Vision AI apps

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.

OCR and document conversion built for math, science, and STEM.

OCR and document-conversion tooling specialized for STEM content. Mathpix reads printed and handwritten math, chemistry, tables, and text from images and PDFs, exporting to LaTeX, DOCX, Markdown, Excel, ChemDraw, and more. It ships as the Snip app (web, mobile, desktop, browser extension) for individuals and teams, plus a Convert API for developers building solving, tutoring, and grading products.

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.

Multimodal platform to search, reason over, and clip large volumes of video.

Reka Vision is an enterprise multimodal system that indexes large image and video libraries so teams can search by meaning, ask timestamp-aware questions, and auto-generate highlights and clips. It is built by Reka, a frontier multimodal-model lab, and is available via API, an MCP server, or a hosted app. Access is sales-led (request a demo).

State-of-the-art YOLO models for real-time object detection and vision.

The open-source PyTorch framework behind the YOLO (You Only Look Once) family of vision models. One unified API covers object detection, instance and semantic segmentation, image classification, pose estimation, and oriented bounding boxes, with both a CLI and a Python interface. The 2026 flagship, YOLO26, is an end-to-end, NMS-free architecture tuned for edge and low-power deployment.

Enterprise data engine for labeling and managing unstructured AI data.

An AI-ready data platform that manages, labels, and orchestrates unstructured data — images, video, LiDAR, audio, and text — across the model lifecycle. It pairs data management and human-in-the-loop annotation with a serverless pipeline layer for pre/post-processing, RLHF, and RAG, plus a model-and-app marketplace. Originally focused on computer-vision production pipelines.

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.

Video intelligence API: search, classify, and summarize video.

Video understanding platform built on its own multimodal foundation models — Marengo for embeddings and semantic search, Pegasus for generative tasks like summaries and captions. Developers index video once and run natural-language search, classification, and analysis via API. Free tier with usage-based pricing beyond it.

Visual prompting + vision agents from Andrew Ng's lab.

Build vision applications with a labelling-light workflow — point at examples, get a deployable detector. Recently extended into vision agents that reason over images and PDFs without bespoke training.

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.

FiftyOne — open-source vision data platform.

Open-source toolkit for exploring, debugging, and curating vision datasets. Strong story for finding model failure modes, balancing classes, and tracking experiment drift across visual data at scale.