Testing GenAI Performance and Reliability - Case Study
In high-stakes industries like finance, adopting GenAI requires trust, evidence, and rigorous testing. A global wealth management institution put this to the test with LatticeFlow AI.
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Introduction
In this exclusive case study from the Global AI Assurance Pilot, led by the AI Verify Foundation, you‘ll discover how a leading wealth management institution and LatticeFlow AI worked to technically evaluate a RAG-powered investment assistant. Together, the teams designed and executed targeted tests to surface performance gaps, flag risks, and deliver the insights needed to unlock AI adoption and innovation, safely.
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WHY IT MATTERS
Most GenAI pilots fail to scale, not because of a lack of ambition, but because of a lack of evidence-based insights on performance and risks.
This case study shows what it takes to move forward: concrete methods, measurable insights, and the right governance-to-operations bridge.
Inside the Case Study
Tested in a high-stakes pilot
Part of the Global AI Assurance Pilot led by the AI Verify Foundation.
Proven framework for adoption
How to enable safe, scalable GenAI, without blocking innovation.
Real-world GenAI application
Built on RAG architecture to assist relationship managers with investment insights.
Risk-focused technical checks
- Accuracy, soundness, and relevance.
- Transparency and intended-use alignment.
- Hallucinations, bias, cybersecurity risks & more.
Access the Case Study
Learn how a leading wealth management institution and LatticeFlow AI partnered to validate GenAI for finance and build trust through testing.