Built on Expertise.
Grounded in Principle.

AxiLayer AI was established by professionals with extensive, hands-on experience in AI systems architecture, machine learning engineering, and enterprise technology governance. Our founding team brings decades of combined expertise building, deploying, and evaluating AI systems across industries — and recognized, long before regulators codified it, that the field urgently needed an independent certification body with genuine technical credibility.

Founding Partner and CEO Ovi Pinzaru brings over 20 years of enterprise technology leadership at IBM (Director of Enterprise Architecture), Hewlett Packard Enterprise (Global IT Infrastructure Leader), and FDaaS Group (CTO). His career spans Fortune 500 client engagement, enterprise AI governance, and the design of MLOps and LLMOps ecosystems at global scale. That hands-on engineering foundation — combined with Anisa Kimmig's expertise in financial governance and enterprise operations — is what distinguishes AxiLayer AI from advisory firms staffed by generalists.

AxiLayer AI, Inc. is incorporated as a Delaware Corporation, headquartered at 300 Colonial Center Parkway, Roswell, Georgia. We operate with zero conflicts of interest — no technology vendor relationships, no platform affiliations, no commercial interests in the AI systems we assess. Our sole function is objective, independent assurance. That independence is not a feature — it is the foundation.

The Expertise Behind
Every Engagement

AxiLayer AI is led by experienced founders supported by a distinguished advisory board — combining deep AI engineering expertise, financial governance, and strategic counsel to deliver the technical credibility and institutional discipline that enterprise and government clients require.

Independence

We have no commercial relationships with AI vendors, platforms, or technology providers. Our only obligation is to objective, evidence-based assessment. This is non-negotiable.

Integrity

Every finding we produce reflects the evidence — nothing else. We do not adjust conclusions to accommodate client preferences or commercial relationships. Our certifications must be trusted to be valuable.

Excellence

We apply the highest standards of professional competence to every engagement. Our methodology is rigorous, our documentation is thorough, and our deliverables are actionable.

Built for Independence

AxiLayer AI, Inc. is incorporated as a Delaware Corporation, established in January 2026, and headquartered at 300 Colonial Center Parkway, Suite 100A, Roswell, Georgia 30076.

Our corporate structure is designed to protect and preserve our independence. We maintain strict separation between our certification activities and any commercial interests in the AI industry. Our governance policies — including our Conflict of Interest Policy, Ethics Code of Conduct, and Client Confidentiality Policy — are in full effect for all personnel and engagements.

AxiLayer AI operates in accordance with internationally recognized conformity assessment standards, applying the same rigor to AI systems that financial auditors apply to financial statements.

Our Framework Commitments

• EU AI Act — Full compliance with Regulation (EU) 2024/1689 conformity assessment requirements
• NIST AI RMF 1.0 — Structured risk management using GOVERN, MAP, MEASURE, MANAGE functions
• ISO/IEC 42001 — AI Management System certification and implementation support
• ISO/IEC 23894 — AI risk management process alignment
• ISO/IEC 27001 — Information security management for all client data
• ISO/IEC 17021 — Conformity assessment body requirements
• FedRAMP — Federal government cloud and AI system authorization support

What Does
Non-Compliance Cost?

The EU AI Act establishes some of the largest potential fines in corporate history. Use this calculator to estimate your organization's potential exposure and the ROI of proactive compliance certification.

Recent Publications

By AxiLayer AI | Independent AI Certification & Auditing | axilayerai.com | April 2026

Most CEOs are not AI experts. They do not need to be.

But in 2026, every CEO leading an organization that develops, deploys, or depends on AI systems needs to be asking the right questions of the people who are. Not because the technical details are the CEO's responsibility, but because the organizational, regulatory, and reputational consequences of getting AI wrong land squarely at the top.

The EU AI Act is in enforcement. NIST AI RMF alignment is increasingly embedded in federal procurement requirements. Boards are asking about AI governance. Insurers are asking about AI risk. Enterprise clients are asking for evidence of independent certification before signing contracts.

The question is no longer whether AI governance matters to your business. It is whether your organization is prepared to demonstrate it.

Here are five questions every CEO should be asking their AI team right now, and what the answers will tell you.

1. “Which of our AI systems would regulators classify as high-risk, and have we treated them that way?”

This is the foundational question, and it is the one most organizations have answered incompletely.

The EU AI Act's Annex III lists specific categories of AI systems that are classified as high-risk and subject to mandatory third-party conformity assessment before deployment. The list includes AI used in credit scoring, hiring and workforce management, healthcare diagnostics, law enforcement, border control, critical infrastructure, and education. If your organization operates in any of these sectors and uses AI to support decisions in these areas, there is a meaningful probability that one or more of your systems meets the high-risk classification threshold.

What you are listening for: a confident, specific answer that maps your actual AI systems to the regulatory criteria, not a general reassurance that “we have reviewed it and we are fine.” If your team cannot tell you precisely which systems are high-risk and what documentation exists to support that classification, you have a gap that requires immediate attention.

What raises a concern: any answer that begins with “we do not think we have any high-risk systems” without being able to explain in detail why each system falls below the threshold.

2. “If a regulator asked us to produce our technical documentation for our most important AI system tomorrow, what would we hand them?”

EU AI Act Annex IV is specific about what technical documentation for a high-risk AI system must contain. It covers the system's general description, its design and development methodology, its training data governance, its risk management records, its accuracy and robustness metrics, its human oversight provisions, and its post-market surveillance plan, among other requirements.

