Phil Taul

Practical AI automation, built and run in production by one person.

Twenty years of networking, infrastructure, and business across a dozen industries. I build AI systems for real businesses, then I operate them. Everything published here runs in production first: the case studies below are live systems, not demos.

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production-floor --tonight

title-pipeline58 forms overnight · 0 errors
voice-fleet37 VMs · multi-tenant · green
email-triage4x daily · drafts only
video-pipeline1 livestream in · a week of shorts out
daily-briefdrafted 07:20 · human publishes
backupsnightly · encrypted · off-site

real systems, summarized · client details anonymized

Case studies from the production floor

Three systems I built and operate today. Clients and identifying details are anonymized; the numbers are real.

Empty council chamber at night with a glowing waveform floating above the podium

Every quote traces to a timestamp

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.

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Clapperboard beside a glowing flowchart of production stages, one stage highlighted

The state machine behind a weekly show

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.

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Six stacked translucent network layers with a single light beam passing through all of them

Six vendors, one question: tracing a call across systems that don't know each other

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.

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How an engagement works

Every project starts with a paid Workflow Roadmap Session, then takes one of three paths.

Roadmap first

One 90-minute working session mapping your actual workflow, then a written roadmap: where automation pays off, what it takes to build, and what to skip.

Build and hand off

I build the system and hand it over with documentation written so your team can run and maintain it without me.

Build and operate

I build it and keep operating it: monitoring, backups, and iteration as your workflow evolves. The same posture I use on my own systems.

Work with me

Latest

Film strip transforming into a row of glowing vertical phone screens

Stop renting your video pipeline

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.

Vintage switchboard cables merging into a modern server rack with teal LEDs

Uptime thinking: running AI like it's a phone system

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.

A red button under a clear safety cover on a dark desk

The drafts-only rule: my AI writes everything and sends nothing

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.

Engineer's desk with three monitors of teal dashboards at night

This site is live. Here's what it's for.

Build logs, playbooks, and case studies from AI systems that actually run in production. Written by the one person who builds and operates all of them.