000 · LOADING THE WORK
Antonio Urbina Jr.
Agents in production
Antonio Urbina Jr. · Solutions Engineer · Portfolio 2026

I build AI
that shows up
for work.

Solutions engineer & AI automation consultant. I design, ship, and operate conversational AI in production — and the platforms around it. Every number here is from a live system I run today.

Scroll
Conversational AIAgent OperationsWeb PlatformsAutomationTeachingSolutions Engineering Conversational AIAgent OperationsWeb PlatformsAutomationTeachingSolutions Engineering
№ 01 — The short version
I don't demo agents. I employ them. One person, a stack of AI agents, running real businesses — accounting firms, ops teams, messy data, real money.

The craft. Architecture, data models, pipelines, interfaces, and the unglamorous operations layer — uptime, cost, guardrails. Built solo, owned end-to-end.

The proof. Four client platforms live in production. An AI agent on duty 24/7. A teaching cohort running the same patterns. Nothing on this page is a mockup.

№ 02 — Around the world, for work
2011
Scroll to travel the world · 01 / 13
№ 03 — Case study, in depth

I built a cruise line’s
mobile backbone from zero.

Norwegian Cruise Line Holdings — Norwegian · Oceania · Regent Seven Seas · 27-ship fleet · 2016–2021
01 · The mandate

When I arrived there was no mobile or automation function — just me, and a fleet running on manual process. So I built the department, and architected the shipboard infrastructure end-to-end.

02 · The architecture

Up to ~10,000 devices on a single large ship, across all three brands. Strategic satellite deployments, a device staging lab in Doral stood up with the Apollo Group, and the training + handover docs so each ship’s System Managers and Fleet Assistants could run it without me.

03 · The projects

CBP facial-recognition onboarding at the gangway. Guest-facing apps through the full software lifecycle — and I was the final review gate before anything shipped to the App Store, iOS and Android both. Modernized culinary, restaurants and housekeeping operations. Security sign-off on everything guest-facing.

04 · The outcome

From one person to a team of four. A fleet-wide mobile backbone where there had been none — and a human checkpoint on every release, years before I’d apply that same instinct to AI.

The results didn't stop at sea — every chapter shipped outcomes
Zebra Technologies2021–2023

SME for automating & integrating custom software into Zebra hardware. Led nationwide rollouts — The Home Depot (US + Canada), Lowe's, Target, Dick's, Pilot Travel, Woolworths Australia.

  • 125,000+devices deployed
  • 2,300store locations
  • 80%less manual config · ~41,400 hrs saved
Booz Allen · Federal2023–2024

High-level federal security architecture spanning several agencies on CISA's CDM mission. Under NDA — specifics stay sealed; the scope did not.

  • Severalfederal agencies, one posture
  • National-scale security architecture
  • Compliance-grade coordination
Patriot National2011–2016

Where it started — modernized enterprise mobility & endpoint security across the org. The grounding in systems that simply have to stay up.

  • Hardenedendpoints & mobile security
  • Integratedsystems at enterprise scale
  • Builtthe foundation for everything after
№ 04 — The awakening
Then, the winter of 2022. I typed into a beta — and the future answered back.
ChatGPT · research beta · Nov 2022

I was there on day one. That was the aha — the moment my brain started to imagine again. Eighteen years of enterprise plumbing suddenly had a new verb: talk to it. I haven't stopped building since.

Foretold — conversational computing
Forty years ago, Star Trek gave us a computer you simply talk to.I'm finally building it.

I've been a Trekkie my whole life. Conversational AI isn't a new idea — it's an old promise, arriving. We aren't inventing it. We're shipping it.

From that first prompt to a stack I run today —
ChatGPT Claude Gemini Agents Managed agents OpenClaw Hermes Eli
№ 05 — Case study · conversational AI in production

The agent that
never sleeps.

