Human expertise providers

Turns specialist problem-solving into fine-tuning examples, grading rubrics, agent environments, and demonstrations of browser and desktop work.

Builds verification for AI agents using expert judgment, enterprise knowledge, and encoded rules. Its published description emphasizes grounding and checking agent behavior.

Creates specialist training and preference data and runs model or workflow evaluations tailored to a customer’s domain or use case.

Provides human-feedback and labeling services for generative AI and custom models as the AI-focused division of Apex CoVantage.

Provides human-validated data for model alignment, agent trajectories, speech, physical AI, and model evaluation.

Combines professional specialists, domain datasets, and task environments for AI training and evaluation in regulated knowledge work.

Collects real-world human activity footage and teleoperation data, then prepares annotated training material for embodied-AI development.

Provides crowdsourced security testing, vulnerability programs, and researcher expertise. Its broader security offering should be scoped specifically for an AI evaluation project.

Develops physical-AI datasets through an integrated collection operation spanning capture hardware, logistics, processing, and model-training work.

Combines multilingual and multimodal datasets, expert services, and AI platforms for model development and enterprise deployment.

Creates specialist-authored tasks and graders for training and evaluating models on finance, legal, computer-use, and responsible-AI work.

Prepares unstructured data and combines human validation, model oversight, and workflow orchestration to support operational AI systems.

Uses creative professionals’ judgments to evaluate AI-generated work across media and creative tools, with benchmarks focused on quality and taste.

Collects first-person workplace recordings and robot trajectories, including supervised deployments that produce recovery examples from real-world failures.

Builds data-collection infrastructure and curated datasets informed by human expertise, with research including the DeepSWE software-engineering benchmark.

Collects robotics training material including first-person video, manipulation demonstrations, and platform-specific teleoperation recordings.

Now operating as TrustScale, e2f provides custom training datasets, expert annotation, and model evaluation. Its current offering covers multimodal data, language quality, factual verification, reasoning benchmarks, and safety review.

Combines multimodal data management and annotation software with managed collection and domain-expert annotation services.

Connects business-document owners and domain professionals with AI labs for content licensing, consulting, and evaluation engagements.

Converts expert decisions, corrections, preferences, and verification evidence into datasets, private evaluations, and regression environments.

Uses software-engineering expertise to turn production workflows into tasks, rubrics, evaluations, and training environments with scoring and telemetry.

Runs an AI expert program and research activity, with a separate partnership program for licensing company records for AI development.

Captures synchronized human activity across visual, motion, audio, and tactile signals to support physical-AI learning.

Provides expert-led annotation and generative-AI workflows, including feedback, reasoning data, and agent evaluation through its tools and services.

Combines expert networks, data preparation, evaluation tooling, and enterprise AI implementation services.

Sources licensable datasets from contributors and supports custom collection tasks for AI labs and research organizations.

Offers expert-produced reasoning annotations, preference examples, and reinforcement-learning evaluations aimed at robot learning and world models. Public service detail is limited to the official site’s description.

Collects, labels, transcribes, and evaluates text, audio, images, and video. Its services include ready-made datasets, custom collection, supervised fine-tuning examples, human preference feedback, and model red-teaming.

Supplies professional expertise, human evaluations, benchmarks, and environments; it also offers a business-workflow data monetization program.

Develops expert data, realistic training environments, contextual agent evaluations, and demonstration data for robotics.

Recruits and manages domain specialists for model evaluations, post-training examples, and independent annotation audits. Deliverables include preference comparisons, demonstrations, rubric scores, and quality-assurance reporting.

Sources licensed business datasets and connects AI teams with domain experts. Services include identifying data owners, agreeing permitted uses, preparing records, and developing expert tasks and evaluation criteria.

A talent marketplace and managed-service provider for AI data work. Teams can source specialists for annotation, model evaluation, preference feedback, and safety testing while retaining their existing tools.

Develops human-sourced training signals for AI models through specialist participation and research. Its public site centers expert data and offers project inquiries, while detailed deliverables require direct scoping.

Creates custom engineering problems, solution traces, and reasoning annotations with senior practitioners. Datasets are calibrated to model difficulty and can support fine-tuning, preference learning, or held-out evaluation.

Runs expert-led data and evaluation programs, supplies specialists, and collects embodied multimodal recordings. Its work includes professional-domain assessment, safety testing, multilingual benchmarks, and labeled robotics sensor data.

Provides expert annotation and evaluation services alongside its Syncronus collaboration platform. Work covers difficult reasoning, tool use, agent behavior, safety testing, and peer-reviewed specialist workflows.

Connects researchers and AI teams with verified human participants. AI use cases include collecting preference judgments, safety feedback, and evaluation data from selected populations rather than relying solely on automated metrics.

Combines domain data and expert review to produce reasoning datasets, sandboxed training tasks, and adaptive evaluations. Coverage includes scientific problems, software engineering, financial analysis, and physical-system sensor data.

Uses software engineers to develop code-focused datasets, expert demonstrations, evaluation tasks, and training environments, alongside its engineering talent business.

Combines subject-matter experts with managed workflows and automation to deliver human feedback, labeled examples, and model evaluations.

Delivers human-verified annotation, data validation, and model evaluation for computer vision and multimodal AI.

Reviews AI training data against agreed criteria using AI judges calibrated and checked against human experts.

Provides AI training data and human expertise alongside evaluation, enterprise, and public-sector AI services.

Combines multimodal annotation, data orchestration, human review, and evaluation tooling, including support for agent trajectories and RL environments.

Offers off-the-shelf data, expert workforce services, post-training work, and benchmarks across professional tasks.

Provides AI data operations, feedback, evaluation, and safety services alongside broader business-process and autonomous-system operations.

Offers data collection, specialist post-training work, annotation, safety evaluation, and off-the-shelf datasets across language, vision, and physical AI.

Creates agent-training data, simulated environments, human feedback, and evaluations covering tool use, coding, and model safety.

Provides AI safety evaluations, red teaming, and training environments through researchers and engineers working on frontier-model risks.

Supports frontier-model development with datasets, reinforcement-learning environments, and specialists in software, knowledge work, and STEM.

Offers data collection, annotation, testing, and human-in-the-loop validation for generative and physical AI through its operations platform and contributor network.

Develops reasoning datasets, expert-reviewed mathematical work, and private benchmarks that capture partial progress as well as final answers.

Connects vetted domain specialists with AI projects involving response review, scenario judgments, and practitioner-written problem approaches. Expert matching considers relevant experience and demonstrated depth in the required specialty.

Designs model-learning tasks, rubrics, verifiers, feedback, and evaluations across professional and multimodal work.

Works with creative contributors to produce and license imagery, video, design, and other multimodal material for AI development.