QuantPi Joins NVIDIA Halos AI Systems Inspection Lab Ecosystem to Advance Trustworthy Physical AI
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Built on math.
‍Proven in production.

The QuantPi platform runs on a proprietary mathematical framework developed at one of the world’s leading research centers for AI security. Not LLMs judging LLMs. Not benchmarks that haven’t changed in decades. Peer-reviewed, institutionally validated and embedded in every test we run.

Trusted by Enterprise Customers

Validated by institutions and venues that don’t compromise

Why is Research so important to us?

Mathematical Objectivity

Move beyond subjective benchmarks and LLMs judging LLMs with our deterministic mathematical engine. Our platform provides the reproducible results required for high-stakes enterprise decision-making.

Peer reviewed validation

Our test methodologies are scrutinized by the world’s leading AI researchers. This external rigor ensures your assessments are built on a foundation of globally recognized academic integrity.

Future-proof testing

We bridge the gap between frontier AI research and enterprise-grade testing. Our platform is continuously updated with technical assessment metrics for novel AI architectures or data modalities.

Research Validation

Our commitment to advancing artificial intelligence responsibly

QuantPi is in continuous exchange with world-leading researchers in machine learning, data science and mathematics. Having emerged from fundamental research, we are convinced that this is a fundamental prerequisite to ensure assessments of AI systems are trustworthy themselves.

A black-box framework for testing any ML model

Funded by the German Federal Ministry for Economic Affairs. Model-agnostic, no access to business critical IP required. The architectural principle behind QuantPi's engine.
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Finding where malware detectors silently fail

EU-funded within ELSA. Subgroup discovery algorithms that expose localized failure regions invisible to global accuracy metrics. Applied to production malware detection.
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How to measure whether synthetic data is actually safe to use

With HPE, CISPA, and DZNE. Novel authenticity metrics that catch mode collapse and minority-group suppression — the failure modes most relevant to assess AI systems for bias.
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Test Criteria and Certification of AI Systems

Zertifizierte KI: QuantPi is an associated partner in the German Lighthouse Project on the development of testing criteria for AI systems.
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Operationalization of AI Tests

DIN, DKE: QuantPi co-authors the second version of the German AI Standardization Roadmap defining requirements for AI testing tools.
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Documenting Explainability Methods for AI

DIN: QuantPi co-authors DIN SPEC 92001-3 which provides a domain-independent guide on explainability methods of AI system life cycle stages.
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Shape Partition of Tensors and Eigenvalue Problem

SIAM Review: QuantPi Co-Founder co-authors a paper on the novel applications of the nonlinear Perron-Frobenius theory, published in SIGEST.
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Defending API-Based Attacks on ML Models

Federal Ministry of Education and Research: QuantPi developed a technological framework to optimize the utility-privacy trade-off.
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Machine Learning Security in Practice

IEEE Transactions on Information Forensics and Security: QuantPi co-authors a study on the occurrence of attacks on AI systems in industry.
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Trustworthy AI Tool Support

OECD.AI Policy Observatory: QuantPi's AI Trust Platform is in the catalogue of tools and metrics designed to help with trustworthy AI.
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Trustworthiness of Generative AI

European Innovation Council: QuantPi is tasked to develop the first automated risk management platform for generative AI systems.
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Mental Models of Adversarial Machine Learning

SOUPS: QuantPi co-authored and presented a study on practitioners’ perception of vulnerabilities in AI systems.

Interested
in Collaborating

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