B2B synthetic-data studio · done for you
obj_0001 · class: training_data · conf 1.00

Training data for physical AI.
Generated, not collected.

Send photos of your environment. We build the physics-accurate sim, generate thousands of perfectly labeled frames overnight, and hand you the dataset. No photo shoots. No labeling crews.

Get 500 free labeled images See how it works

output: 0 frames / overnight run · labels: pixel-perfect · hand-labeling: none

[ the bottleneck ]

Your model isn't the problem.
Your data is.

collection

Cameras are slow

Weeks of staging for every variation your robot will meet.

annotation

Labeling is worse

Dollars per mask. Months at scale. Errors anyway.

coverage

Edge cases won't pose for you

Crushed boxes, occlusions, rare defects: none of them pose on demand.

// A simulation already knows where every pixel belongs. We use that.

[ the math ]

Collect and label.
Or generate.

manual pipeline

Weeksper collection round
$2-8per hand-labeled mask
Cappedby what you can stage

delivered by binary core

Hoursper 10,000-frame dataset
$0marginal cost per label
Unlimitedvariations, on demand

[ how we work ]

You send a photo.
We deliver the dataset.

A done-for-you engagement: our engineers build it, you receive the files. Days, not quarters.

step_01

You describe

Send photos, CAD, or one plain-English sentence. That's enough.

step_02

We build

BowerBot, our open-source scene agent, builds a physics-ready environment in hours, sensors matched to your camera.

step_03

We randomize

Every frame re-rolls items, lighting, and viewpoint, so your model learns the concept, not one room.

seed: 8421 → 8422 → 8423 → …

step_04

You receive

Pixel-perfect ground truth lands in your hands: COCO, KITTI, YOLO, or your schema.

runs on NVIDIA Isaac Sim & Omniverse · drops into your existing training stack

[ evidence ]

Our own products run on this data.

proof: medical

CytoLens™

Medical AI trained on 100% synthetic imagery, zero patient data, validating in 2 hospital systems. Pathology was the hard case.

Discover CytoLens ›

proof: open_source

BowerBot

The agent behind our speed is open source: natural language to production-correct OpenUSD. Read the code that builds your sim.

View on GitHub ›

proof: pedigree

Photorealism is our trade

A decade of Hollywood production pipelines, then Microsoft HoloLens and Meta Reality Labs. Realism at scale is the trade.

Film credits ›

[ built for ]

Teams with robots, cameras, and deadlines.

env: warehouse

Warehouse & logistics automation

Bin picking, parcel induction, depalletizing. Every SKU before it hits the line.

env: integration

System integrators

Ship a trained perception model with every deployment.

env: factory

Industrial inspection

Thousands of the defects you've only seen twice.

env: field

Defense, agriculture & field robotics

Terrains, weather, and scenarios you can't stage.

[ engagements ]

Three steps from photo to production.

step_a · no risk

Sample

free · 48 hours

  • Send one photo of your bin, cell, or product
  • Get 500 labeled synthetic images of it back
  • Judge the quality yourself
Request free sample

step_b · prove it

Pilot

4 weeks · from $25,000

  • Full simulation of one environment + your objects
  • 10,000+ frames in the modalities you need
  • Trained baseline model + benchmark on your real footage
  • You keep the data, the model, and the sim

step_c · scale

Production

scoped to your roadmap

  • New SKUs, environments, and sensors on demand
  • Fresh variations in days
  • Additional modalities as your stack evolves

You own everything: images, labels, models, and the simulation itself. Full transfer, no lock-in.

[ objections, answered ]

The questions every perception lead asks.

Does synthetic data actually transfer to a real robot?

Yes, when the sim is built right. Domain randomization forces the model to learn the object, not the render; it's how we trained CytoLens for clinical use. Every pilot is benchmarked on your real footage first.

What do you need from us?

A few photos, rough dimensions, CAD if you have it, and a sample frame from your camera. NDAs welcome.

We don't have 3D models of our objects. Is that a problem?

No. Model creation happens inside our pipeline. Box-like products are built procedurally from photos, complex objects are reconstructed by scan or photogrammetry, and AI-assisted generation fills in variety. You never source an asset.

Can you match our specific sensors?

We match intrinsics, mounting, and noise. Depth, stereo, and LiDAR come from the same scene: perfectly registered, never faked in post.

We already collect real data. Why add synthetic?

Keep it. The best results blend both. Synthetic covers what real can't: new SKUs, rare defects, and scenarios you'll never stage.

Who owns the data and the models?

You do. Images, labels, models, and the USD sim transfer in full, pipeline included if you ever take it in-house.

Also at Binary Core: OpenUSD pipelines & digital twins for AEC and manufacturing.

Ask about pipeline work ›

[ the founder ]

Built by someone who's shipped realism for a living.

Arturo Morales Rangel

Founder & CEO · Binary Core LLC

A decade building production pipelines for Hollywood, then spatial computing at Microsoft HoloLens and Meta Reality Labs.

Binary Core exists to break the data bottleneck holding physical AI back.

Film & TV Spellbound · Atlas · The Little Mermaid · Pinocchio · Deadpool · Furious 7 · Chappie · Suicide Squad · House of Cards
LinkedIn Arturo Morales Rangel
Member Visual Effects Society (VES)
Credits Full IMDB profile
NVIDIA

Inception Program member.

NVIDIA Inception Program Member

[ 48-hour offer ]

Your robot has never seen enough.
Fix that this week.

One photo in. 500 labeled images back in 48 hours, free. Then decide.

Get 500 free labeled images

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