Coverage
Rigid boxes are covered. Soft bags, clear objects, and occlusions are not.
DATA FOR ROBOTICS & PHYSICAL AIDEFINED. REVIEWED. DELIVERED.
Real-world tasks.
Training-ready
data.
Match existing data.
Capture the missing cases.
BatchOS helps robotics and AI teams source task-fit data, coordinate gap-filling capture, and review each batch against agreed acceptance criteria.
01 / THE DATA GAP
More data.
Still missing
the right cases.
AI teams have data. What they need is a fluid market for task-specific data they can evaluate and accept.
A thousand repeats of one motion can still miss the object, angle, or recovery a model needs next.
Interaction
The action is visible. The hand pose, transition, or recovery may be missing.
Acceptance
Usable delivery depends on the right fields, clear permissions, and agreed quality checks.
02 / A TASK, MADE CONCRETEWAREHOUSE PACKING
Start with the task.
Build the right batch.
Match task, hardware, and fields first. Then focus production on the examples that are missing.
TASK VIEW / PACKING
ILLUSTRATIVE TASK · SCOPED PER PROJECT
TELEOPERATION / UMI
The action.
And the signals behind it.
Scope video, end-effector poses, and action records around the target hardware and control task.
EGOCENTRIC / MULTI-VIEW
See the motion.
Understand the sequence.
Use human activity video, hand pose, and stage labels to study actions. Robot control requires its own aligned state and action records.
CUSTOMER SETTING / EDGE CASES
Capture what
the model still misses.
Turn evaluation gaps into a focused capture plan: different objects, obstructed views, slips, and recovery attempts.
DATA NEEDS / SCOPED TO YOUR MODEL
Different inputs.
One clear brief.
Physical AI is our starting point. We also discuss targeted data sourcing across languages, domains, and model uses.
DATA TYPETRAINING USEFIELDS TO REVIEW
01Robotics & egocentric
Policy learning · Action understanding
Video, pose, actions, hardware, and synchronization
02Speech & audio
Speech recognition · Audio understanding
Language, domain, transcripts, speakers, and recording conditions
03Text & code
Domain adaptation · Code understanding
Language, domain, format, source, and permitted use
04Multimodal
Vision-language learning · Video understanding
Image/video pairs, captions, timestamps, and alignment
These are enquiry categories. Sample availability, field coverage, and permissions are reviewed for each project.
03 / HOW BATCHOS WORKS
One workflow.
From need to acceptance.
Use suitable existing data. Put new production effort where it changes coverage.
01
Match
Screen candidate data for task fit, hardware, required fields, and permitted uses.
SOURCE SHORTLIST02
Validate
Review samples together. Define acceptance criteria and the missing cases before capture.
SAMPLE + GAP BRIEF03
Produce
Coordinate targeted capture, cleaning, annotation, and quality review against the spec.
REVIEWABLE DATA BATCH04
Accept & iterate
Review the batch, record issues and revisions, and use model feedback to scope the next order.
ACCEPTANCE + NEXT BRIEFThe next batch starts with what the last one revealed.
04 / WHAT TRAVELS WITH THE DATAKnow what
Know what
you are accepting.
A data batch needs context. Agree on the evidence and checks that matter to your use case.
BATCH RECORD / PROPOSED FIELDS
- Provenance
- Source, capture context, and version
- Permitted use
- Purpose, territories, and onward rights
- Quality review
- Fields, sync, calibration, and alignment as applicable
- Acceptance
- Agreed criteria, sample review, and revisions
Availability, field coverage, rights, and delivery scope are confirmed for each project.
05 / CONNECTING THE TWO SIDES
U.S. demand.
Cross-border execution.
Our initial focus is U.S. robotics and AI teams, including frontier and neo labs with task-specific data needs.
DEMAND
Model teams
Tasks · Hardware · Evaluation
SUPPLY
Data & production
Existing sources · Capture teams
Asia is an initial supply and coordination focus. Each project is scoped around permitted uses and destinations.
Connecting markets
starts with understanding
both sides.
UCLA and four years in California shaped Stephen’s understanding of the U.S. market. Experience in Web3 and foreign trade brings a practical grounding in cross-border supply, contracts, and delivery.
UCLA4 YEARS IN CALIFORNIAWEB3 + FOREIGN TRADE
BatchOS brings that experience to AI data: translate a buyer’s model needs into a clear brief, coordinate supply, and work toward verifiable delivery.
Connect with StephenFrom a usable batch.
To a repeatable workflow.
To a marketplace for data.
We are starting with project delivery. The marketplace vision grows from accepted samples, reusable workflows, and repeat demand.
BEFORE WE STARTA few practical
A few practical
questions.
Can we start with existing data?
Yes. We first assess suitable sources against your task, hardware, fields, and intended use. Samples, availability, and licensing are confirmed before a project is agreed.
What if our required examples do not exist?
We can scope gap-filling capture and coordinate annotation and quality review. The plan depends on your setup, sensor requirements, target conditions, and agreed acceptance criteria.
How do you price a project?
Existing data is scoped by usage and volume. Custom capture, annotation, and processing are quoted by task. We agree on the deliverables, review process, and commercial terms before work begins.
Can I contribute data or production capacity?
Yes. Share the data types or capture capabilities you can offer, together with sample availability and the rights you can grant. We assess relevance to specific projects before making any commitment.
LET’S DEFINE THE NEXT BATCH
What does your
model need next?
Tell us about the task.
We’ll start with the data conversation.