This is not a theoretical question. National competent authorities under the EU AI Act have the power to request technical documentation from organizations deploying high-risk AI systems. Organizations that cannot produce compliant documentation on request face significant enforcement exposure.

What you are listening for: the ability to describe, specifically, what documentation exists, where it is maintained, when it was last updated, and whether it has been reviewed against the Annex IV requirements by someone who knows those requirements in detail.

What raises a concern: documentation that was created at the time of system development and has not been maintained since, or documentation that covers the technical aspects of the system without addressing the regulatory requirements it is supposed to satisfy.

3. “Who, outside our organization, has reviewed our AI systems for compliance?”

This question cuts to the heart of independent assurance, and the answer reveals more about your organization's actual compliance posture than almost anything else.

Internal reviews, vendor assessments, and consultant-led gap analyses are useful. None of them constitute independent third-party certification. The EU AI Act requires third-party conformity assessment for most high-risk systems listed in Annex III, precisely because the regulatory framework recognizes that organizations cannot objectively certify their own compliance.

Think of it the way you think about your financial statements. Your internal finance team produces the numbers. Your external auditor independently verifies them. The credibility of your financial reporting depends on that independence. The same principle applies to AI compliance.

What you are listening for: the name of an independent, third-party certification body that has conducted a formal assessment of your AI systems against a recognized standard, with a formal report and certificate to show for it.

What raises a concern: any answer that describes internal processes, vendor-provided compliance documentation, or consulting engagements where the same firm that helped build your compliance program also assessed it. That is not independence.

4. “What happens to our AI compliance status when the model is retrained or the system is updated?”

AI systems are not static. Models are retrained on new data. Deployment contexts evolve. User interfaces change. New use cases emerge that were not anticipated at the time of the original compliance assessment. Each of these changes has the potential to affect a system's compliance status, and many organizations have no structured process for evaluating those implications.

The EU AI Act's post-market surveillance requirements under Article 72 exist precisely because regulators understand that a point-in-time conformity assessment is insufficient for systems that change over time. The obligation is ongoing, not one-time.

What you are listening for: a described process for evaluating the compliance implications of system changes, including defined thresholds that trigger re-assessment, a functioning post-market surveillance program, and documented records of how changes have been evaluated against the applicable standards since the original certification.

What raises a concern: any answer that treats certification as a completed task rather than an ongoing obligation, or that cannot describe what triggers a re-assessment when the system changes.

5. “If our most important AI system caused harm tomorrow, what is our documented evidence that we did everything required to prevent it?”

This is the hardest question, and it is the most important one.

AI systems make consequential decisions. In healthcare, financial services, law enforcement, and hiring contexts, those decisions affect real people in real ways. When things go wrong, the question regulators, courts, and the public will ask is not whether the organization intended harm. It is whether the organization took every required step to identify and mitigate the risk of harm before it occurred, and whether it can prove it.

The documentation of a defensible AI compliance program is not just a regulatory requirement. It is the evidence base that determines organizational accountability when something goes wrong. Risk registers, audit reports, non-conformity records, human oversight logs, post-market surveillance reports: these are the documents that either demonstrate due diligence or reveal its absence.

What you are listening for: the ability to describe, specifically, what documented evidence exists that the organization identified the risks, implemented the required controls, had those controls independently verified, and maintained them over time.

What raises a concern: any answer that relies on general statements about the organization's values, its commitment to responsible AI, or its internal review processes without being able to point to specific, dated, independent documentation of each of those steps.

What the Answers Tell You

If your AI team can answer all five of these questions specifically, confidently, and with documentation to back each answer up, your organization is in a strong compliance position.

If the answers are vague, incomplete, or reveal that key steps have not been taken, you now know exactly where to focus. The good news is that none of these gaps are irreversible, and identifying them now, through a proactive internal conversation, is substantially better than identifying them through a regulatory inquiry or a procurement loss.

The role of independent third-party certification is to give you and your board the documented, objective assurance that the answers to these questions are not just credible internally, but defensible externally. That is what regulators require, what enterprise procurement teams increasingly demand, and what your stakeholders deserve.

A Note on Where AxiLayer AI Stands

We hold ourselves to the same standards we bring to every client engagement. AxiLayer AI is actively pursuing ISO/IEC 17020 accreditation through ANAB, with expected completion in 2026, reinforcing our capability to perform independent inspection and conformity assessment for AI systems to the highest internationally recognized standard. When we issue a compliance certificate, it is backed by a certification body that has itself been independently verified.

If any of these five questions prompted a conversation you have not had yet, we would be glad to be part of it. Our scoping consultations are complimentary, confidential, and genuinely useful regardless of where your organization is in its compliance journey.

AxiLayer AI is an independent AI certification and auditing body headquartered in Roswell, Georgia. We conduct third-party conformity assessments under the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sector-specific frameworks, with zero vendor affiliations and zero conflicts of interest.

By AxiLayer AI | Independent AI Certification & Auditing | axilayerai.com | March 31, 2026

Most organizations know they need an AI audit. Far fewer know what one actually involves. The term gets used loosely in the industry. Internal reviews, vendor assessments, automated scanning tools, and consultant-led gap analyses are all described as "AI audits" in various contexts. Some of them are useful. None of them are the same as a formal, independent third-party conformity assessment conducted by a certified body against a recognized regulatory standard.

This piece walks through what that process actually looks like, from the first conversation to the certificate on the wall, and what organizations should expect at each stage.

