Carlos Castro · Austin, Texas

I learn how work moves, then help make it clearer.

I’m a business student with experience in student-housing turns, live event operations, and customer-facing work. I bring field observation, structured thinking, and modern tools to support better execution.

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WHAT I BRING

Operations judgment with product curiosity.

Field coordinationLive troubleshootingCustomer communicationWorkflow improvement

How I contribute

01Understand the work

02Make the next step clear

03Improve the operating loop

About Carlos

Business-minded, operationally curious, and still learning in public.

I’m completing a Business Administration degree with a Marketing minor at Concordia University Texas, expected December 2026. I’m interested in operations, implementation, product, customer success, and early-stage teams where practical judgment and technical curiosity overlap.

I use AI coding tools to accelerate implementation. My contribution is defining the workflow, supplying the field context, evaluating output, testing the experience, and identifying the operational risks around it.

Tools I use

OpenAI CodexClaude CodeVercelHugging FaceLocal modelsPocketPalGitHubSupabase

GitHub LinkedIn

Carlos Castro outdoors in a mountain setting — Carlos Castro · Wyoming

What the work has taught me

Real operations are the starting point.

My experience is not one product. It is learning to prepare, communicate, troubleshoot, document, and follow through when the work is moving fast.

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OPERATING FOUNDATION

01Prepare02Coordinate03Troubleshoot04Document05Follow through06Improve

Across field coordination, live events, and customer-facing service, I’ve learned that reliable execution depends on clear status, good handoffs, and attention to what happens next.

Experience

Evidence from the environments where I’ve worked.

Property Doctor Services

Complex Supervisor · Current

Coordinates student-housing turn operations across work areas: organizing priorities, inspecting work in progress, tracking unit and task status, and communicating follow-up needs. Turn OS is a personal supervisor companion, not company software.

BallerTV

Site Lead, Live Event Operations · 2024–present

Prepares and troubleshoots live-event equipment, completes readiness checks, and coordinates with venue contacts and teams during fast-moving tournament operations.

H-E-B

Cooking Connections Partner · 2025

Ran live product demonstrations, answered customer questions, and kept preparation, presentation, and supplies ready during high-traffic retail periods.

Vamos Coffee

Barista · 2024

Supported accurate, calm service and clean teammate handoffs during busy customer periods.

How I work

Observe, structure, build, test, learn.

Observe

Learn how the work moves before trying to change it.

Structure

Name the states, handoffs, decisions, and failure points.

Build

Prototype the smallest workflow that makes the next action clearer.

Test

Check the experience across people, devices, and real constraints.

Learn

Use evidence to decide what should improve next.

Selected project

Turn OS: one example of how I’m learning to build.

I began building Turn OS while learning how unit status, crew updates, follow-up, and reporting can move between paper notes, text messages, spreadsheets, phone calls, and individual memory.

THE PROBLEM

Field work is full of states that are easy to blur together.

Reported completion, supervisor verification, and management or client approval are not the same thing. The product model keeps those distinctions visible.

WHAT I’M BUILDING

Small, reviewable operating loops.

Unit tracking, issue capture, crew assignments, daily logs, reports, export, and offline-aware records are designed around what needs to be checked next — not around an AI claim.

CURRENT STATUS

Private prototype, under active validation.

Turn OS is not official Property Doctor Services software or a replacement for company processes. It is being developed and evaluated through personal, synthetic, or permitted redacted shadow use.

PRODUCT PRINCIPLES

Useful even when AI is unavailable.

Structured operational data and approved knowledge come before model output.
AI can draft, summarize, propose, or audit — never silently change consequential records.
Local save, recovery, export, and clear sync state matter in field conditions.
The next feature should follow evidence from use, not excitement.

What I learned building it

The product has to respect the field.

Turn OS is one ongoing learning process: understand the operating reality first, keep the data and decisions clear, and use technology to support—not override—people’s judgment.

Reported is not verified

A crew-reported update and a supervisor-verified condition need different states.

The field sets the product

The most useful next feature comes from observing the real work, not from adding complexity.

AI needs a review boundary

A draft can make follow-up faster, but a person should confirm consequential operational updates.