CHALLENGE
A disseqt AI partner was tasked with testing an enterprise AI application used for internal decision-making workflows. As organisations rely more on AI-powered processes, the bar for reliability, accuracy, and consistency is exceptionally high.
The core challenges were:
Lack of structured test coverage — Without diverse, representative test scenarios, gaps in AI behaviour go undetected until they reach end users.
No standardised evaluation metrics — Testing was inconsistent, with no clear framework to measure answer relevancy, factual consistency, or response quality.
Enterprise readiness at risk — Without a repeatable QA process, scaling the AI solution confidently across the organisation was not feasible.
PROCESS
01
Prompt pack creation
Diverse and representative test scenarios are generated to cover the full range of real-world inputs the application is likely to encounter, ensuring no critical edge cases are missed.
02
Metric-based evaluation
Each AI response is evaluated against a defined set of quality metrics including answer relevancy, factual consistency, and response quality, providing an objective, repeatable measure of model performance.
03
Results review and sign-off
Evaluation results are reviewed to identify failure patterns, surface improvement areas, and confirm the AI model meets enterprise-grade standards before deployment or scaling.
OUTCOMES
85%+ Accuracy Achieved
Model accuracy improved beyond 85%, meeting the threshold required for enterprise deployment.
Significant Time Savings
Structured testing pipelines reduced the time spent on manual QA and review cycles.
Lower Operational costs
Automation of the evaluation process reduced the overhead associated with AI testing and validation.
Scalable Enterprise-ready AI
A repeatable framework now supports confident rollout of AI solutions across the organisation.