Before the Audit Begins: Scoping

Every engagement at AxiLayer AI begins with a scoping consultation, and scoping is not a formality. It is one of the most consequential steps in the entire process.

During scoping, we work with the organization to answer three foundational questions. First, what AI systems are in scope? Not every AI system an organization operates requires third-party conformity assessment. The EU AI Act applies mandatory assessment requirements to high-risk systems defined in Annex III. NIST AI RMF assessments may cover a broader portfolio. Scoping determines exactly which systems are being assessed and against which frameworks.

Second, what is the organization's current compliance posture? We ask for existing documentation, governance frameworks, prior assessments, and any known gaps before the formal audit begins. Third, what is the certification objective? An organization seeking EU AI Act conformity assessment for a single high-risk system has a different pathway than one pursuing ISO/IEC 42001 certification for its enterprise-wide AI management system.

Scoping typically takes one to two weeks and results in a formal audit plan with defined scope, applicable frameworks, evidence requirements, timeline, and deliverables.

Stage 1: The Documentation Review

The formal audit begins with a Stage 1 documentation review. For an EU AI Act high-risk system, this means reviewing the Annex IV technical documentation package: the general system description, the design and development methodology, the training data documentation, the risk management system records, the accuracy and robustness metrics, the human oversight provisions, and the post-market surveillance plan.

What we are evaluating at Stage 1 is not whether the AI system works correctly. We are evaluating whether the organization has the documented foundation that a compliant AI program requires. Stage 1 produces a formal report that identifies any areas where documentation is missing, incomplete, or non-conformant. Stage 1 typically takes two to four weeks depending on the complexity of the AI system.

Stage 2: The Technical Audit

Stage 2 is where the AI system itself is assessed. This is the most technically intensive phase of the engagement and the one that most distinguishes a real conformity assessment from a documentation exercise.

The Stage 2 audit involves direct evaluation of the AI system against the applicable regulatory requirements, with evidence collected through system walkthroughs, technical interviews, testing, and observation. It covers algorithm evaluation, bias and fairness testing, human oversight verification, cybersecurity and robustness assessment, and post-market surveillance verification.

The Non-Conformities Register

Every audit produces findings, and findings are classified. Major non-conformities are findings that indicate a fundamental failure to meet a requirement of the applicable standard and must be resolved before a certificate can be issued. Minor non-conformities are gaps or weaknesses that do not represent a fundamental failure but indicate a requirement is not fully met. Observations are areas of concern that do not rise to the level of a non-conformity but warrant attention.

Certificate Issuance and the Surveillance Cycle

When all major non-conformities have been resolved and the audit team is satisfied with the evidence, the certification decision is made and the formal compliance certificate is issued. For most engagements, from the initial scoping consultation to certificate issuance, the timeline runs between eight and sixteen weeks.

Certification is not a permanent status. Annual surveillance audits confirm that the system continues to meet the standard it was certified against. Full re-certification typically occurs every three years, or sooner if significant changes to the system or regulatory environment occur.

By AxiLayer AI | Independent AI Certification & Auditing | axilayerai.com | March 26, 2026

After conducting AI compliance assessments across healthcare, financial services, defense, and enterprise technology, a pattern emerges. Organizations are not failing because they ignored AI governance. Most of them tried. They assigned ownership, wrote policies, documented their models, and in many cases engaged consultants to help them build a compliance framework. On paper, their programs look credible.

The gaps are not in the effort. They are in the specifics: the places where what an organization believes it has documented and what an independent auditor can actually verify diverge. Those gaps are where regulatory exposure lives.

1. Risk Classifications That Don't Survive Scrutiny

The EU AI Act's risk framework is deceptively simple on the surface: unacceptable, high, limited, minimal. Most organizations have done some version of a risk classification exercise for their AI systems. Many of them got it wrong.

The classification errors we see fall into two categories. The first is over-classification: organizations treating every AI system as high-risk out of an abundance of caution. The second, and more consequential, is under-classification: AI systems that meet the Annex III criteria for high-risk treatment but have been documented as limited or minimal risk.

2. Technical Documentation That Exists But Isn't Compliant

EU AI Act Annex IV specifies, in considerable detail, what technical documentation for a high-risk AI system must contain. Most organizations deploying high-risk AI systems have technical documentation. Very few have Annex IV-compliant technical documentation.

The problem is not that organizations have no documentation. It is that the documentation was written by engineers who know the system, not by compliance professionals who know the standard. The result is documentation that answers the questions the engineering team thought were being asked, rather than the questions a notified body or national authority will actually ask.

3. Human Oversight That's Designed but Not Deployed

Article 14 of the EU AI Act requires that high-risk AI systems be designed to allow appropriate human oversight. The pattern we see is consistent. An organization designs human oversight into the AI system at the architecture stage, documents it in the technical specification, and then watches it erode during deployment.

Human oversight that exists in documentation but not in practice is not compliant. A straightforward operational walkthrough of how the system is actually used, rather than how it is supposed to be used, reveals the gap immediately.

4. Post-Market Surveillance Plans That Stop at Launch

Post-market surveillance is not a separate phase. It is an ongoing obligation that must be planned and documented before certification and operational at the point of deployment. Organizations that go through the certification process without a credible, operational post-market surveillance plan will find themselves re-certifying within months, or facing enforcement attention when incidents occur without documented response processes.

5. Governance That Lives in Policy, Not Practice

Every organization we assess has an AI governance framework. Most have a written AI policy, many have an AI ethics committee or governance board. They are also, in a significant number of cases, more symbolic than operational.