“Eli” — the AI agent that runs my practice. Self-hosted, multi-channel, on duty since early 2026.
Runtime
OpenClaw on a Mac mini M4, launchd-supervised, 24/7
Channels
Slack (production) · Telegram · Discord — allowlisted access control
Models
Anthropic Claude + OpenAI, routed per task for cost & capability
Memory
Active context + long-term store, consolidated nightly at 3am — it sleeps on it
Tools
Headless browser, email triage, calendar watch, transcript ingest, health checks
Guardrails
Permission tiers on execution, secrets management, crash-loop recovery, weekly memory curation
A working day · unattended
  • 06:00Transcript ingest — meetings → searchable notes
  • 07:00Morning brief — email + calendar + infra
  • 4× / dayEmail triage — routed & flagged
  • 10 minBooking detection — real-time alerts
  • 21:00Spend audit — API cost vs budget
  • 03:00Dreaming — memory consolidation
№ 06 — Selected work

Platforms with AI
doing the heavy lifting.

Case file K-01B2B · Live

Industrial Order Portal

B2B distributor · ERP order-ops automation
ChallengeOrders arrived as ERP alert emails and got retyped by hand — slow, error-prone, no trail.
BuiltAn automation pipeline: Outlook → MS Graph → a parser that reads 5 ERP email formats → structured orders, line-items & documents.
Outcome0emails parsed clean · 197 orders auto-loaded · every message audited.
React · TSSupabaseMS GraphZodRLS
Case file T-01Fintech ops · Live

Reconciliation Engine

Bookkeeping firm · medical-device client
ChallengeA team hand-matched payments across 18 bank & processor feeds every month — hours of work, easy to miss.
BuiltA matching engine (bank-first logic) + a dashboard the team works daily — partial-amount search, manual-match, audit feed.
Outcome0matched automatically · scope grew 9 → 18 sources on results. Exactly the messy-money problem agents are next for.
PythonPostgresVercel FnPDF parseCron
Case file R-01Events · Live → growing

REFRAME → Event OS

Reframe Accounting · national conference
ChallengeA national accounting conference needed attendee + sponsor commerce — on a date that could not slip.
BuiltOne SPA: Stripe checkout, on-the-fly PDF sponsor contracts, private signed-URL storage, passwordless auth. Now growing into an Event OS.
Outcome0PRs across two live UAT rounds in 11 days · shipped on deadline. Full case study ↓
React · ViteStripepdf-libSupabaseResend
Case file S-01Internal · Live

Mission Control

My own client-operations portal
ChallengeRunning several client engagements solo scatters context across a dozen tools.
BuiltOne portal — status, requests, e-signature, email — every action auto-logged. The control plane my agents and I both work from.
Outcome0client engagements on this page run end-to-end through it.
Next.js 14SupabaseDocuSealResendRBAC
DRAG / SCROLL · 01 — 04
№ 07 — Case study, in depth

From a payment portal
to an Event OS.

Reframe Accounting — Hector & Carlos Garcia · REFRAME 2026 · Loews Coral Gables · Nov 2026
01 · The brief

A national accounting conference needed attendee and sponsor commerce in one place — registration, payments, sponsor packages, contracts — on a date that could not move.

02 · Shipped

One SPA doing real work: Stripe checkout for attendees and sponsors, sponsor contracts generated as PDFs on the fly, stored behind private signed URLs, passwordless sponsor auth. I consolidated 32 serverless functions down to 9 to fit platform limits, and delivered through repeated live UAT rounds with the client.

03 · Where it's going in active development

It's outgrowing the word "portal." The foundation for an Event OS is already in — an event-app shell with content management and issue tracking, feature-flagged and in preview. The next layer is conversational: an assistant attendees and sponsors can simply talk to — schedule, logistics, contracts, support — the same production conversational-AI pattern I run elsewhere, pointed at a live event.

0/7
Production agent uptime, self-hosted
0
Client platforms shipped & live
0+
Automated routines running daily / weekly
0
Operator. Plus a stack of agents.
Antonio Urbina Jr.
The only call to action
If you've read this far —

I've already lived the second half of the conversational-AI problem — the part after the demo. Multi-channel agents real people talk to daily. Memory that stays accurate over months. Cost controls, permission tiers, audit trails, and the judgment to put a human checkpoint exactly where the model shouldn't be trusted alone.

I work where it actually counts: messy real-world data, compliance-minded clients, systems that have to be right — not just impressive. Eighteen years of enterprise rigor, now pointed at AI. And because I teach this, I bring a team along, not just ship beside them.

Book a call or just reach out
— Antonio