Alternatives to Cortex AI

Applied research lab that captures expert reasoning and structures it into SFT, RL rubric, agent, and computer-use training data for frontier models.

Velvet sources, verifies, and delivers curated video datasets that help AI labs train interactive and world models with real spatial reasoning.

Snorkel AI builds expert training data, evals, and runnable environments for frontier models and agents.

Modular humanoid robot platform for builders collecting data, training skills, and deploying autonomous robots.

Open-source, multimodal data annotation platform for image, video, 3D point cloud, and audio labeling—self-hosted, cloud, or fully managed

GameEngineBench benchmarks and UE5 training data for AI agents that write real game-engine code.

BioStack Platforms supplies structured clinical datasets and RL environments for healthcare AI teams.

Offline AI image annotation for YOLO and VOC datasets — local SAM2, SAM3 and YOLO-World, one-time purchase from $49.

Human-recorded computer-use datasets that teach AI agents to operate real software the way people do.

Early-stage San Francisco robotics lab building policies for robots that do real work, and hiring its first researchers.

A human data platform connecting AI teams and researchers to 300,000+ verified participants for preference tuning, safety evals, and online studies.

A free 70,000-image Zalando clothing dataset built as a drop-in replacement for MNIST.

Open-source Python framework for generating grounded synthetic datasets from real sandboxed tool execution traces.

Hub collects real-world multimodal data for embodied AI — egocentric video, teleoperation, hand tracking and tactile — with custom collection delivered in

Rhoda AI builds video-predictive robot foundation models that learn heavy industrial manipulation tasks from a single human demo.

Web3 data network paying contributors in crypto for text, audio, video, code, and mapping data that trains AI models.

Raiinmaker builds custom, rights-cleared video datasets and runs real-time human evaluation for generative AI video models.

Managed training data, RL environments, and safety red-teaming for teams building AI agents, coding models, and LLM/VLM pipelines.

Open-source Unreal Engine plugin giving computer vision researchers synthetic images with pixel-perfect ground truth.

A downloadable Python annotation tool and web app you run yourself to build training and evaluation data for custom AI, ML and NLP models.

Daft is an open-source multimodal data engine for turning video, images, audio, and sensor data into training-ready datasets.

The skill network for AI agents — encode expertise once, activate it autonomously, distribute it across agents.

Open-source, self-hosted annotation for text, media, and AI agent traces — configured entirely in YAML.

Open-source Python SDK and CLI for training and deploying physical AI models — VLA and SLM — from managed cloud compute to real robot hardware.

Audio datasets built like research artifacts for teams training speech recognition, translation, synthesis and voice-interaction models

Traverse is a research lab producing expert-captured training data that gives frontier models taste and judgment for ambiguous, long-horizon work.

Multimodal real-world human data for training embodied AI agents

Mercor is a marketplace where vetted domain experts do AI model evaluation and training data work for frontier labs and enterprises, averaging $112/hr.

Physical Intelligence (π) builds vision-language-action foundation models that let one policy steer any robot through any manipulation task.

RL environments and expert-labeled data for frontier models and enterprise agents