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Kora AI Governance

Kora AI Governance is a comprehensive ML model lifecycle management platform that provides model registry, version control, monitoring, drift detection, fairness evaluation, explainability reporting, and A/B experimentation through a unified API. Built for financial institutions, fintechs, and any organization deploying ML models that require auditability, compliance, and responsible AI practices.

What you can do​

  • Model Registry — Register, version, and catalog all ML models across your organization. Track model metadata, ownership, approval status, and deployment history in a central repository.
  • Version Management — Manage model versions with full lineage tracking. Promote versions through staging environments (Development, Staging, Production) with approval workflows.
  • Model Monitoring — Monitor deployed models in real time with configurable metric collection. Track prediction latency, throughput, error rates, and custom business metrics.
  • Drift Detection — Detect data drift, concept drift, and prediction drift using statistical tests (KS, PSI, Chi-Square, Jensen-Shannon). Configure thresholds and receive alerts when drift exceeds acceptable limits.
  • Fairness Evaluation — Evaluate model fairness across protected attributes (gender, ethnicity, age, geography). Compute disparate impact, equalized odds, demographic parity, and other fairness metrics.
  • Explainability — Generate model explanations using SHAP values, LIME, feature importance, and partial dependence plots. Produce human-readable explanation reports for regulators and auditors.
  • A/B Experiments — Run controlled experiments comparing model versions with configurable traffic splits, statistical significance targets, and guardrail metrics.

How it works​

Kora AI Governance integrates with your ML infrastructure to provide end-to-end model lifecycle management.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Your ML    │────▶│  AI Gov     │────▶│  Your Server │
│  Pipeline    │     │  API        │     │  (webhook)   │
└──────────────┘     └──────┬──────┘     └──────────────┘
                            │
              ┌─────────────┼─────────────┐
              │             │             │
        ┌─────▼─────┐ ┌────▼────┐ ┌──────▼──────┐
        │  Model    │ │ Drift   │ │  Fairness   │
        │  Registry │ │ Monitor │ │  Evaluator  │
        └────────────┘ └─────────┘ └─────────────┘
  1. Register models with metadata, ownership, and classification (risk tier, use case, data sensitivity)
  2. Push model versions with artifacts, training metadata, and performance benchmarks
  3. Monitor predictions by sending inference logs for real-time metric tracking
  4. Detect drift with automated statistical tests on feature distributions and prediction outputs
  5. Evaluate fairness by running bias audits across protected demographic attributes

Who it's for​

  • ML engineering teams needing a central registry and deployment tracking for models
  • Risk and compliance teams requiring auditable model governance with approval workflows
  • Data science teams running experiments and tracking model performance over time
  • Internal audit teams needing explainability reports and fairness assessments
  • Regulators seeking transparency into AI/ML model usage in financial services

Next steps​