hasta.ai
Labeling is eating your CV budget.
Capture and hand-labeling are slow and expensive. We turn 3D models and specs into ready-to-train synthetic datasets — boxes, masks, keypoints — in days, not months.
For labs, startups, and ops teams that need labeled vision data without a labeling army.
01
Labels eat the budget
Most of the spend goes into getting labels, not into the model.
02
Waiting stalls training
Every week spent waiting on labels is a week your model sits idle.
03
Edge cases and privacy
Manual labels miss the hard scenes and create privacy headaches.
The offer
A project-delivered synthetic dataset
Images plus precise labels — export-ready for your training stack.
1
Tell us the task
Describe what you’re building and what you have — 3D/CAD, photos/video, or specs.
2
We generate and label
We deliver a synthetic dataset: images plus precise labels, ready to train.
3
You train — then scale
Ship the model. Need more volume or refreshes later? Another project, or a retainer when it makes sense.
What you get
Labeled data, ready to train
Synthetic computer vision datasets sized to your task — drop into PyTorch, TensorFlow, or Hugging Face workflows.
- 2D bounding boxes
- Instance segmentation masks
- Semantic segmentation masks
- Pose / COCO keypoints
- Augmentations and corner-case coverage
- Pixel-accurate labels — no human labeling pass
Use cases
Where this pays off
High-value vision tasks where labeled data is the bottleneck.
Manufacturing / inspection
Defect and part vision without months of labeled defect photos.
Robotics / warehouse
Perception data for the scenes and objects your robots actually see.
Retail / product recognition
SKU and shelf-style datasets sized to your catalog and cameras.
Works with your stack
Domain randomization in synthetic data can lift detection rates substantially — see the research.
Lower cost. Faster datasets. Labels you can train on.
Tell us the task. We’ll quote a synthetic data project.