What to Do With This List

None of these gaps are unusual, and none of them are unfixable. The organizations that navigate AI compliance successfully are not the ones that had perfect programs from the start. They are the ones that identified their gaps through a controlled, proactive assessment rather than through a regulatory inquiry or enforcement action.

We offer complimentary scoping consultations for organizations that want to understand where they stand before a regulator does. No obligation, no sales process: a genuine assessment of your current compliance posture and what it would take to close the gaps.

By AxiLayer AI | axilayerai.com | March 19, 2026

And yet, many organizations still do not have a credible, documented compliance posture.

Why AxiLayer AI Exists

This is exactly why we built AxiLayer AI. We are an independent AI certification and auditing firm. We do not build AI systems. We do not sell AI tools. We have no stake in the systems we assess. Our only job is to provide organizations with objective, third-party assurance that their AI meets the regulatory standards that matter: EU AI Act, NIST AI RMF, ISO/IEC 42001, and ISO/IEC 23894.

Eight service lines. One accountable partner. No conflicts of interest.

Who Needs to Act Now

If your organization is deploying AI in any of the following environments, the EU AI Act's high-risk system obligations apply and independent conformity assessment is required:

• —Healthcare and life sciences, including clinical decision support, patient risk scoring, and medical device AI
• —Financial services, including credit scoring, insurance risk assessment, and anti-money-laundering AI
• —Defense, law enforcement, and border control applications
• —Government and public sector AI systems affecting citizen-facing decisions
• —Critical infrastructure management including energy, water, and transportation
• —Employment, HR, and workforce management AI systems

The timeline for compliance was not extended. The penalties are real. And the organizations that have invested in independent certification are already positioned to demonstrate compliance when regulators ask.

The Next Step Is Simple

We would be glad to start with a conversation. A complimentary scoping consultation to help your organization understand exactly where it stands, what conformity assessment would involve, and what timeline is realistic given your current posture.

By AxiLayer AI | Independent AI Certification & Auditing | axilayerai.com | January 2026

As artificial intelligence becomes embedded in the decisions that shape citizens' lives, government agencies face a challenge that is simultaneously technical, legal, and ethical: how do you ensure that an AI system treating one person differently from another is doing so for defensible, documented, and auditable reasons — and not because of proxy variables that encode protected characteristics?

This question sits at the center of AI fairness in government applications. It is not a question that can be answered with a single metric or a one-time test. It requires a structured, ongoing audit approach that connects algorithmic behavior to regulatory standards and real-world outcomes. This report outlines the emerging standards for algorithmic fairness in government AI and the audit methodologies that public sector agencies need to adopt.

Why Government AI Fairness Is a Distinct Problem

The fairness challenges facing government AI systems differ from those in commercial applications in several important ways. Government AI systems often operate in high-stakes contexts where errors have direct consequences on individuals' access to services, benefits, liberty, and opportunity. They frequently operate on populations with protected characteristics defined by civil rights law. And they are subject to legal obligations — including equal protection requirements, disparate impact standards, and sector-specific mandates — that most commercial AI systems are not.

The Fairness Measurement Challenge

There is no universally accepted single definition of algorithmic fairness. This is not a gap in the science — it reflects a genuine mathematical reality. Several commonly used fairness metrics are mathematically incompatible with each other, meaning that optimizing for one necessarily compromises another. The choice of which fairness metric to apply is therefore not a purely technical decision: it is a policy decision with distributional consequences.

Key Fairness Metrics in Government AI

• —Demographic parity — Does the AI system produce positive outcomes at equal rates across protected groups? A hiring AI satisfies demographic parity if it selects candidates from different racial groups at statistically equivalent rates.
• —Equalized odds — Does the system have equivalent true positive and false positive rates across groups? A recidivism prediction model satisfies equalized odds if it correctly identifies high-risk individuals and incorrectly flags low-risk individuals at equal rates across demographic groups.
• —Calibration — When a model assigns a risk score of 70%, does that score mean the same thing across groups? A well-calibrated model has equivalent predictive accuracy regardless of group membership.
• —Individual fairness — Are similar individuals treated similarly? This requires defining what similarity means in the context of a specific decision, which is itself a substantive policy judgment.
• —Counterfactual fairness — Would the outcome for an individual have been different if they belonged to a different protected group, holding other factors constant?

Agencies deploying AI in high-stakes contexts must make explicit choices about which fairness criteria apply to their specific use case, document the rationale for those choices, and accept accountability for the distributional consequences of those choices.

Emerging Standards: EU AI Act and NIST AI RMF

EU AI Act Requirements for High-Risk Government AI

For government agencies operating within EU jurisdiction or deploying AI systems that affect EU residents, the EU AI Act establishes binding fairness-related requirements for high-risk AI systems under Articles 9 through 15. Specifically, Article 10 requires that training, validation, and testing data for high-risk AI systems be examined for possible biases that could lead to discrimination. Article 14 mandates human oversight sufficient to detect and correct bias-related failures. Article 15 requires that high-risk AI systems achieve appropriate levels of accuracy and robustness across relevant population segments.

NIST AI RMF Guidance

The NIST AI Risk Management Framework's MEASURE function provides the most operationally detailed U.S. federal guidance on AI fairness evaluation. MEASURE 1.1 through 1.3 address the identification and documentation of AI risks, including bias risks. MEASURE 2.5 specifically requires that bias testing be conducted across relevant subpopulations, with results documented and incorporated into risk management decisions. GOVERN 1.1 requires that organizational AI risk tolerance explicitly address fairness and equity considerations.

