LandingAI

Vision MLOps end-to-end. Annotate, train, deploy.

Annotation tooling, auto-labelling, hosted training, and edge deployment for computer-vision projects. Strong default when you're shipping a custom vision model rather than reaching for a multimodal LLM.

Data platform to curate, label, and manage AI training data.

An enterprise data development platform for preparing high-quality training data across images, video, documents, audio, DICOM, and 3D point clouds. It pairs AI-assisted labeling (SAM auto-segmentation, object tracking) with data curation, model evaluation, and workflow tooling, plus LLM-powered data agents for document tasks. Used heavily in medical imaging, robotics, and other physical-AI domains.

All-in-one computer vision platform to curate, label, and train models.

A unified computer vision platform covering data curation, annotation, model training, and deployment across images, video, 3D point clouds, and medical imagery. AI-assisted labeling, experiment tracking, and a large catalog of installable apps make it customizable for most CV workflows. Free for researchers and small teams; Pro and self-hostable Enterprise editions for companies.

Tiny open vision-language model for efficient image understanding.

An open-weights family of small vision-language models for captioning, visual Q&A, pointing, counting, and object detection — small enough to run on-device (checkpoints down to 0.5B on Hugging Face). Run it locally with the Photon engine, or call Moondream Cloud's OpenAI-compatible API with a free monthly credit tier and pay-per-image pricing.