Gen AI Assurance
Product owners and QA teams know how slow a manual chatbot review is and how little confidence a handful of scripted test cases gives them before launch. Using Nimbus AI, we evaluate real conversations across business-relevant dimensions from accuracy and…
Trustworthy chatbots, tested from data to dialogue
Product owners and QA teams know how slow a manual chatbot review is and how little confidence a handful of scripted test cases gives them before launch. Using Nimbus AI, we evaluate real conversations across business-relevant dimensions, from accuracy and tone to hallucination risk, in both LLM and non-LLM models.
The assessment reaches past the agent to the reliability of the application, the integrity of its data and its performance under load. It connects with RAG pipelines and APIs, generates test cases automatically and reports through dashboards that business teams can read.
How GenAI Assurance works
GenAI system assurance
We look at how your GenAI chatbot holds up in the real world, not just in ideal conditions. Whether users type in confusing prompts, the network slows down, or something unexpected happens, we check if your system stays steady and safe. With Nimbus AI’s confidence scoring, you get a clear sense of where the risks are, so you can fix issues before they reach production.
GenAI agent assurance
Good chatbots carry conversations. We provide enterprise-grade assurance by testing how well your chatbot remembers past messages, stays on topic, and maintains a consistent tone. Pick your industry and sub-domain, then Nimbus helps you catch glitches in memory, logic, or tone early, ensuring conversations feel smooth, natural, and trustworthy over time.
GenAI application assurance
Users want the right answers, in an interface that makes sense. We test how your chatbot performs across the entire experience, not just the model behind it. From the user interface and design flows to API and support tool integration, we ensure it all works together, driving fewer drop-offs and faster resolution.
GenAI data assurance
Your chatbot is only as good as the information it runs on. Using Datagaps-powered validation, we check the quality and freshness of your prompt libraries, grounding docs, and vector stores to ensure answers are relevant and fact-based. That means fewer hallucinations, more credibility, and higher trust, especially in domains where facts matter.
What the service includes
QKTech framework for robust AI testing and model trust
Real-time analysis of live AI assistant conversations
Non-LLM evaluation for transparent, model-free testing
AI-driven scoring across 60+ evaluation parameters
Automated, reliable reports and QA dashboards
Integration with RAG pipelines and APIs
Insights
Challenges we’ve met
Success Stories
Latest Insights
Common questions about Gen AI assurance
What is Gen AI assurance?
Gen AI assurance is the testing and monitoring of chatbots and other generative AI applications to confirm that their answers are accurate, safe and consistent. It covers the data behind the model, the conversations it holds and the application around it.
What does QKTech test in a chatbot?
We test four layers. System assurance checks the chatbot under real-world conditions, and agent assurance checks memory, topic adherence and tone. Application assurance covers the full experience, including UI, APIs and support tools, while data assurance checks the quality and freshness of the material that grounds each answer.
How are hallucinations and tone problems detected?
We evaluate real conversations across multiple dimensions, including accuracy, tone and hallucination risk, and confidence scoring flags risk before a chatbot reaches production. Continuous monitoring then surfaces drift after launch.
Can QKTech assure a chatbot that does not use an LLM?
Yes. The evaluation works for both LLM and non-LLM models.
Does the service work with RAG pipelines and APIs?
Yes. It integrates with RAG pipelines and APIs, and data assurance checks the prompt libraries, grounding documents and vector stores that feed them.
How do business teams use the results?
Automated reports and QA dashboards present the findings with explainability, and audit trails support compliance. Feedback captured after launch helps refine the model, and A/B testing validates each improvement.
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