The Audit Methodology: What a Government AI Fairness Audit Covers

An independent fairness audit of a government AI system is not a documentation review. It is a technical assessment of actual system behavior, conducted by auditors who combine AI/ML expertise with regulatory knowledge of applicable fairness standards. The following components constitute a comprehensive fairness audit.

1. Data Provenance and Representation Analysis

Fairness audits begin with the training data. Auditors examine the composition of training datasets for representation gaps — whether certain demographic groups are underrepresented in ways that affect model performance for those groups — and for historical bias encoded in labels, particularly in applications where the label itself reflects past discriminatory outcomes (criminal justice, employment, lending).

2. Proxy Variable Analysis

Many AI systems that do not directly use protected characteristics as inputs still produce disparate outcomes because they use variables that are statistically correlated with protected characteristics — zip code as a proxy for race, occupational history as a proxy for gender. Proxy analysis identifies these relationships and assesses their impact on system outputs.

3. Disparate Impact Testing

Auditors run statistical analyses comparing outcome rates across protected groups and subgroups. For hiring AI, this means comparing selection rates. For benefits eligibility AI, this means comparing approval and denial rates. For risk assessment tools, this means comparing score distributions and decision thresholds. Results are evaluated against applicable legal standards, including the 4/5ths rule used in employment discrimination analysis.

4. Performance Disaggregation

Overall model accuracy metrics can mask significant performance disparities across subgroups. A model that achieves 92% accuracy overall may achieve only 78% accuracy for a specific demographic group. Auditors disaggregate all performance metrics — accuracy, precision, recall, F1 — across relevant protected characteristics and intersectional subgroups.

5. Operational Audit: Human Oversight Verification

In government AI systems, the human oversight provisions that exist in system documentation frequently do not survive contact with operational reality. Auditors conduct operational walkthroughs to verify that case workers, benefits administrators, and other operators actually understand the AI system's limitations, can meaningfully interpret its outputs, and have functioning pathways to override or escalate AI-generated recommendations.

Corrective Action and Ongoing Monitoring

A fairness audit that identifies disparate impact is not the end of the engagement — it is the beginning of the remediation process. Depending on the source and magnitude of the disparity, corrective actions may include retraining the model on rebalanced data, adjusting decision thresholds independently for different groups, implementing pre- or post-processing fairness interventions, revising the features used in the model, or, in cases of fundamental fairness failure, discontinuing the system pending redesign.

Critically, government agencies must implement ongoing monitoring programs that detect fairness degradation after deployment. Models trained on historical data will encounter distributional shift as the populations they serve change over time. A system that meets fairness standards at deployment may develop disparate impact within months if monitoring is not in place.

Recommendations for Public Sector AI Governance Teams

• —Establish fairness criteria before model development, not after. The choice of which fairness metric applies to a given application is a policy decision that should involve legal counsel, civil rights expertise, and stakeholder engagement.
• —Require disaggregated performance reporting as a procurement standard. Any AI system procured for government use should be required to provide performance metrics disaggregated by protected characteristics as a condition of contract.
• —Commission independent third-party fairness audits before deployment and on a regular surveillance cycle thereafter. Internal assessments are necessary but not sufficient — they are subject to the same organizational pressures that produce compliance gaps in other areas.
• —Document fairness decisions explicitly. The choice of fairness metric, the threshold for acceptable disparate impact, and the rationale for deployment despite identified gaps must all be documented in a form that survives personnel turnover and regulatory scrutiny.
• —Build human oversight that works in practice, not just on paper. Invest in training for operators who interact with AI-generated outputs, and build feedback mechanisms that allow frontline workers to flag suspected fairness failures.

EU AI Act High-Risk Systems: Compliance Readiness for August 2026

A technical deep-dive into the conformity assessment requirements, technical documentation standards, and registration obligations that take effect in August 2026.

NIST AI RMF Implementation Workshop: Govern, Map, Measure, Manage

A practical workshop translating the NIST AI Risk Management Framework into operational AI governance programs for enterprise practitioners.

AI Compliance for Government Agencies: Federal Requirements and Certification Pathways

A focused session for federal, state, and local government agencies on OMB AI guidance, FedRAMP AI obligations, and third-party certification requirements.

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Our leadership team speaks on AI regulation, certification methodology, and governance frameworks at industry conferences, corporate events, and government briefings worldwide.

Senior AI Strategy &
Architecture Guidance

AxiLayer AI's CTO Advisory practice provides board-level and executive-level technology leadership for organizations navigating AI transformation, governance, and compliance simultaneously. Led by Founding Partner and CEO Ovi Pinzaru — a technology executive with 20+ years of enterprise AI, infrastructure, and governance experience at IBM, Hewlett Packard Enterprise, and FDaaS Group — this service delivers the strategic clarity that complex AI programs require.

We advise C-suite executives, boards of directors, and technology leadership teams on AI strategy development, AI architecture governance, MLOps and LLMOps program design, and the integration of compliance requirements into AI development lifecycles.

Build the Future of Trusted AI Together

As AI regulation moves from voluntary guidance to mandatory enforcement, organizations across every sector need a trusted, independent certification partner. AxiLayer AI's Alliance Ecosystem brings together the firms, institutions, and innovators best positioned to serve that need — collectively and at scale.

We do not partner with AI system vendors, AI platform providers, or organizations with commercial interests in the AI systems we certify. Our independence is non-negotiable — and our alliance partners understand and respect that principle.

