AI Infrastructure & Governance Planning
Governance that is enforced in the platform, not written in a policy document. A fixed-scope plan that answers who owns the AI controls, where models run, how every call is routed and capped, what guardrails apply, what each agent may touch in production, and what record it leaves. The team that writes it is the team that builds it out.
- Timeline
- 2-3 weeks, fixed scope
- Team
- architect, platform engineer, security architect
- Typical stack
- Your platform. The plan names the gateway, the policy store, the audit sink and the guardrail layer in terms of what you run; the build-out is the AI Platform & Orchestration engagement.
What you get
- 2 to 3 week engagement, fixed scope
- a reference architecture with enforced controls
- an owner for every control
Outcomes
- A named owner for every AI control, and the place in the platform where it is enforced
- A model gateway design: routing by cost and latency policy, hard per-team budgets, fallback to a second provider
- Guardrails and a permission model for what each agent may read, write and trigger in production
- Per-action audit records that answer an auditor or a customer asking what an agent did
- Data boundaries: which data may reach which model, where, and what never leaves
What we need from you
- The models and providers teams call today, and how the keys are handed out
- The last spend surprise, or the question an auditor or customer asked that you could not answer
- Which agents touch production systems, and what they are allowed to do
- Where models run now: vendor API, cloud region, on-prem, or all three
- The data classes in play and the rules that already govern them
Proof points
- A control inventory: control, owner, enforcement point, evidence it produces
- Gateway and budget design with the fallback path per provider
- Agent permission model and the audit record format, with a worked example
- Data boundary map by data class and model
Frequently asked questions
Whoever runs the platform the models are called through. Security and legal set the rules, and that is where most plans stop; the people who can actually enforce them sit in engineering or platform. The plan names an owner per control and the point in the platform where it is enforced, so the answer is a name and a place, not a committee.
Because nothing reads it at request time. A policy that is not enforced in the gateway, the budget or the guardrail is documentation. The plan takes each policy line and attaches it to a control that runs, or says plainly that it cannot be enforced yet.
One gateway in front of every provider, keys per team, hard caps that return an error rather than an alert, and a fallback provider so a cap or an outage degrades a feature instead of taking it down. The plan specifies this for your teams and providers; the orchestration engagement builds it.
After the plan, yes: every action an agent takes is written as a record with the input, the tool call, the output and the decision, kept somewhere an auditor can read. That is usually the trigger that starts this engagement, alongside a spend incident, and it is the first control we design.
That is model governance, and the MLOps engagement already covers it: registration, label-backed quality checks and a rollback that has been run. The plan references it rather than repeating it.
This plan covers the controls on the AI layer. The infrastructure underneath it is the platform readiness assessment on the DevOps page. If that layer still runs on console clicks, start there.
DevOps & Platform Engineering, the platform readiness assessment
No. It is a plan for the controls, written by the engineers who then build them, which is why it is fixed scope and two to three weeks rather than a quarter of workshops. The security and compliance engagement takes over where an auditor needs the evidence mapped to a framework.
Fixed scope and a fixed price agreed before work starts, for two to three weeks. The free platform readiness check tells you in five minutes where the layer underneath stands; it does not measure AI controls, which is what this plan is for.