I am an entrepreneur and software builder focused on creating practical AI systems, automation platforms, data tools, and intelligent workflows that solve real operational problems.

My background combines business ownership, emergency medical services, logistics, management, and software development. Before moving deeper into technology, I founded and operated an ambulance company that grew from one ambulance to 21 ambulances and 134 employees. That experience taught me how to build systems, lead teams, manage high-pressure operations, solve problems quickly, and turn an idea into a working organization.

Today, I apply that same mindset to software. I enjoy taking ambitious ideas, breaking them into manageable parts, and building systems that are reliable, useful, and capable of growing over time.

Jarvis Command Center is the primary interface for a larger collection of AI-powered systems. I am designing it as a mission-control platform where users can communicate with AI workers, assign tasks, review approvals, monitor costs, access project knowledge, and manage specialized applications from one place.

The long-term goal is to create a persistent, voice-enabled AI command center that can operate across desktop, mobile, local servers, and cloud infrastructure.

Key areas include:

• Multi-agent task orchestration
• Voice interaction and conversational interfaces
• Human approval controls for sensitive actions
• Persistent task history and project memory
• Worker health and status monitoring
• Cost and usage controls
• Mobile access
• Real-time dashboards
• Local and cloud-based AI model integration

AI-Jarvis-2.0 is a local-first AI operating system and orchestration platform designed for persistent, project-aware, multi-agent work.

The system is being developed to coordinate AI tools, route tasks to the right agent, maintain project context, track activity, and provide a visible operator interface.

Some of the major goals include:

• Persistent project memory
• Intelligent task routing
• Multiple AI provider support
• Agent observability
• Secure approval workflows
• Budget enforcement
• Local model support
• Cloud deployment
• Integration with development tools and external services

I am especially interested in building AI systems that do more than answer questions. I want them to understand projects, complete useful work, maintain context, and operate within clearly defined safety and cost limits.

Jarvis AI Trading is a research-focused platform for developing, testing, and evaluating algorithmic trading strategies.

The project combines market data, strategy research, backtesting, signal generation, dashboards, and AI-assisted analysis. My current focus is on futures research, particularly the Micro E-mini S&P 500, with strict separation between research, paper trading, and live execution.

Areas of interest include:

• Algorithmic trading systems
• Futures and market-structure research
• Backtesting and strategy validation
• Risk and cost modeling
• Market regime analysis
• Signal detection
• Trading dashboards
• AI-assisted research
• Reliable execution infrastructure

My approach is to test ideas honestly, account for realistic trading costs, and avoid treating an unproven strategy as profitable until the evidence supports it.

Government Contract Intelligence is an independent data and research engine that analyzes government award information and converts it into structured opportunity signals.

The system is designed to collect public award data, normalize contract information, resolve companies to public-market identities, and identify developments that may be relevant to investors, businesses, or researchers.

The project currently explores information from sources such as:

• USAspending
• Department of Defense contract announcements
• SEC company data
• Public company identifiers
• Government procurement records

The goal is to turn large amounts of public information into structured, explainable, evidence-backed intelligence.

The Opportunity Engine is a reusable layer that sits between specialized data engines and downstream applications.

Its purpose is to receive structured signals, evaluate their significance, preserve supporting evidence, assign confidence levels, and deliver useful opportunity information to other systems.

The broader architecture is:

Specialized Data Engine → Opportunity Engine → Opportunity Signal → Jarvis Platform

This allows future systems—such as patent, FDA, SEC, congressional, industrial, or market intelligence engines—to use a consistent format.

The Alpha Engine SDK is a shared development standard for creating independent intelligence engines.

It is being designed to standardize:

• Engine identity
• Signal formats
• Evidence records
• Confidence scoring
• Opportunity scoring
• Information lifecycle
• Reasoning and recommendations
• Schema versioning
• Integration with Jarvis systems

My goal is to make it easier to build specialized research engines without repeatedly rebuilding the same infrastructure.

I enjoy building systems that connect multiple technologies into one useful product. I am especially interested in projects involving:

• Artificial intelligence
• AI agents and orchestration
• Voice-enabled applications
• Full-stack platforms
• Python backend services
• Automation systems
• Algorithmic trading tools
• Data pipelines
• Real-time dashboards
• Knowledge graphs
• Persistent AI memory
• Government and financial intelligence
• Developer tools
• Cloud and local infrastructure
• API integrations
• Business process automation

I am most motivated by projects that have a clear purpose, solve a real problem, and can eventually become a dependable product.

