Abundant builds reinforcement-learning environments, evaluation benchmarks, and training datasets for AI labs, AI startups, and large enterprises developing and improving AI agents.
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What they offer
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Data sources
AI Astrolabe supplies human-written and annotated training datasets, model evaluations and red-teaming services, with a focus on Arabic dialects and other low-resource languages. Its catalog includes text, speech and audio, images, and safety preference data.
Alice, formerly ActiveFence, sells AI safety and security training datasets, agentic RL environments, and expert-led red teaming and evaluations to foundation model labs; it also sells WonderSuite guardrail software to enterprises and content moderation to online platforms.
Appen supplies AI labs and product teams with human-produced training data, annotation, expert feedback, RL environments, and model evaluations for machine-learning and agentic AI systems.
Builds production-style simulated workspaces and verifiable benchmarks for AI agents, helping AI developers train and test long-horizon tasks in cybersecurity, incident response, software engineering, and tool use.
Bugcrowd provides cybersecurity training environments and an exploitation benchmark to AI developers, alongside crowdsourced vulnerability testing services for organizations.
Chakra Labs builds expert-authored tasks, graders and computer-use environments for training and evaluating AI models. Its off-the-shelf and bespoke task sets cover finance, legal work, responsible AI and computer use.
Claru provides custom and licensed video, robotics demonstrations, and human annotations to AI labs building robotics, embodied AI, and world models.
Collinear sells stateful simulated environments, agent evaluations, and synthetic training data to frontier AI labs and enterprise AI teams building and improving AI agents.
Datacurve sells expert-built coding data, reinforcement-learning environments, benchmarks, and evaluations to foundation-model labs and AI developer-tool companies.
Exponential sells AI developers multi-agent security environments for RL post-training, security-focused training data, and adaptive red-team evaluations that test AI models and agent systems against realistic attacks.
G2i supplies coding post-training data to AI labs (RL environments, evals, RLHF preference data, SFT datasets, scrubbed seed data) through managed projects or contract engineers placed on lab teams. It also runs an engineer hiring platform and tech conferences.
Gray Swan sells AI-model and agent security to frontier labs and enterprises: adversarial testing before release, plus runtime filtering and monitoring to block attacks, unsafe outputs, and policy violations.
Idler sells reinforcement-learning environments, benchmarks and evaluation datasets based on production work to AI labs and other teams training and evaluating models.
Incalmo builds reinforcement-learning cyber environments and security evaluations for AI labs and security teams to train and assess cybersecurity-capable models.
Innodata provides AI developers and enterprises with training data, human feedback, annotation, reinforcement-learning environments, model evaluations, and red-teaming services for building and improving AI models and agents.
Labelbox sells simulated reinforcement-learning environments, expert-generated preference and training data, and evaluation services to frontier AI labs; it also offers enterprise teams software for running agents on real company workflows.
LatchBio sells cloud data and compute software, bioinformatics workflows, and AI-based analysis to biotech R&D teams, laboratories, and scientific kit or instrument providers. It also publishes evaluations and offers assessment services for biology-focused AI agents.
Noumad builds formally verified reinforcement-learning environments for post-training AI agents on unsolved problems in long-horizon machine learning, computational science, robotics and cybersecurity.
Olam Labs builds simulated multi-agent environments, behavioral evaluations and game-derived datasets for AI labs and researchers training or evaluating models’ social, agentic and safety capabilities.
Quesma publishes coding-agent benchmarks and builds RL training environments for frontier labs; it also markets tools that help development teams understand agent costs, workflows and outcomes.
RL Labs builds long-horizon software-engineering environments where frontier AI labs can train models on complex, multi-step coding tasks.
TELUS Digital supplies AI labs and developers with expert-built training data, annotations, agentic RL environments, red-team testing and evaluations, plus custom multimodal and physical-AI datasets and managed data-collection services.
Trajectory Labs runs safety evaluations, expert red-teaming and RL environments that help AI labs find model vulnerabilities and measure safer behavior.
Turing supplies AI labs with training data, evaluations, and RL environments for coding, enterprise knowledge work, and STEM. It also helps large enterprises build and deploy AI agents in important workflows.
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