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For universities, research institutions, think tanks, and standards bodies.

Become an Alliance Ecosystem Partner

We review all alliance applications carefully. If your organization is a strong fit, our team will reach out within five business days.

Role Overview

AxiLayer AI is seeking a high-performance Global Revenue Lead to own and drive the company's full revenue pipeline across all geographies and market segments. This role is accountable for building and closing new business with U.S. federal and government agencies, Fortune 500 enterprises, and international clients across the European Union, Middle East, and Asia-Pacific markets. The Global Revenue Lead will serve as AxiLayer AI's primary commercial driver, combining strategic pipeline development with a relentless focus on revenue execution. As organizations worldwide face mounting obligations under the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sector-specific AI governance mandates, the demand for independent third-party AI certification is accelerating rapidly.

Key Responsibilities

• —Develop and execute a comprehensive global revenue strategy spanning U.S. federal agencies, commercial enterprise, and international markets including the EU, Middle East, and Asia-Pacific
• —Own the full sales cycle from prospecting and pipeline qualification through proposal development, negotiation, and contract execution across all segments and geographies
• —Build and maintain an accurately forecasted pipeline targeting contracts of $500,000 and above as the primary deal profile using CRM systems
• —Lead federal and government business development targeting DoD, DHS, HHS, GSA, NIST, and other agencies; identify opportunities through SAM.gov, GovWin, and agency procurement forecasts
• —Drive international revenue development across EU-regulated markets, Gulf Cooperation Council (GCC) governments, and APAC enterprise clients navigating AI regulatory obligations
• —Cultivate executive-level relationships with Chief Compliance Officers, Chief AI Officers, General Counsels, CIOs, and procurement executives
• —Develop and pursue strategic teaming partnerships with large prime contractors, systems integrators, Big 4 consulting firms, and law firms to expand deal flow
• —Pursue GSA Schedule registration, government contract vehicle setup, and relevant small business set-aside designations
• —Track and report on global pipeline activity, win rates, revenue projections, and market intelligence to the CEO and CFO

Required Qualifications

• —Bachelor's degree in Business, Finance, Public Policy, Computer Science, or related field; advanced degree or MBA preferred
• —7+ years of demonstrated success in enterprise sales, business development, or revenue leadership with a track record of closing high-value contracts ($500K+) across multiple geographies
• —Proven ability to manage long, multi-stakeholder sales cycles in professional services, compliance technology, consulting, or regulatory advisory environments
• —Experience selling to U.S. federal government clients including knowledge of FAR/DFARS, contract vehicles (GWAC, IDIQ, BPA), and federal procurement processes
• —Demonstrated success developing international commercial relationships in EU, GCC, or APAC markets
• —Strong understanding of the global AI regulatory landscape including the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sector-specific AI governance requirements

Role Overview

AxiLayer AI is seeking a visionary Chief Technology Officer to serve as the company's senior technical authority and architect of its audit technology platform. The CTO will be responsible for building and leading AxiLayer AI's technical infrastructure, defining the tooling strategy that underpins its AI audit and certification services, and ensuring that the company's methodologies reflect the highest standards in AI systems evaluation. This role is foundational to AxiLayer AI's credibility with enterprise clients, government agencies, and investors. A fractional or advisory engagement structure is available for the right candidate during an initial phase, with a clear path to full-time as the company scales.

Key Responsibilities

• —Define and execute AxiLayer AI's technology strategy, including development of a proprietary AI audit and risk assessment platform supporting audit workflow, evidence management, and reporting automation
• —Oversee architecture and implementation of internal tools for AI model evaluation, bias testing, explainability analysis, and regulatory conformity scoring
• —Establish technical credibility with enterprise and government clients by contributing to audit methodology design, technical documentation standards, and AI system evaluation protocols
• —Provide technical leadership on client audit engagements requiring deep AI/ML systems expertise, including review of model architectures, training pipelines, and governance controls
• —Lead the company's technical response to NIST AI RMF, EU AI Act Annex IV documentation requirements, and ISO/IEC 42001 AI management system standards
• —Support government contracting and SBIR/STTR grant applications by serving as Principal Investigator or technical authority on R&D proposals
• —Build and manage a technical team including AI engineers, data scientists, and audit tooling developers as the company grows
• —Represent AxiLayer AI at technical conferences, government forums, and industry working groups to build brand authority and identify business opportunities

Required Qualifications

• —Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, or related technical discipline; advanced degree (M.S. or Ph.D.) strongly preferred
• —10+ years of experience in AI/ML engineering, technical leadership, or research, with at least 3 years as CTO, VP of Engineering, or equivalent
• —Deep expertise in machine learning systems including model development, training infrastructure, evaluation methodologies, bias/fairness testing, and explainability frameworks (LIME, SHAP)
• —Demonstrated experience with AI governance, responsible AI principles, or AI risk management frameworks at an architectural or organizational level

Role Overview

AxiLayer AI is seeking a driven and strategically connected Director of Capital Development and Investor Relations to lead the company's efforts to secure the capital necessary to accelerate growth. This role is responsible for pursuing all non-dilutive and dilutive funding pathways: government grants (SBIR, STTR, NIST, NSF, DoD), angel and seed investment, strategic venture capital, corporate strategic investors, and public-private partnership funding. This role is structured as a commission-based engagement initially, with a clear transition to full-time employment as the company achieves target funding milestones.

