Generative AI Testing
Generative AI Testing Services That Go Beyond the Lab
Top innovators choose Applause to train, test and continuously improve Gen AI apps and features.
Optimize Generative AI Systems for Every Use Case
Your Gen AI is only as good as how it performs in the real world. We test it there.
Gen AI is probabilistic. A model that performs well in a controlled eval can still hallucinate, produce biased outputs, or fail in real user conditions — and it can do so differently every time.
Applause provides fully managed Gen AI testing and evaluation services that help you catch those failures before your users do. From domain expert evals and fine-tuning to red team testing and LLM-as-judge pipelines, we give you on-demand access to the diverse testers, real-world data, and independent methodology needed to ship Gen AI you can stand behind.
An Independent AI Quality Layer You Can't Build Internally
This is not just another eval workflow — it’s a one-of-a-kind independent AI quality layer. With years of experience testing the world’s leading Gen AI models and applications, Applause helps ensure Gen AI systems are functional, intuitive, inclusive and safe with expert-led red teaming to uncover vulnerabilities, global testing coverage including domain experts and end users, and an independent AI evaluation layer combining human and AI review. Domain expert judgment with a rigorous, multi-model AI infrastructure delivers evaluations that are scalable, independent and built on defensible statistical methodology.
Domain Expert Anchoring
Real specialists in legal, medical, financial and other high-stakes domains establish authoritative ground truth. Benchmarks reflect the standards that matter in your industry — not just what a general-purpose model was trained to expect.
Vendor-Independent Evaluation
Applause has no stake in any model, platform or outcome. That structural independence is rare — and it's exactly what organizations need when the evaluator's credibility has to be beyond question.
Multi-Model Jury
Three or more independent frontier models from different vendors evaluate outputs in parallel using structured rubrics. Agreement is quantified using inter-rater reliability metrics, and disagreement triggers escalation to human expert review.
Real-World Coverage
Evaluation spans languages, geographies and user contexts — so testing reflects your actual market, not a lab. Semantic similarity, fact-checking, rubric-based scoring and similar methodologies are applied to multiple data modalities (text, image, audio, video, etc.)
Compound System Evaluation
Applause evaluates RAG pipelines, multi-step tool-using agents and orchestrated multi-model workflows with trace-level assessment, step-by-step correctness checking, tool-call accuracy, retrieval relevance scoring and end-to-end task completion metrics.
Continuous Improvement
Evaluation outputs provide quantitative and qualitative insights enterprises can use to fine-tune AI systems over time — in other words, an authoritative benchmark or “golden dataset” that can be leveraged in future regression testing.
Red Team Testing
AI safety failures don't wait for scheduled tests. Applause assembles expert red teams — diverse by design — to probe your Gen AI for bias, toxicity, jailbreak vulnerabilities and edge-case failures before they reach users or regulators.
User Experience Research
Exploratory research, UX studies, longitudinal studies, benchmarking studies, inclusive design and other methodologies help ensure the Gen AI experience is actually engaging, intuitive and trusted in the real world.
Ready to Learn More About Generative AI Training and Testing With Applause?
Find out how you can optimize your customer experience, drive engagement, innovate faster and launch confidently at scale. We’ve helped the most innovative brands in the world launch effective, trusted AI solutions.
- The largest, most diverse community of digital testing experts and end users providing the breadth and depth of insights required for high-quality AI experiences
- Access to millions of real devices and configurations in over 200 countries and territories
- Custom teams with specialized expertise in AI training and testing, including conversational systems, Gen AI models, image/character recognition, machine learning and more
- Model optimization and risk reduction techniques to mitigate bias, toxicity, inaccuracy and other potential AI harms
- Real-time insights and actionable reports enabling continuous improvement
- Seamless integration with existing Agile and CI/CD workflows
- Highly secure and protected approach that conforms to information security best practices
Dive Deeper Into Digital Quality
From customer stories to expert insights, our Resource Center offers a deeper look at how we approach digital quality.
