AI That Recommends,
Never Decides
AXIOMIA is built on a fundamental principle: AI augments human judgment—it never replaces it. Every recommendation requires human approval. Every decision is traceable. Every output is auditable.
What We Do
- Recommend actions based on policy-aligned analysis
- Provide confidence scores and uncertainty bounds
- Generate exportable evidence for every recommendation
- Surface bias risks before they become decisions
- Maintain complete decision trace lineage
- Require human approval for all consequential actions
What We Don't Do
- Auto-write pay changes without human approval
- Execute workforce actions silently
- Train models on customer data without consent
- Hide model logic behind black boxes
- Make decisions that bypass policy gates
- Share data across tenant boundaries
7 Pillars of Responsible AI
Our framework ensures every AI capability meets enterprise governance standards.
Human Oversight
Every AXIOMIA recommendation requires human approval before action. No silent writes, no auto-execute.
- •Mandatory approval workflows for all pay and workforce changes
- •Clear escalation paths with role-based permissions
- •Audit trail of who approved what, when, and why
Transparency & Explainability
Every AI output includes a full explanation of inputs, logic, and confidence levels.
- •Decision traces showing exactly how recommendations were generated
- •Confidence scores with uncertainty bounds
- •Plain-language explanations for non-technical reviewers
Fairness & Bias Mitigation
Continuous monitoring for disparate impact across protected categories.
- •Bias testing before any model deployment
- •Ongoing fairness audits with documented results
- •Automatic alerts when statistical thresholds are breached
Privacy & Security
Enterprise-grade data protection with tenant isolation and encryption.
- •Data encrypted at rest (AES-256) and in transit (TLS 1.3)
- •Strict tenant isolation—no cross-customer data access
- •SOC2-aligned access controls and logging
Auditability & Evidence
Complete, immutable records of every AI-assisted decision.
- •Evidence manifests with cryptographic hashes
- •Proof packs exportable for auditors and regulators
- •Version-controlled policy definitions
Governance & Lifecycle
Structured model risk management from development through retirement.
- •Model inventory with risk classifications
- •Change control for model updates
- •Retirement protocols with stakeholder sign-off
Continuous Monitoring
Real-time performance and drift detection for all deployed models.
- •Automated accuracy and fairness dashboards
- •Drift alerts when data distributions shift
- •Quarterly model reviews documented and archived
Model Risk Management
Model Inventory & Classification
Every model in AXIOMIA is cataloged with risk tier, use case, and owner. High-risk models (pay recommendations, workforce decisions) undergo enhanced review.
Validation & Testing
Models are tested against holdout datasets, bias benchmarks, and edge cases before deployment. Results are documented and archived.
Training Data Practices
AXIOMIA does not train on customer data without explicit consent. Training datasets are documented with provenance and sampling methodology.
Third-Party Models
Any third-party AI components (e.g., LLMs for summarization) are evaluated for compliance with our responsible AI standards before integration.
Data Provenance & Evidence Manifests
Every AXIOMIA output includes a complete evidence manifest documenting:
Input Sources
Which data feeds contributed to the analysis, with timestamps and version IDs.
Processing Steps
The transformation and inference steps applied, with model versions.
Cryptographic Hashes
SHA-256 hashes of inputs and outputs for tamper-evidence and reproducibility.
Questions About Our AI Practices?
Contact our compliance team for detailed documentation or to discuss your governance requirements.