Case Studies
AI-assisted coverage of public meetings, built for a world that rightly distrusts AI-written news: local transcription, draft articles where each claim maps to a moment in the recording, and two human gates before anything publishes.
Case Studies
A weekly live production has a dozen stages, hard deadlines, and one operator. The fix was not more effort. It was a production ledger that always knows exactly where every episode stands, so a crash costs nothing but the restart.
Case Studies
A phone problem can live in any of six layers owned by five different vendors. This is how the ops stack from the earlier case study actually works: a diagnostic ladder that walks the whole call path from one conversation.
Case Studies
A watch-everything intelligence engine for one geographic market: public sources in, deduplicated and relevance-scored items out, a cited morning digest by 7 a.m. Adding a second market took a JSON file and zero code changes.
Case Studies
A one-person operation cannot afford a two-week rebuild after a dead machine. So both machines rebuild from one encrypted off-site copy, one script, one passphrase. Then the backups started failing on exactly one machine, and that was the point.
Case Studies
Every consultant hates timesheets, and every consultant leaks billable hours to bad memory. This system reconstructs the workday from evidence the tools already emit, and a human approves every entry before it becomes money.
Case Studies
A hyperlocal news platform: one weekly livestream becomes a week of short videos and articles, and a daily community brief drafts itself by 7:20 every morning. Humans still make every editorial call.
Build Logs
Per-minute AI clip tools make sense until you publish weekly. Then the math flips, and owning the pipeline wins on cost, quality, and control. Here's the math from my own show.
Case Studies
How AI tooling lets a single architect operate a multi-tenant cloud voice fleet, dozens of client networks, and a full security posture that used to need a team.
Playbooks
Phone systems taught me a standard: when it breaks, someone can't call 911. AI automation deserves the same discipline, and almost nobody builds it that way.
Case Studies
An auto dealer group was burning 20 to 30 staff hours a week transcribing vehicle titles onto state forms. Now an AI pipeline does it overnight, and a human only touches the titles that deserve a second look.
Playbooks
Every automation I run can draft an email, a reply, a report. None of them can hit send. That one design rule is why I can run AI against real business email without losing sleep.