AI Testing & Evaluation Services
Structured evaluation, benchmarking and quality assurance for AI models and agents, built on 28 years of production experience at global gaming scale.
AI agents are getting increasingly more adept at passing public benchmarks, with the separation between the top models becoming increasingly slim. Stanford’s 2026 AI Index found that evaluations which were meant to stay challenging for years are losing the ability to tell the top models apart in just a few months.
Whilst these benchmarks tell you how effective a model is at passing a general test, there’s no guarantee that this will translate to the specific needs, requirements, nuances and expectations of your audience and industry.
Effective evaluation needs to be tailored to the needs of your users: their industry, the languages they speak and the tasks they’ll need your model to perform, evaluated against your own definitions of what a passing result looks like.
We don’t build or own AI models. We test and evaluate your models with the same in-house specialists, processes and dedication to quality that’s led to us working with 24 of the world's top 25 game publishers.
What Is AI Evaluation?
AI evaluation is the process of measuring the performance of an AI model against a set of pre-defined standards for accuracy, consistency and behavioral expectations across multiple runs, to ensure it behaves in the way it’s intended to once it meets real users and unexpected edge-cases.
The process works hand-in-hand with Red Teaming, with evaluation designed to test performance across a set of known and well-defined criteria whilst Red Teaming looks for the bugs and edge-cases that haven’t been mapped out yet. Once new issues are identified, they’re passed over to the evaluation team to become an ongoing part of the evaluation pipeline.
The Layers of AI Evaluation
Evaluating an AI model isn’t a single task that results in a pass or fail, it’s an ongoing, layered process designed to measure accuracy and consistency across repeated non-deterministic responses.
Services within AI testing and evaluation can include:
- Model and agent output evaluation to test the accuracy, completeness and coherency of outputs against pre-defined criteria specific to your use case.
- Factual consistency (hallucination) evaluation to compare outputs like retrieval-augmented generation (RAG) responses against verified source material and catch confidently wrong or unsupported answers before the agent reaches production.
- Guardrail and safety evaluation to test the effectiveness of safeguards against biased, harmful or off-policy outputs.
- Regression testing and benchmarking to track a model’s performance across versions and fine-tuning runs.
- Multilingual and cross-cultural evaluation (see below) evaluates how reliably a model performs across languages, cultures, or dialects, other than the one it was created in.
- Post-deployment monitoring tracks whether performance stays consistent after launch, flagging whether outputs start to drift enough to merit a refreshed dataset or a new fine-tuning cycle.
Multilingual and Cross-Cultural AI Evaluation
The outputs of most models can start to vary significantly across different languages (which is a pattern we cover in more depth on our Red Teaming page). Evaluating a model in English only tells you nothing about how it will perform across the languages, dialects and accents it will encounter from your actual users, or the delicate cultural context and nuances baked into each.
Providing a fluent and consistent experience will involve:
- Native-language evaluation instead of translated test sets. Having native-speaking evaluators involved across evaluation criteria, prompts and scoring, rather than simply translating from an English test set, is vital for the accuracy of outputs.
- Cultural and contextual scoring is the difference between an answer that’s technically correct and one that’s culturally appropriate. Native evaluators provide contextual scoring to eliminate answers which are confusing or culturally inappropriate.
- Voice and dialect-aware evaluation uses natively delivered audio to test performance in real use-cases rather than simulating it from text.
Evaluating multilingual performance is separate to the localization process itself: it continuously measures how correct and consistent outputs are across languages and dialects. Evaluation is best left to native-speaking experts with in-depth understanding of their own language and culture. Our localization and multilingual evaluation services are backed by our global team of over 13,000 people.
Why Keywords Studios
- We don't build or own your model. We evaluate and test the model or agent that you bring to us. You retain the model, IP and data throughout.
- European-owned. We ensure full data sovereignty for enterprises and public sector organisations that need to keep work within a European ownership structure.
- 24/7 follow-the-sun production. A global studio network spans over 70 studios across 26 countries, which means evaluation work can continue around the clock.
- Security as standard. External clients assess our security posture over 150 times a year, across ISO 27001 and other certificates.
- End-to-end AI services. We provide one point of contact across the entire AI pipeline, covering data annotation, training data, Red Teaming, evaluation and localization, so there’s no need to juggle multiple vendors.
Translating Gaming QA to AI Evaluation
QA testing a game needs an in-depth understanding of how thousands of different systems can interact, then adding in the unpredictability of player actions on top. Across our 28 years of AAA game development and QA experience, we’ve built thorough and extensive pipelines which map directly onto the AI evaluation process, including:
- Multi-layered QA: AAA titles have multiple independent QA passes built into their production. We apply that same layering to AI evaluation, combining automated scoring, human review and continuous regression testing for every new model version.
- Structured coverage: With millions of lines of code and potential player actions to account for, AAA games need carefully planned, risk-prioritised checks across a wide range of scenarios, rather than a few ad-hoc spot-checks. We bring this same attention to detail to our AI testing and evaluation services: systematically mapping test cases against your models intended use.
- Built for non-deterministic systems: Testing a game means accounting for the near infinite ways that the systems and players will interact, with detailed documentation of inputs, sequences and edge cases. LLMs and AI agents share the same unpredictable, non-deterministic qualities we’ve spent decades refining our QA processes to measure and test.
- Testing against real-world conditions: We helped Valve test thousands of Windows games on the Steam Deck’s Linux-based operating system, validating against real player conditions. We use this same principle to test AI models against the real-world conditions they’ll face once they’re deployed.
Security, Governance & IP
Frameworks such as the EU AI Act and ISO/IEC 42001 are pushing for increased documentation and evidence on how AI systems are tested and evaluated.
Our studios and locations maintain security credentials and participate in industry and client assurance programmes such as ISO 27001. We don't build or operate our own AI models, so there's no in-house system your data could feed into, and nothing is shared across client engagements.
We work with 24 of the world's top 25 game publishers, and bring the same standards that have protected some of the gaming industry's most sensitive unreleased IPs for decades to our AI services.
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.
End-to-End AI Services, From Data to Deployment
Evaluation rarely happens in isolation. The same programme needs annotated data to measure against, Red Teaming to catch what a standard test can't catch, and training data or localization work when evaluation reveals a gap. We cover the full AI pipeline in-house, under a single contract.
Talk to Our AI Solutions Team
Get in touch to scope an evaluation programme, or see what we're already shipping for frontier AI labs and enterprise AI teams worldwide.
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Frequently Asked Questions
What's the difference between AI Evaluation and AI Red Teaming?
Evaluation and Red Teaming both look for potential points of failure or inconsistency within an agent, but evaluation measures performance against known and pre-defined criteria, whilst Red Teaming aims to discover new and unknown failures. Once a new finding has been confirmed via the Red Teaming process, it can then be added as a permanent regression test within the evaluation pipeline.
Do you evaluate AI performance in languages other than English?
Yes. We evaluate model output natively in 80+ written languages and 50+ spoken languages and dialects. We use native-speaking evaluators rather than translated test sets, to make sure that scoring reflects genuine linguistic and cultural context.
Does AI Evaluation cover AI agents as well as single models or LLMs?
AI agents and single models both require evaluation, though agentic systems have several additional factors to test, such as whether they can accurately complete multi-step tasks, use available tools correctly and behave in a consistent manner when chained with other agents.
How do you know if an AI system is ready to deploy?
The criteria for readiness are pre-agreed before AI evaluation begins. This could be accuracy thresholds, guardrail behavior, consistency of repeated outputs across languages, or any other set of factors based on the requirements and intended usage of the model.