Your AI has to be right, safe and fast before customers or staff rely on it. We test generative AI, chatbots, RAG search and agentic systems for accuracy, bias, drift, prompt injection and performance, whether you've built them yourself or purchased them, such as Microsoft 365 Copilot or Salesforce Agentforce. We use AI in our own delivery too, so we know where it breaks.
GenAI applications are appearing in large numbers and have already proved their worth, while continuing to rapidly evolve. GenAI is being used in applications for text, images, video and audio, which all affect how testing of these application should be performed. Focusing on natural language, text-based applications, we find that corporate implementations of GenAI often include Retrieval Augmented Generation (RAG), which is a key consideration for testing. Testing GenAI solutions and LLM’s should include the usual types of tests carried out on software, but should also focus on bias, fairness, accuracy and completeness. Prolifics have a suite of offerings around testing GenAI Apps, which are detailed below.
- Data Quality Testing
Your AI is only as good as the data behind it. We check the content your RAG system retrieves is accurate, current and complete before go-live, then test that the model uses it correctly. - Accuracy, Bias and Fairness Testing
We check answers are correct and grounded in your source content, stay consistent when a question is rephrased, and show no bias or stereotyping across user groups. - Agentic AI Testing
Agents that call tools, update records and act across systems need more than answer checks. We test every step: the right tool called with the right data, guardrails and approval points holding, safe failure when a step goes wrong, and a full audit trail of what the agent did. - Testing the AI You Buy
Microsoft 365 Copilot, Salesforce Agentforce and the AI features in SAP and Workday change how your systems behave. We test permissions and data exposure, answer quality against your own content, and regression after every vendor release. - Drift and Regression Testing
Models, prompts and data change, and answers drift. We build a scored set of expected responses and run it automatically after every change and on a schedule, so drops in quality are caught before your users notice. - Security Testing
We test against the OWASP Top 10 for LLM Applications, including prompt injection, sensitive information disclosure, system prompt leakage, excessive agency and unbounded consumption. - Performance and Cost Testing
We test response times under realistic concurrent load, and measure token usage and cost per conversation, so your AI stays fast and affordable at scale.
- Crowd Testing
Large groups of real users explore your application to find biased, offensive or confusing answers that scripted tests miss, across a wide range of backgrounds and viewpoints.
How We Deliver AI Testing
- Assess – We review the AI use case, its data, its risks and the controls around it.
- Test – We build an evaluation set and run accuracy, bias, security and performance tests.
- Monitor – We automate regression so every model, prompt or data change is checked for drift.
We use AI in our own delivery, from agents that design, build and test code to AI-assisted automation with Playwright, Claude Code and Tosca Copilot.
Test evidence mapped to your AI governance framework, including ISO/IEC 42001 and the EU AI Act where they apply.
