AI — When It Makes Sense

There's a lot of noise about AI right now. Most of it is hype. But underneath the hype, there are real tools that can save time and reduce manual work — if they're applied to the right problems.

I don't lead with AI. But when it fits — extracting data from documents, drafting repetitive content, answering common questions — I'll use it. And I'll be honest when it doesn't fit.

Anyone can demo an AI tool. Building something that actually works in your business — that connects to your systems, handles edge cases, and doesn't break when the data gets messy — that's different.

I've built these kinds of integrations manually for 20+ years: data pipelines, system integrations, workflow automation. I know how they fail. AI makes some of this faster, but it doesn't replace the judgment of someone who's done it the hard way.

• Start with the problem: AI is only useful if it solves something real. We figure that out first.
• Small pilots: I don't recommend betting big on AI. Try it on one workflow, see if it works, then expand.
• Guardrails built in: AI makes mistakes. I build in human review where it matters.
• Production-grade: Not a demo. Not a prototype. Something your team can rely on.

Same model as all my work — fixed scope, fixed price:

Every project is scoped before we start. No surprises.

1. We talk: You tell me what's frustrating. I ask questions to find where AI might actually help.
2. I scope it: Clear proposal — what I'll build, what it costs, what "done" looks like.
3. Small pilot first: We test on one workflow before expanding. Lower risk, faster learning.
4. You use it: I hand it off with documentation and make sure your team is comfortable.
5. We iterate: AI improves with feedback. I stick around to tune and expand.