QuantPi joins NVIDIA Halos AI Systems Inspection Lab
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Compliant Generative Text Pipelines

QuantPi offers rigorous alignment, safety, and robustness testing for enterprise Large Language Models (LLMs) and fine-tuned generative text pipelines. These systems process unconstrained natural language distributions to automate financial tasks, extract entity insights, or generate programmatic data outputs. Because open-ended generation exposes enterprise pipelines to complex semantic variations and non-deterministic behavior, model constraints and operational boundaries must be assessed continuously before production.

Typical Failure Modes

What basic validation benchmarks conceal

Format & Schema Rejection
The model fails to output structured payloads under edge-case prompts, breaching required enterprise system interfaces.
Hallucinations
Generating factually unsupported claims, semantic contradictions, or untrustworthy content that violates strict source parameters.
PII & Sensitive Data Leakage
Accidental exposure of personally identifiable information or failure of content moderation filters under diverse input queries.
testing approach

How QuantPi validates generative text pipelines

Domain InformationDimensional DecompositionAcceptance CriteriaDeployment Decision

Domain Information

Every assessment begins by defining the target functional domain boundaries: runtime formatting restrictions (JSON, SQL, code syntax), query distribution profiles, language parameters, and corporate content safety envelopes.
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Dimensional Decomposition

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Generative behavior is rigorously mapped across six primary performance and alignment dimensions using automated data embedders and perturbers:
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• Text generation and QA quality: Measuring token-level overlap via BLEU, ROUGE, and METEOR versus semantic similarity via BERTScore.
• Output validation rates: Evaluated against strict Pydantic schemas and regular expressions.
• Faithfulness and factual consistency: Quantified using specialized cross-model judges including HHEM-2.1.
• Inherent behavioral stability: Calculating an inherent stability score across identical query replicas to detect probabilistic variance.
• Safety, PII, and sensitive data exposure: Triggering automated content moderation flags and PII presence tracking.
• Dataset distribution and representation shifts: Analyzing representation gaps and context content diversity profiles.

To eliminate evaluation bias, testing leverages AI-driven user simulation models to stress-test execution boundaries. All performance scores are strictly reported as a Metric + Confidence Interval pair to statistically quantify uncertainty stemming from data constraints or stochastic model environments.

Acceptance Criteria

Acceptance criteria are predefined, measurable performance thresholds the system must meet on each tested dimension to qualify for deployment. For LLMs, they are evaluated as format parsing compliance and factual grounding coefficients under controlled perturbation sweeps, demanding strict threshold conformance on corporate data protection and schema alignment vectors.
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Deployment Decision

Models passing all structural and content safety thresholds are cleared for live API routing; localized failures isolate the exact validation schemas or context windows requiring targeted prompt refinement or fine-tuning iteration.
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Driving Real-World Impact with Trusted AI

Real-world examples of how companies use QuantPi to build trustworthy AI — from identifying weaknesses to achieving reliable, production-grade performance

Car damage detection for insurance claims

Computer vision model to analyze vehicle images to identify and segment damage, enabling automated insurance coverage estimation to customers.

Client/Partner

ControlExpert

Challenge

Ensuring the robustness of a car damage segmentation model for diverse real-world conditions such as weather, time of day, car pose) is difficult and requires costly manual data annotation.

Assessment Scope

The QuantPi platform was leveraged to rigorously test the model’s performance in depth and continuously allow for improvements to the computer vision model in a matter of hours, instead of weeks. 

Metrics

Automated weather perturbation testing (e.g. day/night, clear/rain) was implemented using Stable Diffusion and CLIP models to identify outdoor images.

Data annotation was automated by using a CLIP model to dynamically tag images with relevant attributes (e.g. car pose, indoor/outdoor, raindrops).
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Model performance was evaluated by using IoU across tagged attributes to identify weaknesses and guide targeted improvements

Business Impact

- Reduced manual effort for Data Scientist
- Increase model performance
- Reduced claims processing time

RAG & LLM for Factory Troubleshooting in Life Sciences

LLM and RAG based AI system for factory troubleshooting. Assisting line workers in identifying the root cause to issues quickly and enabling speedy resolution.

Client/Partner

Violet AI
Read the entire success story

Challenge

Violet AI needed to demonstrate the quality and reliability of their RAG-based AI systems, particularly regarding document retrieval accuracy and response faithfulness, to meet compliance demands in the life sciences industry, including the EU AI Act.

Assessment Scope

The QuantPi platform was leveraged to address these challenges, rigorously evaluating the RAG and LLM systems' performance and preparing Violet AI for compliance.

Metrics

We evaluated RAG system quality, incorporating metrics for retrieval accuracy (LAcc, SAcc, MRR) and LLM faithfulness/hallucination (based on RAGAs framework and existing out of the box metrics from QuantPi).

Business Impact

- Reduced manual effort for Data Scientist
- Increase model performance
- Reduced claims processing time

AI-Powered Investment Research Assistant

Agentic workflow using LLMs as an orchestrator for summarization, multi-turn chatbots, and data extraction from unstructured sources.

Client/Partner

Unique
Read the full case study

Challenge

The system faced significant risks regarding the failure to meet regulatory requirements or internal company guidelines (e.g., recommending restricted products). Other critical concerns included the potential oversharing of client data, inconsistent advice quality across different customer segments, and the risk of poor financial outcomes

Assessment Scope

QuantPi provided a comprehensive, black-box AI testing technology to evaluate the model for performance, bias, robustness, and data quality. The pilot testing focused specifically on three core areas: Accuracy, hallucination and reliability.

Metrics

Hallucination Risks: Measured via a faithfulness metric to ensure responses were grounded in the provided context.
Document Search Risks: Assessed using Word Overlap Rate, Mean Reciprocal Rank (MRR), and Lenient Retrieval Accuracy.
Reliability: Tested across various query difficulty levels, domain bias, and typo tolerance.

Business Impact

- Risk Mitigation: Identified and addressed risks related to advisor misuse and inadequate clarity in investment recommendations.
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- Performance Assurance: Guaranteed the grounding of AI-generated advice in provided financial contexts to prevent misinformation.

CV Matching System

StepStone develops an LLM-based candidate recommender system which automatically provides recruiters with candidate recommendations purely based on job listings.

Client/Partner

StepStone, TÜV AI.LAB
Read the full white paper

Challenge

Stepstone needed to validate and justify the fairness of their LLM-based candidate recommender system, particularly regarding non-discrimination of applicants based on age, gender and ethnic origin.

Assessment Scope

The QuantPi Platform was used to assess false negative and selection rates of the system with regards to the sensitive attributes and their perturbations – resulting in quantitative test results on non-discrimination on individual and group level.

Metrics

An assurance case was built together with TIC partner TÜV AI.Lab. This document serves as structured line of argumentation and links the quantitative evidences generated in the QuantPi Platform to applicable legal requirements under Germany’s Equal Treatment Act (AGG) and future provisions of the EU AI Act.

Business Impact

- Reduced bias and discrimination
- Increased system performance
- Compliance documented in assurance case

QuantPi’s LLM evaluation suite produces:

A format and schema parsing report specifying validation failure rates across structural Pydantic data layers.

A factual consistency diagnostic detailing hallucination scores and grounded context precision tracking.

A traceable compliance evidence package validating model guardrail parameters for data privacy reporting and corporate risk auditing.

Applied across automated financial report generation, intent classification systems, and structured document processing pipelines.

See QuantPi's continuous alignment auditing and data integrity testing for custom enterprise models.

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