"Onshoring" can sound abstract. What does it actually mean in practice?
It means owning four capabilities that, together, give an enterprise genuine control over their AI infrastructure.
1. Govern
Governance is the ability to set policies, audit actions, and ensure compliance—across all AI activity.
This includes:
- Access controls: Who (and what) can access which data, systems, and actions
- Audit trails: Complete logs of what agents did, what data they accessed, and why
- Policy enforcement: Automated guardrails that prevent agents from exceeding their authority
- Reversibility: The ability to undo or roll back agent actions when necessary
A private equity firm we work with built versioned Agent Operating Procedures (AOPs) with full attribution tracking. After 10, 20, 50 runs, they can review performance and systematically improve. Every action is logged. Every output is traceable.
Governance isn't a feature you buy from a vendor. It's a capability you build into your infrastructure.
2. Build
Building is the ability to create agents, workflows, and automations using your own data, logic, and institutional knowledge.
This is where the real differentiation happens. A vendor's pre-built agent is trained on generic patterns. An agent you build knows your processes, your terminology, your edge cases.
One law firm identified 52 distinct agent use cases within a single practice area—each requiring domain-specific knowledge that no vendor could provide out of the box. Due diligence workflows, regulatory monitoring, contract analysis—all customized to how they work.
When you own the building capability, this customization is straightforward. You iterate, refine, and improve based on actual usage. When you rent, you file feature requests and wait.
3. Configure
Configuration is the ability to adapt AI behavior without waiting for vendor roadmaps.
Requirements change. Regulations shift. Business priorities evolve. The AI infrastructure that worked six months ago may not fit today's needs.
Onshored capabilities mean you control the configuration layer:
- Adjust agent behaviors in hours, not weeks
- Modify workflows without re-negotiating vendor contracts
- Respond to compliance requirements on your timeline, not theirs
A Fortune 500 retailer we deployed with needed to change their financial reporting workflow mid-quarter. With onshored infrastructure, their team made the adjustment same-day. No support tickets. No waiting.
4. Deploy
Deployment is the ability to run AI infrastructure in your environment—whether that's your private cloud, on-prem data center, or across multiple public clouds.
True onshoring is cloud-agnostic by design. The same architecture deploys to AWS, GCP, or Azure. You choose based on your requirements, your existing investments, your regulatory constraints—not your AI vendor's preferences.
This also means deployment flexibility within your environment:
- Private cloud: Full isolation, full control, compliance-ready
- Hybrid: Some capabilities in your data center, others in your VPC
- Multi-cloud: Workloads distributed based on cost, performance, or regional requirements
We made cloud-agnostic architecture a day-zero decision when we built Athena—deployable across all three major clouds using Terraform. One Fortune 500 deployment runs 100% on GCP. Our managed cloud runs on AWS. Another enterprise customer is 100% Azure. Same platform, different infrastructure.
If you're locked into one cloud because your AI vendor is, you haven't onshored. You've just accepted a different landlord.