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We begin with an introductory questionnaire to assess governance maturity. This easy-to-use document helps define scope by identifying organizational goals, stakeholders, and roles, existing AI inventory and lifecycle management, and jurisdictional relevance (industry, region, etc).
Our team is a group of AI experts with a focus on extending organizational norms to the AI context. We will avoid replacing any policy or framework unless achieving agreed objectives makes it absolutely necessary. This step starts with reviewing existing documents to understand established policies, governance charters, risk management frameworks, data management and protection, AI usage controls, and previous audit results. We will cross-reference for alignment with concrete frameworks such as the NIST AI Risk Management Framework and the European Commission.
We then evaluate the organization’s governance framework to assess roles and responsibilities, use of internal and external AI ethics boards, departmental and executive oversight, organizational culture, training, and incident reporting.
Our process will then map organizational policies to confirm conformance with seven core principles: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, societal and environmental wellbeing, and accountability. The is a key step in identifying where weaknesses exist in the integration of policies and the intended behaviors of the organization.
During this stage, we interview leadership, compliance officers, developers, and impacted stakeholders to assess the human oversight models employed by the organization. For example, are human-in-the-loop or human-in-command oversight models implemented and rigorously followed?
This step evaluates risk identification efficacy to assess if processes are designed to cover key criteria, such as bias, discrimination, accessibility, security, transparency, and interpretability, and confirm they are lifecycle-based to ensure end-to-end design-to-retirement management.
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Our efficacy testing offers an integrated, hands-on approach that rigorously evaluates your AI’s accuracy, reliability, and efficiency against real-world benchmarks. We enable rapid iteration through collaborative feedback loops—linking your technical teams, dashboards, and data annotation—to continuously improve model performance and governance.
Our ground truth generation and enhancement services strengthen your AI’s learning, accuracy, and adaptability across changing environments. By continuously evolving this foundation and pairing it with efficacy testing, we help prevent costly errors and reputational or legal risks from AI hallucinations.
Our smart knowledge base editor and reference lookup tools keep your AI’s outputs accurate, compliant, and up to date. They enable real-time updates to reflect new information or regulations, ensuring the integrity and trustworthiness of every AI interaction.
Our bias and fairness testing identify and mitigate hidden disparities in AI models to ensure equitable outcomes across user groups. Through continuous monitoring and dataset refinement, we help organizations build systems that are transparent, inclusive, and aligned with ethical and regulatory standards.
Our privacy audits evaluate data collection, storage, and usage practices to safeguard personal and sensitive information throughout the AI lifecycle. By enforcing privacy-by-design principles and compliance with evolving regulations, we minimize data exposure risks while preserving model performance.
Our AI security assessments detect vulnerabilities, adversarial risks, and system weaknesses that could compromise model integrity or user trust. We strengthen defenses through penetration testing, threat modeling, and resilience strategies that ensure safe, reliable AI operations.
Our interpretability and explainability audits make AI decisions transparent, traceable, and understandable to both technical and non-technical stakeholders. By embedding explainable AI techniques and documentation standards, we enhance accountability, user trust, and regulatory readiness.
What better way to test your AI vulnerabilities than by asking a team that’s been building AI and machine learning systems for decades to break it. We know where the bones are likely to be buried, and we’ll find them.
Your organization will receive a prescriptive report with executive summary, compliance assessment, gaps, risks, and a roadmap for policy and governance improvements. The report will include a scorecard measuring governance maturity, strengths, weaknesses and high-priority gaps across each domain. This high-value document establishes the baseline for future audits and prepares your organization for standards certifications and future regulatory requirements.
A standardized audit process aligned to a predictable scope and defined business objectives.
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Monthly, quarterly, or annual evaluations for AI projects requiring progress assessments or post-deployment compliance certifications.
Recurring monthly fee-based engagements for rolling audit support and AI readiness services.
When open-ended enterprise projects with less predictable scope or project timelines require an adaptable approach.
Answer three questions across five categories to help determine your organization's AI-readiness.
FAQ
Comprehensive AI governance assessments for enterprises.
Technical risk analysis tailored to your industry.
Actionable gap analysis for trustworthy AI.
