Our company's marketing department is me. Our research assistant is me. So I engineer for that the same way I engineer for clients: find the real bottleneck, weigh cost against security, and decide, on purpose, whether to buy the tool or build it. This page is the part of my work that never ends, my ongoing practice.
Editing a talking-head video is the same twenty decisions, every time. Years of voice memos, the thinking behind every project, sat unsearchable on a phone. And every promising AI tool that could help came with the same unanswered questions: where does the data go, what does it cost at scale, and who checked?
A two-person company can't hire its way out of any of that. It has to engineer its way out.
I almost bought a video tool. I'd assumed the value was in the editing itself. Then I noticed that most of editing isn't creative at all. It's deterministic. Cut the silences, sync the captions to the transcript, apply the brand treatment. If I can write down the decisions, I build it as code I own. That's the rule I actually use: if the workflow's decisions can be written down, build the pipeline; if the value is someone else's ongoing R&D, buy it, after the security review.
And the inverse discipline matters as much: knowing what not to automate. Some steps stay manual on purpose, because the judgment is the value, or because the failure mode of automating it is worse than the labor of doing it.
The pipeline ships polished video without a production hire. The research database turns years of voice memos into an asset instead of an archive. The audit habit means every tool in the stack was adopted with eyes open: cost, security, and public-records exposure priced in.
When I recommend an AI architecture to a client, it's the same reasoning I use on my own company every week.