AI Infrastructure Review
Know if private AI is realistic before you commit.
A focused readiness review for regulated teams evaluating private LLMs, production RAG, sovereign agents, or Kubernetes-based AI workloads. We map the architecture, data boundaries, operational risks, and implementation path so your team can make a clear platform decision.
The review focuses on the decisions that make or break private AI.
Data and privacy boundaries
We identify what data enters the AI system, where it crosses trust boundaries, which services can retain prompts or responses, and what must stay inside your environment.
Model hosting options
We compare cloud APIs, private managed endpoints, local models, and self-hosted serving against your latency, security, cost, compliance, and operations requirements.
RAG and agent architecture
We review retrieval quality, access-aware ingestion, agent tool permissions, approval workflows, fallback behavior, evaluation, and auditability.
Kubernetes and platform readiness
We assess whether your platform can support model serving, GPU scheduling, storage, networking, policy boundaries, observability, and Day-2 operations.
Observability, cost, and operations
We look for gaps in tracing, quality checks, latency, spend visibility, incident response, upgrade paths, runbooks, and ownership after launch.
Implementation sequence
We turn findings into a practical roadmap: architecture decisions first, then platform work, application changes, risk controls, and operational handoff.
You leave with decision-ready artifacts your team can act on.
- Executive readiness memo with go / no-go recommendation
- Data-flow and trust-boundary map for AI workloads
- Model hosting recommendation with cost, security, and operations tradeoffs
- Target architecture notes for private LLM, RAG, or agent rollout
- Kubernetes/platform readiness scorecard covering serving, storage, GPU, policy, and observability
- Risk register for privacy, compliance, access control, evaluation, cost, and Day-2 operations
- Prioritized 30/60/90-day implementation roadmap with owners, sequencing, and next decisions
Need to know whether private AI is realistic for your team?
Book a 30-minute AI Infrastructure Review. We'll map your data boundaries, platform risks, agent controls, and operating model before you commit to a build path.