AI Data Annotation & Labeling Services

Proper annotation of a game world means understanding thousands of interacting systems, characters, and edge cases well enough to label them consistently, at volume, and without any lost nuance. Our multilingual annotation skills across text, image, audio and video data have been refined over 28 years of AAA gaming production, mapping onto AI annotation far more directly than most people expect.

• Domain and behavioural nuance: Gaming content carries cultural humour, character voice and genre conventions that a generic labeling workforce would miss. 
• Global consistency at scale: Text-based annotation in 80+ languages and voice data in 60+ are delivered from regional studios across the globe, on the same production infrastructure used for 28 years of consistent AAA game localization.
• Motion, action, and embodied data: Motion capture, pose and action-sequence labeling for robotics and physical AI draws directly on decades of AAA motion capture and animation production experience.
• Trained teams, not crowdsourcing: Every annotator is a trained employee working to documented guidelines and ontologies, held to the same quality and data security standards that have led to us being trusted by 24 out of the top 25 top gaming companies in the world.

We annotate across the wide range of data types modern AI systems are trained and evaluated on. If your exact need isn’t included here, contact us and speak to our team to discuss a bespoke solution.

• Text & NLP Annotation: Intent classification, sentiment and relevance labeling, entity recognition, and preference ranking across 60+ languages for language model training and evaluation. These inputs feed directly into our post-training capabilities like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO).
• Image & Video Annotation: Multi-modal labelling including bounding boxes, segmentation, keypoint annotation and action recognition across consumer, industrial and entertainment imagery.
• Speech & Audio Annotation: Transcription, speaker labeling, and emotion or event tagging, delivered natively across 50+ languages and dialects.
• Motion & Embodied Data Labeling: Managed via our Mocap Lab and Teleop Lab, we capture and label humanoid motion, pose estimation, and action sequences to convert teleoperation data into reusable action libraries for robotics and embodied AI systems.
• Multimodal Annotation: Complex co-annotation across paired image-text and audio-visual data streams for multimodal model training.
• 3D & Sensor Annotation: Point cloud and sensor-fusion labeling for autonomous systems. See our Physical AI & Robotics page for more information.

Not every annotation provider is built for the same kind of work. Our teams specialise in complex, high-stakes annotation that needs real subject-matter expertise and tight quality control, not high-volume commodity labelling.

We Annotate

• Agentic and safety-critical systems: Data for AI that needs to understand context, state and intent.
• Multimodal, real-world data: Video, motion capture and sensor data drawn from real environments.
• Expert-domain content: Projects needing genuine subject-matter expertise in areas like technical, medical or legal content, annotated by people who understand what they're looking at.
• Multilingual, culturally nuanced datasets: Delivered natively across 60+ languages, with the cultural context that keeps meaning intact.
• Post-training alignment data: Curated, expert-reviewed datasets that support RLHF and other post-training alignment work.

We Don’t

• Build or own AI models: We're the data, simulation and evaluation layer, not a model developer, so nothing we handle ever trains anything outside your engagement.
• Take on unmanaged, commodity labeling: Our workforce is fully employed and vetted, not contracted, so low-cost, transactional bulk labeling isn't what we're set up to deliver.
• Work without proper clearance: As a public company under strict governance, we don't take on projects involving export-controlled data without the right clearances already in place.

We turn down annotation work which doesn’t meet these requirements, such as a recent request to annotate gameplay footage a lab had sourced without holding the rights to it, so please ensure your project meets the standards we’ve listed above, or contact us directly if you’re uncertain.

Annotation quality problems rarely show up immediately. An MIT-led study of ten widely used AI benchmark datasets found an average labeling error rate of 3.3% (rising above 10% in some datasets), errors that sat undetected in training and evaluation data for years before anyone noticed. Once a model has been trained on inconsistent labels, undoing that damage is far harder than getting the annotation right the first time.

We keep our annotation quality consistent by:

• Calibrating against client-defined gold-standard sets before any annotator, human or AI, works live on a project.
• Measuring Inter-Annotator Agreement (IAA) to catch inconsistency between human and AI labelers before it reaches your dataset. Industry practice treats a score of between 0.8 and 0.9 as the ideal benchmark, though we’ll set agreed thresholds with you for each project.
• Setting multiple independent QA layers. A single pass is never enough, especially for larger projects across multiple languages. Our annotation projects have continuous human-in-the-loop QA and feedback built in from the start.
• Creating documented guidelines and domain-specific ontologies built bespoke for your project, not from a generic template library.