I work with and continue to develop my skills in:

• Python
• JavaScript
• React
• HTML
• CSS
• SQL
• Pine Script

• OpenAI APIs
• Anthropic Claude
• Claude Code
• Google Gemini
• Local large language models
• AI agent workflows
• Model routing
• Prompt engineering
• Structured AI responses
• Model Context Protocol integrations

• FastAPI
• REST APIs
• SQLite
• PostgreSQL
• Supabase
• JSON and JSONL data systems
• Data normalization
• Relational database design
• Persistent memory systems
• Knowledge graphs

• Git
• GitHub
• GitHub branches and pull requests
• Visual Studio Code
• Linux and Ubuntu
• PowerShell
• Render
• Tailscale
• Docker
• Environment variables
• Virtual environments
• Local servers
• Cloud GPU infrastructure

• Pandas
• Alpaca API
• TradingView
• NinjaTrader
• Futures data
• Backtesting systems
• Market signal research
• Strategy monitoring

I believe the fastest way to learn technology is to build real projects and work through real problems.

I am currently strengthening my knowledge of:

• Git and professional GitHub workflows
• Full-stack software development
• DevOps practices
• Multi-agent AI architecture
• Scalable backend systems
• PostgreSQL and database design
• Cloud deployment
• Linux server administration
• Secure API design
• AI evaluation and observability
• Model Context Protocol integrations
• Software testing
• Production monitoring
• Structured data pipelines

I am also focused on improving how projects are planned, documented, reviewed, tested, and shipped.

I am involved with the NextWork community and Build & Brew meetings in Houston, where I enjoy helping create an environment for people who want to learn, build, and gain practical experience.

I believe learners benefit from working together using professional development practices such as:

• GitHub repositories
• Feature branches
• Pull requests
• Code reviews
• Project boards
• Team documentation
• Real project ownership
• Collaborative problem-solving

One of my goals is to help people move beyond tutorials and gain the confidence to take on freelance work, contribute to teams, and build portfolio-ready projects.

I enjoy working with people who are motivated, dependable, open to learning, and willing to turn ideas into working products.

• AI agent platforms
• Open-source AI tools
• Voice-enabled AI applications
• Full-stack SaaS products
• Developer productivity tools
• Algorithmic trading research
• Financial and government intelligence systems
• Knowledge graphs and persistent memory
• Business automation
• Real-time monitoring platforms
• Community-based learning projects
• Tools that help people launch businesses or improve operations

• Building a business from the ground up
• Turning operational problems into software ideas
• AI agent architecture
• Multi-agent orchestration
• Python automation
• Algorithmic trading research
• Government contract data
• GitHub collaboration workflows
• Building practical portfolio projects
• Moving from an idea to a working prototype
• Learning technology through hands-on projects

I believe good software should be useful, understandable, secure, and built with a clear purpose.

A few principles guide how I work:

• Build real things. Practical projects create deeper learning than theory alone.
• Ship, test, and improve. A working first version creates something that can be evaluated and improved.
• Reuse before rebuilding. Strong systems should build on existing capabilities instead of duplicating them.
• Keep humans in control. Sensitive, expensive, or high-impact AI actions should require clear approval.
• Measure honestly. Whether evaluating software, trading strategies, or AI performance, results should be based on evidence.
• Design for growth. Small systems should be structured so they can expand without requiring a complete rebuild.
• Document the work. Clear documentation makes projects easier to understand, maintain, and share.

My career has taken me through emergency medical services, company ownership, logistics, freight operations, investing, and software development. Although those fields may appear different, they all require many of the same skills: leadership, decision-making, organization, communication, risk management, and the ability to solve difficult problems under pressure.

I bring an operator’s perspective to software development. I do not only think about whether something can be built—I also think about how it will be used, maintained, monitored, improved, and turned into something valuable.

Outside of building technology, I enjoy meeting other builders, discussing business ideas, learning new tools, researching financial markets, and helping people see that difficult goals are often achievable when they are approached one step at a time.

And with a name like Sean Patrick Fitzgerald, my favorite brew is naturally a Guinness. 🍺

• GitHub: SFitz911
• Location: Texas, USA
• Open to: Collaboration, technical discussions, community projects, and opportunities to build useful software
• Email SFitz911@gmail.com

“Nothing is out of reach when you are willing to learn, solve one problem at a time, and keep building.”