We build the policies, approval workflows, monitoring, access controls, audit trails, and safe-rollout plans that let an enterprise actually use AI. Then we operate it. Ongoing prompt updates, model selection, access reviews, and incident response. Named engineer. Monthly retainer. SLA-backed.
AI governance is the set of policies, controls, and audit mechanisms that let an enterprise use AI safely. Managed AI operations is the ongoing work, like monitoring, prompt updates, model selection, access reviews, and incident response, that keeps AI systems working in production. Most organizations need both. We build the governance once, then operate it.
AI governance is the policies, controls, and audit mechanisms that let an enterprise use AI safely; managed AI operations is the ongoing work of monitoring, prompt updates, model selection, access reviews, and incident response that keeps it running in production. Thinklytics builds the governance once, then operates it with a named engineer on a monthly, SLA-backed retainer.
A documented framework for what AI can do in your organization, who approves what, what's logged, and how you respond when something goes wrong.
A managed-operations practice that runs the framework day-to-day after rollout.
Built inside your existing security and compliance program, not a parallel structure.
Mapped to NIST AI RMF, ISO 42001, or EU AI Act if you need it. Frameworks follow the controls, not the other way around.
A compliance binder. We do not ship policy documents that nobody reads.
A blocker. Governance is built so the work scales, not so it stops.
A vendor lock-in. You own the framework, the prompts, the runbooks, and the audit logs.
Policy framework. What AI can and cannot do, what data it can read, what requires human approval, and vendor and model selection criteria.
Approval workflows with documented escalation paths, timeouts, and fallbacks.
Sample-based output review on cadence, drift detection, and quality regression alerts.
Safe rollout plans: pilot to limited production to general production with documented gates and tested rollback.
AI governance framework for a software company managing AI exposure across an enterprise customer base.
Data and AI governance framework for a fast-growing SaaS organization with regulated customer data.
Governance framework for a federal agency engagement. Public-sector access controls and audit requirements.
There's no named owner per workflow. There's no approval framework.
There's no audit log capturing what was retrieved, what was sent, and who approved.
There's no drift detection or sample-based review on a defined cadence.
A vendor's AI tool is a black box with full access to customer data.
There's no model or vendor governance review, and no access scoping.
What is the difference between AI governance and AI readiness?
AI readiness asks: can your data and process support AI? AI governance asks: can your organization run AI safely once it's live? Both matter. Different work, different artifacts.
We can. The policies need to be ratified by your leadership, security, and (in regulated industries) compliance team. We provide the draft, the framework, and the rationale. You approve.
How does this fit with our existing security and compliance program?
We work inside it. We don't propose a parallel AI-only governance structure. The AI controls live inside the existing security and compliance practice, with AI-specific extensions documented and approved.
We can map our framework to these. We don't lead with frameworks. We lead with what your organization actually needs to control. The mapping is simple once the controls are in place.
Can you take over operations from a vendor that built our first agents?
Yes. We do diligence on what they built, document what's there, identify gaps, and take over operations on agreed terms.
Scope is set in an assessment before any retainer. These are the factors that move the effort.
Policies, monitoring, and audit trails scale with how many AI and analytics systems you are governing.
Regulated industries and frameworks like the EU AI Act add classification, documentation, and review obligations.
Escalation paths, timeouts, and fallbacks take more to build where decisions are high-stakes.
Run-it-for-you work (prompt updates, model selection, access reviews) is a recurring tier on top of the build.
The build can stand alone, or pair with an ongoing managed tier so the governance is operated, not just documented.
You want to use AI across the enterprise but need policies and controls first.
You want someone to operate the governance, not just hand you a document.
You face regulatory pressure from the EU AI Act or sector rules.
You only need a one-time compliance gap analysis: see EU AI Act & AI Compliance Readiness.
Your core issue is data trust, not AI policy: start with Data Governance.
You want to build one specific agent, not govern a fleet: see AI Agent Consulting.