Trust Infrastructure
for Artificial Intelligence
Independent, measurable audits for AI governance, content claims, and model behavior. Building the truth layer that organizations need to deploy AI with confidence.
The AI Trust Crisis
Organizations face mounting challenges as AI adoption accelerates without adequate verification infrastructure.
Unverified AI Claims
AI-generated content proliferates without verification, creating systemic risks for organizations relying on automated outputs.
Governance Gaps
Most AI systems operate without documented policies, human oversight protocols, or clear accountability frameworks.
Regulatory Pressure
The EU AI Act and emerging global standards demand transparent, auditable AI systems with documented compliance.
Reputational Risk
AI failures, biased outputs, and misleading claims expose organizations to legal liability and brand damage.
Three Pillars of AI Assurance
Comprehensive audit services designed to establish measurable, repeatable trust across every dimension of AI deployment.
Corporate Governance Audit
Comprehensive assessment of internal AI policies, human-in-the-loop verification systems, data privacy compliance, and board-level reporting frameworks.
Content & Claims Audit
Independent verification of marketing claims, ESG reports, financial prospectuses, and product performance statements against documented evidence.
AI Model & Agent Audit
Technical assessment of chatbot behavior, bias detection, prompt injection vulnerabilities, output consistency, and alignment with stated capabilities.
The Sincerity Scoring Model
A rigorous, quantitative framework for measuring the truthfulness and integrity of AI-generated content and claims.
Claim-Level Scoring Formula
Si = clip[0,100](wE×Ei + wA×Ai + wU×Ui + wT×Ti - wM×Mi - wH×Hi)Document-level score: Weighted average of claim scores by prominence, impact, and regulatory sensitivity.
The Six Pillars
Evidence Strength
Source quality, relevance, freshness, and methodology rigor.
Attribution & Traceability
Citation presence, primary source verification, pinpoint traceability.
Uncertainty Alignment
Language certainty matches evidence strength appropriately.
AI/Method Transparency
AI disclosure, limitations stated, methodology described.
Manipulation Penalty
Overstatement detection, cherry-picking, scope creep.
Harm & Bias Risk
Discrimination detection, high-stakes guidance safeguards.
Aligned with EU AI Act Principles
Who We Serve
Organizations that understand AI trust isn't optional—it's infrastructure.
Public Corporations
Fortune 500 companies seeking to demonstrate AI governance compliance and protect shareholder value.
Government Agencies
Federal, state, and municipal bodies requiring transparent AI deployment and citizen-facing accountability.
Investment Firms
Asset managers and PE firms evaluating AI risk exposure in portfolio companies and due diligence processes.
Insurance Companies
Underwriters assessing AI-related risks and developing coverage frameworks for algorithmic liability.
Advertising Standards Bodies
Regulatory organizations validating AI-generated marketing claims and consumer protection compliance.
Our Position
Sincere Intelligence is not a tool—it's an institution. We sell trust infrastructure through measurable, repeatable audits.
SOC 2 for AI Content
The same rigor applied to data security, now applied to AI-generated content and claims.
Trust Infrastructure
Building the verification layer that responsible AI deployment requires.
The Truth Layer for AI
Independent, quantitative validation that bridges the gap between AI capability and organizational accountability.
Ready to Build Your AI Trust Infrastructure?
Take the first step toward demonstrable AI governance. Our team will assess your needs and recommend the appropriate audit scope.