Key Responsibilities

• —Develop and execute a comprehensive capital development strategy covering government grants, angel/seed investment, strategic VC, corporate strategic investment, and public-private partnership funding
• —Lead identification, application, and management of federal grant opportunities including SBIR/STTR (DoD, NSF, NIST, NIH, DOE) and other AI-focused government funding programs
• —Coordinate registration and compliance for SAM.gov, Grants.gov, UEI/DUNS, and agency-specific portals
• —Build and maintain a targeted investor pipeline including angel investors, seed-stage VCs, RegTech and GovTech-focused funds, and family offices
• —Develop and continuously refine investor pitch materials including decks, executive summaries, financial models, and data room documentation
• —Identify and pursue WOSB, 8(a), and other small business set-aside designations and funding programs for which AxiLayer AI may qualify
• —Represent AxiLayer AI at investor forums, pitch competitions, accelerator programs, and innovation funding events

Required Qualifications

• —Bachelor's degree in Finance, Business Administration, Public Policy, or related field; advanced degree or MBA preferred
• —5+ years in fundraising, capital development, grant writing, investment banking, or VC with demonstrated success securing funding for technology or professional services companies
• —Proven track record closing investment rounds, securing government grants, or executing strategic partnership agreements
• —Familiarity with the SBIR/STTR ecosystem, federal grant application processes, and government innovation funding programs

Role Overview

AxiLayer AI is seeking an experienced Federal and Government Business Development Lead to build and manage the company's pipeline of government contracts, agency relationships, and public sector compliance engagements. As federal agencies accelerate AI adoption under OMB M-25-21, M-25-22, and Executive Order-driven AI governance mandates, the demand for independent AI auditing and conformity assessment services is rapidly expanding across the DoD, civilian agencies, and federally regulated industries. This role transitions to full-time employment as government contract revenue reaches defined targets.

Key Responsibilities

• —Develop and execute a federal business development strategy targeting DoD, DHS, HHS, GSA, NIST, OMB, and other civilian and defense agencies with active AI governance and compliance needs
• —Identify and monitor federal contract opportunities through SAM.gov, GovWin, BGOV, and agency procurement forecasts; qualify opportunities and build a robust, accurately forecasted pipeline
• —Lead capture management activities including opportunity qualification, competitive analysis, teaming strategy, and bid/no-bid decision-making
• —Develop and submit responses to Sources Sought notices, Requests for Information (RFIs), Requests for Proposals (RFPs), and Requests for Quotations (RFQs)
• —Pursue GSA Schedule registration and contract vehicle setup to position AxiLayer AI for efficient federal procurement
• —Identify and develop teaming partnerships with large prime contractors, systems integrators, and GovCon firms
• —Represent AxiLayer AI at government contractor forums, agency industry days, and federal technology conferences

Required Qualifications

• —Bachelor's degree in Business, Public Administration, Political Science, Computer Science, or related field
• —5+ years of demonstrated success in federal government business development, capture management, or government contracting with a track record of winning prime or subcontract awards
• —Strong working knowledge of the federal acquisition process including FAR/DFARS, contract vehicles (GWAC, BPA, IDIQ), and procurement timelines
• —Experience with SAM.gov registration, capability statement development, and federal procurement database tools (GovWin, USASpending, BGOV, or equivalent)

Role Overview

AxiLayer AI is seeking an experienced Strategic Partnerships Manager to build and manage a high-value ecosystem of channel partners, referral relationships, and strategic alliances that drive client acquisition, expand market reach, and accelerate revenue growth. The ideal candidate will identify, cultivate, and activate partnerships with law firms, Big 4 and mid-market consulting firms, systems integrators, technology vendors, industry associations, and other organizations whose clients face AI compliance obligations. This role is central to AxiLayer AI's go-to-market strategy and transitions to full-time as partnership-generated revenue reaches defined thresholds.

Key Responsibilities

• —Develop and execute a strategic partnership and channel development plan targeting law firms, Big 4 and mid-market consulting firms, systems integrators, AI platform vendors, and industry trade associations
• —Identify, qualify, and initiate relationships with potential channel and referral partners whose client base faces AI compliance obligations under EU AI Act, NIST AI RMF, and ISO/IEC 42001
• —Negotiate and execute formal partnership, referral fee, co-selling, and revenue-sharing agreements in coordination with the CEO and legal counsel
• —Build and manage an active partner portal experience enabling partners to track referrals, access co-marketing materials, and monitor commission activity
• —Coordinate joint marketing activities with partners including webinars, conference sponsorships, co-authored white papers, and joint client presentations
• —Pursue relationships with AI platform companies as potential channel or co-certification partners
• —Track and report on partnership pipeline activity, referral conversion rates, and partner-generated revenue using CRM tools

Required Qualifications

• —Bachelor's degree in Business, Marketing, Finance, or related field
• —5+ years in strategic partnership development, channel sales, or business development in professional services, compliance technology, legal services, or consulting
• —Demonstrated track record building productive referral and channel partner relationships that generate measurable revenue
• —Strong understanding of the AI regulatory landscape and the compliance challenges facing enterprise clients in regulated industries

Role Overview

AxiLayer AI is seeking a highly skilled AI Auditor and Compliance Analyst to conduct rigorous, evidence-based audits of artificial intelligence and machine learning systems for enterprise and government clients. You will assess conformity against the EU AI Act, NIST AI RMF, ISO/IEC 42001, and related standards — producing detailed audit findings, gap analyses, and compliance attestations that clients rely on to meet regulatory obligations and build stakeholder trust.

