflows
Turn human demonstrations into structured assets that stay usable across annotation, evaluation, and model training.
Multimodal data prep
Get video, depth, and sensor streams into one consistent structure before they hit your training stack.
Research-to-production handoff
Stop rewriting scripts every time a research dataset needs to work in production.
Built for
RoboticsEmbodied AIImitation LearningPhysical AI LabsResearch Pipelines
Book Demo
Bring us your messiest robotics dataset.
We'll show you how StarkzAI structures it into a cleaner, training-ready pipeline.
What to send
A sample capture stack, current dataset format, and where your prep pipeline breaks.
What happens next
Your request lands in the same intake pipeline as the existing Google Script form and we follow up directly.