Govern AI with confidence. Make complex decisions clear.

Shiny Glint AI Governance helps leadership teams operationalize responsible, data‑driven AI—aligning innovation with control, transparency, and measurable business value.

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Trusted by teams who value accountability

Aligned to NIST AI RMF, ISO/IEC 27001, and GDPR
Executive dashboards for risk, controls, and model performance
Human‑in‑the‑loop decision transparency

Key outcomes

12 weeks to a working AI governance framework
40% faster model review and approval cycles
100% audit‑ready traceability across data, models, and decisions

Why AI Governance matters now

AI is powering decisions that affect customers, revenue, and reputation. Without a clear operating model, organizations face fragmented controls, slow approvals, and compliance risk. We establish a scalable framework that protects value while accelerating delivery.

Model risk management integrated with your SDLC and MLOps
Bias, fairness, and explainability controls built into design
Policy-to-control mapping for every regulation you care about
Continuous monitoring and drift response playbooks
Clear decision rights for data, models, and deployment

Executive-ready solutions

Decision Intelligence Governance: link business outcomes to model controls
Model Risk Management: tiering, validation, and challenger frameworks
Responsible AI by Design: fairness, transparency, and safety guardrails
Data Governance for AI: lineage, quality SLAs, and retention policies
MLOps Governance: gates, monitoring, and rollback procedures

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What we do

Governance Assessments: baseline maturity and prioritized roadmap
Policy & Control Frameworks: tailored to your risk appetite and sector
Operating Model & Roles: clear decision rights and RACI
Model Validation & Testing: documentation, stress tests, and sign‑offs
Monitoring & Drift Response: thresholds, alerts, and playbooks
Training & Change: practical enablement for teams and leadership

See Services

From policy to operational control

Discover: risks, use cases, and regulatory obligations
Design: governance framework, control library, and metrics
Build: workflows, documentation, and dashboards
Pilot: validate with a real use case and calibrate controls
Scale: embed in delivery, measure ROI, and audit readiness

Pillars: Accountability, Transparency, Safety, Fairness, Privacy
Roles: Model Owners, Risk Officers, Data Stewards, Approvers
Decision Rights Matrix: who proposes, reviews, approves, and deploys

Business results

Reduce regulatory exposure while accelerating time‑to‑value
Standardize documentation to speed audits and partner reviews
Improve model performance with continuous oversight
Earn stakeholder trust with explainable decisions

What clients say

“Within three months we moved from ad‑hoc approvals to a clear, auditable process. Product launches sped up and our risk team finally had visibility.”

Sarah Morgan, Chief Risk Officer — Melbourne, AU

“Their control library mapped directly to our banking obligations. Audit findings dropped to zero in the next review.”

Daniel Cho, Head of Model Risk — Singapore

“We gained executive dashboards and action playbooks for drift. It’s governance that actually helps the business move faster.”

Anika Rao, VP Data Science — Sydney, AU