I Design Your Project’s AI Layer
You are not hiring someone to add ChatGPT to your business. You are hiring someone to build the infrastructure that makes your team’s work with AI reliable, cheap, and owned by you. That is what an AI Architect does.
The work has four phases. First, diagnosis: I read your codebase, watch how your team actually uses AI today (often, the answer is “not yet”), and find the workflows where custom tooling would save real hours. Second, design: I scope the custom skills, hooks, subagents, and MCP servers that fit your project’s constraints, including its token budget, its sandbox, and its team’s existing tools. Third, build: I ship that tooling inside your codebase, with no shared dependencies and no external services you do not already use. Fourth, handoff: your team owns the result, documented and editable, and I step out.
No templates, no white-labels, no “here is a starter pack.” What you get is built for your project, by hand, from scratch. The result is an environment with fewer moving parts than what most teams rig up on their own, because every piece is designed to fit your project instead of bolted on from a generic toolkit.
What a Claude Code Environment Actually Looks Like
The infrastructure layer has four primitives. Skills are custom commands your team invokes by name, each one doing a specific job inside your project (generate this article’s WordPress blocks, audit this import for price regressions, build this social video from a source image). Hooks are automations that run on file edits or tool calls, like blocking a database rebuild or auto-converting every image to AVIF. Subagents are focused agents that do one thing well without polluting the main session: a migration reviewer, a spec verifier, a price auditor. MCP servers are pluggable tool servers that expose your existing systems to Claude Code with a clean interface, so the rest of the tooling can call them like plain functions.
For what this looks like across six real projects, see AI Architecture in Practice. Each one has its own mix of primitives, its own named skills, its own hooks and subagents, and its own operational constraints that shaped every choice.
Token Budget, Sandboxing, and Cost Control
Every build I ship has three business-critical properties that a local business owner can understand even if you have never written a line of code.
Token budget awareness means every custom skill is planned to fit inside a spending envelope before it is planned to fit a feature list. AI API spend gets out of hand quietly. I design each piece of tooling to know which model it uses for which step, where caching applies, what runs as a cheap background hook, and what needs a premium model. On high-volume work, I build a cost view so the number is visible before it becomes a surprise on your monthly bill.
Sandboxing means nothing I build for your project reaches into anything else. No shared repositories, no hosted services you did not already sign up for, no dependencies on another client’s tooling. If you take your code to another contractor tomorrow, every skill, hook, subagent, and MCP I built comes with it.
Cost control is the other side of the token budget. I set explicit model routing (fast cheap models for simple tasks, premium models only where they are needed), aggressive caching for repeat inputs, and batch processing where latency allows. You get a monthly API spend forecast before work starts and observability once the tooling ships, so you can see exactly what each workflow costs and why.
Your sandbox stays yours. Nothing I build for you depends on anything I have ever built for anyone else.
For Agencies and Engineering Teams
If you are running a dev shop or an in-house engineering team doing a Claude Code rollout, the value prop is different. Your engineers are already figuring out hooks and skills on their own. What you need is fewer reinventions, a faster ramp for new projects, and governance on top of the piece-by-piece work your team is already doing.
I can embed for a sprint, deliver a rollout plan your team executes, or build the missing skills your team keeps wanting but never has the time to ship. Engagement shape is flexible. The deliverable is the same: custom tooling matched to your team’s specific constraints, documented and owned by you.
How the First Two Weeks Look
Week 1 is diagnosis. I read your codebase, sit in on how your team is using AI today (including not at all, which is often the most useful starting point), and map the workflows where custom tooling would pay off fastest. By the end of week 1, you get a scoped plan with honest cost-benefit on each proposed build.
Week 2 is the first custom build. Usually one skill, one subagent, and one hook. Small enough to ship inside a week, concrete enough that you can see what the work actually looks like before committing to anything larger. After week 2, you decide whether to keep going, stop there, or scope differently.
This is the lowest-risk way to find out whether an AI Architect engagement is worth it for your business.
