Independent, expert-led AI qualifications
Evaluate Vendors’ AI design, data use, and compliance with regulator-aligned assessments.
OUR FOCUS
Gain clear, defensible visibility into a Vendor’s AI maturity, data integrity practices, and compliance posture.
Gain clear, defensible visibility into a Vendor’s AI maturity, data integrity practices, and compliance posture.
Our expertise
Deep technical expertise for GxP environments
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Leverage deep expertise on AI compliance from industry veteran auditors.
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Stay compliant with the latest AI capability standards and evolving regulatory landscape.
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Stay ahead of potential risk and make informed Vendor decisions.
Leverage deep expertise on AI compliance from industry veteran auditors.
Stay compliant with the latest AI capability standards and evolving regulatory landscape.
Stay ahead of potential risk and make informed Vendor decisions.
Leverage deep expertise on AI compliance from industry veteran auditors.
Stay compliant with the latest AI capability standards and evolving regulatory landscape.
Stay ahead of potential risk and make informed Vendor decisions.
Leverage deep expertise on AI compliance from industry veteran auditors.
Stay compliant with the latest AI capability standards and evolving regulatory landscape.
Stay ahead of potential risk and make informed Vendor decisions.
Our audits
Comprehensive qualification support
Our platform and team of experts enable you to apply rigorous, industry-aligned frameworks to every Vendor evaluation.
Our platform and team of experts enable you to apply rigorous, industry-aligned frameworks to every Vendor evaluation.
Our platform and team of experts enable you to apply rigorous, industry-aligned frameworks to every Vendor evaluation.
Our platform and team of experts enable you to apply rigorous, industry-aligned frameworks to every Vendor evaluation.
AI-focused RFI templates mapped to current regulations to ensure you’re asking the right questions of every Vendor.
SME-led risk scoring and risk intelligence to flag issues early.
Custom, regulator-aligned AI audits tailored to your study, Vendor type, and needs.
AI-focused RFI templates mapped to current regulations to ensure you’re asking the right questions of every Vendor.
SME-led risk scoring and risk intelligence to flag issues early.
Custom, regulator-aligned AI audits tailored to your study, Vendor type, and needs.
AI-focused RFI templates mapped to current regulations to ensure you’re asking the right questions of every Vendor.
SME-led risk scoring and risk intelligence to flag issues early.
Custom, regulator-aligned AI audits tailored to your study, Vendor type, and needs.
AI-focused RFI templates mapped to current regulations to ensure you’re asking the right questions of every Vendor.
SME-led risk scoring and risk intelligence to flag issues early.
Custom, regulator-aligned AI audits tailored to your study, Vendor type, and needs.
Audit Coverage
Audit coverage
Thorough, industry-aligned AI assessments
Diligent evaluates the full lifecycle of a vendor’s AI capabilities, with emphasis on transparency, compliance, and ethical use.
AI Architecture & Model Design
Type of AI models used (LLMs, ML models, statistical models, generative tools)
Model provenance, training pipeline, and update cadence
Use of open-source, commercial, or proprietary models
Prompt management, model context windows, and output control mechanisms
Data Use, Training, and Privacy
Whether and how customer data is used for model training or fine-tuning
Data ingestion flows, preprocessing, and anonymization practices
Isolation of customer data from model inference/training layers
Controls to prevent unintended data retention, leakage, or memorization
Compliance with GDPR, HIPAA, and AI-specific governance frameworks
Third-Party AI & Embedded Tools
Identification of external AI services or APIs embedded in the product
Risk analysis of dependencies (e.g., model providers, ML infrastructure, vector databases)
Contractual and technical safeguards around third-party data use
AI Governance, Auditability & Safety
Documented AI policies, acceptable use frameworks, and human-in-the-loop oversight
Model monitoring, bias detection, versioning, and performance drift tracking
Explainability and transparency practices
Security and misuse prevention for model endpoints
Guardrails, red-team testing, and hallucination mitigation strategies
Regulatory Alignment & Readiness
Preparedness for evolving global AI regulations (EU AI Act, U.S. frameworks, ISO standards)
Internal expertise, accountable roles, and governance structures
SOPs, validation, and change-management processes for AI-enabled features
AI Architecture & Model Design
Type of AI models used (LLMs, ML models, statistical models, generative tools)
Model provenance, training pipeline, and update cadence
Use of open-source, commercial, or proprietary models
Prompt management, model context windows, and output control mechanisms
Data Use, Training, and Privacy
Whether and how customer data is used for model training or fine-tuning
Data ingestion flows, preprocessing, and anonymization practices
Isolation of customer data from model inference/training layers
Controls to prevent unintended data retention, leakage, or memorization
Compliance with GDPR, HIPAA, and AI-specific governance frameworks
Third-Party AI & Embedded Tools
Identification of external AI services or APIs embedded in the product
Risk analysis of dependencies (e.g., model providers, ML infrastructure, vector databases)
Contractual and technical safeguards around third-party data use
AI Governance, Auditability & Safety
Documented AI policies, acceptable use frameworks, and human-in-the-loop oversight
Model monitoring, bias detection, versioning, and performance drift tracking
Explainability and transparency practices
Security and misuse prevention for model endpoints
Guardrails, red-team testing, and hallucination mitigation strategies
Regulatory Alignment & Readiness
Preparedness for evolving global AI regulations (EU AI Act, U.S. frameworks, ISO standards)
Internal expertise, accountable roles, and governance structures
SOPs, validation, and change-management processes for AI-enabled features
AI Architecture & Model Design
Type of AI models used (LLMs, ML models, statistical models, generative tools)
Model provenance, training pipeline, and update cadence
Use of open-source, commercial, or proprietary models
Prompt management, model context windows, and output control mechanisms
Data Use, Training, and Privacy
Whether and how customer data is used for model training or fine-tuning
Data ingestion flows, preprocessing, and anonymization practices
Isolation of customer data from model inference/training layers
Controls to prevent unintended data retention, leakage, or memorization
Compliance with GDPR, HIPAA, and AI-specific governance frameworks
Third-Party AI & Embedded Tools
Identification of external AI services or APIs embedded in the product
Risk analysis of dependencies (e.g., model providers, ML infrastructure, vector databases)
Contractual and technical safeguards around third-party data use
AI Governance, Auditability & Safety
Documented AI policies, acceptable use frameworks, and human-in-the-loop oversight
Model monitoring, bias detection, versioning, and performance drift tracking
Explainability and transparency practices
Security and misuse prevention for model endpoints
Guardrails, red-team testing, and hallucination mitigation strategies
Regulatory Alignment & Readiness
Preparedness for evolving global AI regulations (EU AI Act, U.S. frameworks, ISO standards)
Internal expertise, accountable roles, and governance structures
SOPs, validation, and change-management processes for AI-enabled features
rESOURCES
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