Key Responsibilities

• —Plan, scope, and execute comprehensive AI system audits across financial services, healthcare, government, and regulated sectors
• —Evaluate AI models for bias, fairness, explainability, robustness, and data governance against applicable regulatory frameworks
• —Review algorithmic decision-making systems for EU AI Act high-risk requirements including risk management, technical documentation, and human oversight
• —Apply NIST AI RMF Govern-Map-Measure-Manage functions to assess organizational AI risk posture and maturity
• —Produce detailed audit reports including findings, evidence references, risk ratings, and prioritized remediation roadmaps
• —Issue formal compliance attestation letters and certificates upon successful audit completion

Required Qualifications

• —Bachelor's degree or higher in Computer Science, Data Science, Information Systems, Statistics, Engineering, or related technical field
• —3+ years of experience in AI/ML engineering, data science, or technical compliance/audit roles
• —Demonstrated understanding of ML fundamentals: model training, evaluation metrics, bias detection, and explainability (LIME, SHAP)
• —Familiarity with EU AI Act, NIST AI RMF, ISO/IEC 42001, and/or ISO/IEC 23894 frameworks
• —Strong analytical, writing, and communication skills with ability to produce executive-level reports

Role Overview

AxiLayer AI is seeking an experienced Regulatory Consulting Lead to guide Fortune 500 enterprises, government agencies, and emerging technology companies through the complex landscape of AI regulation. You will serve as a subject matter expert and trusted advisor — helping clients interpret regulatory obligations, design compliant AI governance frameworks, and build lasting organizational capacity for responsible AI.

Key Responsibilities

• —Lead regulatory consulting engagements from scoping through delivery, serving as primary client relationship owner
• —Conduct AI compliance gap assessments against EU AI Act, NIST AI RMF, ISO/IEC 42001, and sector-specific requirements
• —Develop comprehensive AI governance framework designs tailored to client risk profiles and regulatory obligations
• —Advise clients on EU AI Act high-risk classification, conformity assessment pathways, and CE marking obligations
• —Produce high-quality deliverables: regulatory analyses, gap assessment reports, implementation roadmaps, and executive briefings
• —Support business development by contributing to proposals, thought leadership, and client presentations

Required Qualifications

• —Bachelor's degree in Law, Public Policy, Computer Science, or related field; J.D. or advanced degree strongly preferred
• —5+ years in AI/technology regulatory compliance, technology law, policy consulting, or a directly related field
• —Demonstrated expertise in at least two of: EU AI Act, NIST AI RMF, ISO/IEC 42001, GDPR, CCPA, or sector-specific AI regulations
• —Strong executive-level communication — ability to brief C-suite, legal counsel, and board-level audiences
• —Proven experience managing complex consulting engagements with multiple stakeholders

Role Overview

AxiLayer AI is seeking a strategic Business Development Manager to drive new client acquisition, expand existing relationships, and build the revenue pipeline across Fortune 500 enterprises, government agencies, and regulatory bodies. This is a high-impact role with direct influence over AxiLayer AI's growth trajectory in a rapidly expanding market. Compensation includes a competitive base salary plus uncapped commission and annual bonus.

Key Responsibilities

• —Develop and execute a strategic business development plan targeting government agencies, Fortune 500 enterprises, and financial institutions
• —Lead the full sales cycle from prospecting through proposal development, negotiation, and contract execution
• —Build relationships with Chief Compliance Officers, Chief AI Officers, General Counsels, CIOs, and procurement executives
• —Represent AxiLayer AI at industry conferences, regulatory forums, and trade events nationally
• —Develop strategic partnerships with law firms, systems integrators, and consulting firms for referral generation
• —Maintain accurate pipeline reporting and forecast submissions to executive leadership

Required Qualifications

• —Bachelor's degree in Business, Finance, Public Policy, Computer Science, or related field
• —5+ years of successful B2B sales or business development in professional services, compliance technology, legal services, or consulting
• —Demonstrated track record closing complex, multi-stakeholder enterprise or government deals with extended sales cycles
• —Exceptional presentation and negotiation skills comfortable at C-suite and board level
• —Willingness to travel nationally up to 40% of the time

Role Overview

AxiLayer AI is seeking a highly organized Operations and Administrative Manager to serve as the operational backbone of the company. You will oversee day-to-day firm operations — coordinating internal processes, supporting executive leadership, managing client engagement logistics, maintaining corporate records, and ensuring AxiLayer AI's people, systems, and processes operate with the precision expected of a premier certification body.

Key Responsibilities

• —Manage daily office operations including facilities, vendor relationships, and administrative systems at Roswell HQ
• —Maintain corporate records, policy documentation, and compliance files per Delaware corporate governance requirements
• —Coordinate client engagement logistics: contract tracking, SOW administration, onboarding documentation, and invoicing
• —Manage HR administrative processes: new hire onboarding, benefits enrollment, personnel files, and policy acknowledgments
• —Support CEO and CFO with scheduling, travel coordination, meeting preparation, and executive correspondence
• —Assist in the preparation of board materials, regulatory filings, and corporate governance documentation

Required Qualifications

• —Bachelor's degree in Business Administration, Operations Management, Public Administration, or related field
• —4+ years of experience in operations management, executive administration, or office management in a professional services environment
• —Strong proficiency with Microsoft Office Suite, Google Workspace, and project management tools
• —Exceptional attention to detail with high standard of professional presentation and written communication
• —Demonstrated ability to manage multiple priorities with precision and discretion in a fast-paced environment