How teams go from AI uncertainty to production confidence
Real-world examples of how companies use QuantPi to build trustworthy AI from identifying weaknesses to achieving reliable, production-grade performance.
NVIDIA validates PeopleNet at scale, with statistical confidence no benchmark could provide
~1 million metric values computed across 15 contextual properties and 7 perturbation types
Statistical confidence intervals per estimate; NVIDIA NIM services integrated to scale annotation
Methodology and findings presented at NVIDIA GTC, March 2025
System:
Computer Vision Model
Modality:
Image
Risk:
Performance, Robustness, Fairness, Bias
AI System & Context
Testing Approach
Customer Impact
Global Fortune 100 Technology and Enterprise Solutions Provider
Fortune 100 enterprise cut hallucination risk in its internal policy RAG
47% improvement in end-to-end system success driven by prompt optimization
36% relative gain in faithfulness to systematically mitigate hallucination risks
Automated evaluation covering 20 distinct scenarios replacing ad-hoc manual spot-checks
System:
LLM + RAG
Modality:
Text
Risk:
Accuracy, Robustness, Faithfulness
AI System & Context
Testing Approach
Customer Impact
European motor-claims AI provider
Continuous robustness assurance for a vision model in claims processing
Validation cycles cut from weeks to hours
Silent failure modes surfaced across weather, lighting, and vehicle-angle variations
Continuous testing replacing manual annotation as the bottleneck
System:
Computer Vision Model
Modality:
Image
Risk:
Performance, Robustness
AI System & Context
Testing Approach
Customer Impact
European recruiting platform built quantitative evidence of non-discrimination
Mapped to Germany's AGG and the EU AI Act
Quantitative bias testing across age, gender, and ethnic origin
Assurance case co-developed with TÜV AI.Lab
Bias surfaced and reduced; system performance improved
System:
LLM-based recommender system
Modality:
Text
Risk:
Fairness, Non-discrimination
AI System & Context
Testing Approach
Customer Impact
European provider of AI-driven claims-processing infrastructure
Standards-aligned assurance for an agentic AI claims-processing system
Test coverage scaled from 32 to 1,200 scenarios; metric library expanded from 17 to 85
8 risk criteria adapted from ISO/IEC 25010 and ISTQB, replacing 2 ad-hoc criteria
Continuous testing integrated into the customer's CI/CD pipeline
Testing depth and cost made measurable and controllable
System:
Agentic AI
Modality:
Text, Image
Risk:
Performance, Robustness, Fairness, Functional Suitability
AI System & Context
Testing Approach
Customer Impact
Black-box assurance for Unique's agentic AI investment research assistant
Published as a pilot case by Singapore's AI Verify Foundation.
Tested for accuracy, faithfulness, and robustness across realistic query conditions
Two-layer assessment: the agent itself plus its retrieval subsystem
Reusable test scenarios that transfer across LLM providers and model versions
System:
Agentic AI (RAG)
Modality:
Text
Risk:
Accuracy, Hallucination, Robustness
AI System & Context
Testing Approach
Customer Impact
Quality assurance for Violet AI's RAG system in life-sciences manufacturing
End-to-end RAG validation: retrieval accuracy and LLM faithfulness
Continuous, repeatable evaluation replacing ad-hoc spot-checks
Reduced manual effort for data scientists; improved system reliability
System:
LLM + RAG
Modality:
Text
Risk:
Accuracy, Hallucination, Faithfulness
AI System & Context
Testing Approach
Customer Impact
