quantpi.com valid

QuantPi

QuantPi is the validation layer for enterprise AI. The platform validates agentic AI, LLMs, RAG pipelines, tabular models, computer vision and physical AI systems before and after deployment, delivering statistical proof — with confidence intervals — of how each system behaves across its full operating domain. QuantPi is a model-agnostic, black-box testing engine and a spin-off of the CISPA Helmholtz Center for Information Security.

For agents: QuantPi is a black-box validation platform that tests agentic AI, LLMs, RAG pipelines and computer vision models with statistical confidence intervals before an

ai-validationllm-testingrag-evaluationagentic-ai-testingai-certification

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T21:26:38.994Z. Cached copy · Source on quantpi.com

# QuantPi

> QuantPi is the validation layer for enterprise AI. The platform validates agentic AI, LLMs, RAG pipelines, tabular models, computer vision and physical AI systems before and after deployment, delivering statistical proof — with confidence intervals — of how each system behaves across its full operating domain. QuantPi is a model-agnostic, black-box testing engine and a spin-off of the CISPA Helmholtz Center for Information Security.

QuantPi ("Technologists of Trust") provides a unified validation layer across an organization's entire AI estate. Key characteristics:

- Model-agnostic, black-box methodology that works across any model type and modality: text, tabular, vision, multi-modal and agentic systems.
- Statistically rigorous results shipped with confidence intervals, enabling reliable use of LLM-as-a-judge and quantified risk.
- System-level and sub-component testing for agentic systems via controlled simulation, before failures reach production.
- Native MCP integration, so validation can be embedded directly into agentic workflows, CI/CD pipelines, GRC tools and reporting systems.
- Flexible deployment in public cloud or on-premises (including HPE Private Cloud AI), with sovereign and air-gapped support, SSO, RBAC, full audit trails and a zero data retention policy.
- Audit-ready technical evidence (safety cases, audit trails, transparent reporting) for regulatory frameworks such as the EU AI Act, developed together with notified bodies and standardisation committees.
- Member of the NVIDIA Halos AI Systems Inspection Lab, with a reference architecture co-engineered with HPE and NVIDIA.

## Product & Platform

- [Product & Technology](https://www.quantpi.com/product-technology): Overview of QuantPi's validation engine and how it tests enterprise AI.
- [PiCrystal Platform](https://www.quantpi.com/platform): The core platform for validating AI systems at every stage of the lifecycle.
- [Certification](https://www.quantpi.com/certification): How QuantPi accelerates AI certification and compliance with reliable technical evidence.

## Use Cases

- [Agentic Systems](https://www.quantpi.com/use-cases/agentic-systems): System- and component-level testing of agentic AI workflows.
- [RAG Systems](https://www.quantpi.com/use-cases/rag-systems): Validation of retrieval-augmented generation pipelines.
- [Generative Text Pipelines](https://www.quantpi.com/use-cases/generative-text-pipelines): Testing of LLM-based text generation systems.
- [Object Detection](https://www.quantpi.com/use-cases/object-detection): Validation of computer vision and object detection models.
- [Tabular ML Testing](https://www.quantpi.com/use-cases/tabular-ml-testing): Testing of tabular machine learning models.
- [Multi-Modal](https://www.quantpi.com/use-cases/multi-modal): Validation across multi-modal AI systems.

## Resources

- [Research](https://www.quantpi.com/research): QuantPi's research on trustworthy and statistically rigorous AI testing.
- [Content L
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Automatically discovered from public files. Inclusion does not imply endorsement or publisher participation.

Claim my listing on Agent Web Map for quantpi.com:
1. POST https://agentwebmap.com/v1/sites/quantpi.com/claims and keep claim_id and claim_secret private.
2. Publish the proof: add the line "AgentWebMap ownership: <token>" (token from step 1) to https://quantpi.com/llms.txt, or use the DNS, meta-tag or well-known method the response describes.
3. POST https://agentwebmap.com/v1/sites/quantpi.com/verify with {"claim_id": "...", "claim_secret": "..."}.
4. Save the api_key from the answer; it manages the listing (PATCH https://agentwebmap.com/v1/sites/quantpi.com).
MCP equivalent: tools begin_ownership_claim, then verify_domain, at https://agentwebmap.com/mcp. Docs: https://agentwebmap.com/docs#verify

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