Hasta AI
Training data for the cases you can’t capture.
Custom synthetic datasets for computer vision. Hasta AI turns 3D assets, CAD, and reference material into training images with precise labels, giving models more examples of the objects, environments, and difficult conditions they need to understand.
- The product
- Custom synthetic vision datasets
- The output
- Training images + annotations
Give your vision model more to learn from.
A vision model needs examples of the world it will encounter. Real data collection can leave gaps: a rare defect, an unfamiliar object, a difficult camera angle, or lighting that changes the appearance of a part. Capturing and labeling enough of those cases takes time.
Hasta AI builds datasets around the task. Starting with 3D models, CAD, photos, video, or specifications, it creates scenes and generates images with their annotations. Teams get targeted training material for the conditions they need to cover, with less dependence on collecting and labeling every example by hand.
- Synthetic data
- Computer vision
- 3D simulation
- Automated annotation
Control the scene. Expand the coverage.
Synthetic environments let you vary the inputs deliberately: objects, viewpoints, lighting, backgrounds, and the arrangement of a scene. Build examples around the conditions that challenge a model, including unusual angles and objects that are difficult to capture in sufficient numbers.
The dataset can evolve with the model. Evaluate where recognition breaks down, then add focused variations around those gaps. The aim is useful coverage for a specific task, from recognizing a component to finding an object in a cluttered workspace.
Images and labels, generated together.
The output goes beyond pictures. Hasta AI supplies 2D bounding boxes for locating objects, instance masks for separating individual objects, semantic masks for labeling regions, and pose or COCO keypoints for describing body positions.
Those annotations support object detection, segmentation, and pose estimation. The dataset is shaped around your task and training requirements, so your team can put the images and labels to work in its existing model training process.
Built around what the model needs to see.
For manufacturing, that means parts, defects, and visual quality checks. For robotics and warehouses, it means the objects and scenes a machine encounters while working. For retail, it means products and shelves matched to the catalog, layouts, and camera views that matter.
Hasta AI’s focus is the training data those systems depend on. Each project starts with the target task and the required labels, then builds the scene variation needed to support training, testing, and the next iteration.
From your assets to training data.
A custom dataset starts with the model’s job: what it needs to recognize, the conditions it must handle, and the annotations required to learn from each image.
Define the task
Share the objects, environments, and difficult cases. Bring 3D models, CAD files, reference photos, video, or a set of specifications.
Generate and label
Build scenes around those requirements. Vary the conditions and generate training images alongside bounding boxes, masks, or keypoints.
Train, test, refine
Put the dataset into your training process. Evaluate the model, identify remaining gaps, and expand the data where more coverage is needed.
What does your model need to see?
Tell us what you’re training, where your data falls short, and which labels you need. Let’s scope a synthetic dataset around the problem.
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TWO TACOS
- ✓ TWO WORKSTREAMS AT A TIME.
- ✓ CHAT WITH US IN TRELLO.
- ✓ NO CONTRACTS.
- ✓ 30 DAY CANCELLATION NOTICE