Data ownership, quality, lineage, and access rules can be applied across deployments.
GLOBAL RAILWAY OPERATIONS AND SOLUTIONS COMPANY / OPERATIONS
Reusable data governance delivered in ten weeks.
A global railway operations company was deploying AI across multiple business units and locations, but each new project required its own governance rules and processes. PivotX created a reusable governance framework for managing data, security, and AI models across both cloud-based systems and secure environments that are isolated from external networks.
Industry
Manufacturing
Function
Operations, Data and Analytics
Environments
Air-gapped and cloud
Delivered In
Ten weeks
RESULTS
Reusable governance standards
Standards that work across connected and isolated systems
Controls travel with the data in secure environments and work through cloud-native tooling where infrastructure allows.
Governed MLOps pipelines
The framework covers model versioning, evaluation, and deployment gates.
Business units can reuse the same governance standards, reducing setup work for new AI initiatives.
THE SITUATION
New AI initiatives were repeatedly rebuilding governance controls.
Different business units ran AI systems in both cloud-based and secure offline environments. Because governance requirements were defined separately for each project, teams repeatedly created new rules for managing data, security, and AI models instead of reusing existing work.
This increased the time required to launch new AI projects and made it difficult to share standards and key learnings across the organization.
01
Governance rebuilt from scratch for every new deployment
02
Knowledge developed for one project rarely transferred to the next
03
Controls needed to work across air-gapped and cloud environments
04
MLOps pipelines lacked a consistent governance layer
THE WORK
What we built
01
One governance framework across different technology environments
Before working with PivotX, AI projects running in different technology environments required teams to define their governance controls separately.
PivotX created one framework covering data ownership, quality, lineage, access, model evaluation, and deployment. It applies to operations and maintenance systems, synthetic-data projects, and the processes used to manage and deploy AI models.
Teams can now apply the same governance standards whether an AI system runs in the cloud or in a secure offline environment.
02
Governance the organization can maintain and reuse
Before PivotX's involvement, governance rules and lessons remained within individual project contracts. Other business units could not easily build on work that had already been completed.
PivotX designed the framework so the organization can maintain and extend it after each contract ends.
The framework was delivered in ten weeks. New projects can now begin with established governance standards instead of creating them again.
WHERE THIS TRANSFERS
Where this applies
Your AI program is growing, and each new initiative is rebuilding the governance infrastructure the last one already established.
You operate across environments with different security requirements, and your current governance cannot move between them.
The teams who know how the last deployment was governed are gone by the time the next one starts.
Proof
Pilots that became operating procedure.
PivotX has taken companies from initial assessment to production-grade AI in weeks, not quarters. The cases below aren’t proofs of concept. They’re actual production.
Which procurement workflow is creating the longest delay?
We can review the manual steps, handoffs, and systems involved and identify a practical place to begin.