An annotation engagement is only as good as what it’s given to work with. For the best output, this is what we need from you:

• Representative source data. Edge cases only surface at volume. The larger the sample you can provide, the more accurate the output will be.
• Clear guidelines. The best results are achieved when we’re fully aligned with your goals and expectations. Clear guidelines at the start of a project ensures we work in the way best suited to your needs.
• A defined risk profile. Understanding at the outset what “good enough” means for your use case means we can identify potential issues long before they happen and provide suitable solutions.
• A named reviewer. Having a single dedicated point-of-contact on your side who can sign off on edge-case decisions greatly reduces the potential for friction and delays.
• Commitment to feedback loops. Annotation guidelines should evolve as your model does, not remain static.

The cost of annotation isn’t fixed, because no two projects are the same. How well defined the project is, how long it needs to run and the level of expertise it needs will all shape the time, resources and cost of executing it correctly. Factors which will affect your projects price include:

• Modality and complexity tier
• The numbers of languages required
• Whether domain expertise is needed
• QA layers and agreement threshold
• Security and data residency requirements
• Required turnaround time
• Whether the work runs on your tooling or ours

To best fit the requirements of your project, we operate across three pricing models:

• Fixed price pilot: a pre-agreed, fixed price with milestone payments
  This is how most engagements start, to give you a clear view of cost and quality before you commit to work at volume.
• Per unit: priced by label, frame, hour of video or trajectory. 
  Best suited to well-defined, high volume work where the task remains consistent.
• Dedicated team: priced per annotator-month
  Best suited for long-running programmes which need a consistent team embedded within your workflow over time.

Every engagement is delivered by a fully employed and vetted team, so we don’t offer crowd or marketplace pricing. 

For a quote tailored to your requirements, or to arrange a fixed-price pilot before committing to work at scale, contact our team to discuss your project.

If you’re evaluating annotation services, they’ll usually fall into one of three types of alternatives. Here’s how we compare to each of them:

vs. Generic Data Labeling Vendors

vs. In-House Annotation Teams

vs. AI Tooling & Platform Vendors

We don’t build or own AI models, so client data provided for annotation is never used to train anything outside your engagement, and nothing is pooled across clients. Our dedication to uncompromising security and privacy is rooted in 28 years of handling some of the industry’s most sensitive unreleased IP ahead of major game launches.

We're majority-owned by EQT, a European investment group, and headquartered in Ireland. This gives enterprises and public sector organisations full data sovereignty over their annotation work, without dependency on a US hyperscaler or exposure to foreign data-access laws, such as the US CLOUD Act.  

Our studios and locations maintain security credentials and participate in industry and client assurance programmes such as ISO 27001. Our Information Security & Privacy team reviews the security and assurance requirements of individual engagements and works with delivery teams to address client-specific requirements where needed.

We’re trusted by 24 out of the top 25 gaming companies in the world, and are behind 76% of 2025’s Game Awards winners. Our annotation teams already label and validate data for global hyperscalers, at a scale most AI teams have never had experience operating in.

End-to-End AI Services, From Data to Deployment

Annotation rarely happens in isolation. The same project usually needs training data pipelines to put labelled data to use, evaluation to track model performance over time, and red teaming or governance to keep it defensible in production. These services are often sourced from different providers, each with a separate contract to manage.

We cover the full AI pipeline in-house: data annotation, synthetic data and simulation, training data, red teaming, evaluation, localization and cultural data, and governance under a single contract.

Talk to Our AI Solutions Team

Get in touch to scope an annotation programme, or view our case studies to see what we’re already shipping for frontier AI labs and enterprise AI teams worldwide.

What is data annotation?

Data annotation is the process of labelling raw data, text, images, audio or video, so an AI model can learn from it. It’s the step that turns unstructured content into structured training signals a model can learn from and be evaluated against. This can be across multiple formats, such as text, image, audio, video and motion data.

How is data annotation different from AI training data?

Annotation is the first step: labelling content to allow it to become training data. Training data is what that labelled data becomes once it’s structured, curated and prepared for a specific model or fine-tuning run. For projects which need full RLHF (Reinforcement Learning from Human Feedback), supervised fine-tuning datasets or preference-pair generation, this is covered in more depth on our AI Training Data & Model Tuning page.

Do you annotate data in languages other than English?

Yes. We deliver text annotation in 80+ languages and voice data natively in 50+ languages from regional studios worldwide, on the same production infrastructure we’ve used for 28 years in AAA game localisation.

What happens to our data once we share it for annotation?

Keywords Studios doesn’t build or own its own AI models, so there’s no separate system for your data to ever feed into and nothing is shared across client engagements.

Can you take on annotation work already underway with another vendor or in-house team?

Yes, picking up an in-flight annotation programme, whether to fix quality issues, add QA layers, or scale volume, is one of the more common ways we’re brought into a project.