From MLB Pitcher to AI Agent Architect: A Founder's Story
What pitching in professional baseball taught me about building AI systems: specialization, parallel execution, and knowing when to trust your team.
The Mound
I used to throw 95 mph fastballs for the Washington Nationals organization. On the mound, you learn something fast: you can't do everything yourself.
A pitcher doesn't field every position. You don't chase fly balls. You don't cover second base on a double play. You throw the pitch. Your team handles the rest. Each player has a specialized position, and the magic happens when everyone executes their role simultaneously.
That lesson stuck with me long after I hung up the cleats.
The Pivot
After baseball, I went into tech. Founded VeloX Data Co, a B2B data integration company serving dental service organizations. Built systems that process millions of records through automated pipelines.
But the real inflection point came when I started experimenting with AI agents — not the single-chatbot-does-everything approach, but coordinated teams of specialized agents.
I built a system called ClawdSquad: 13 AI agents, each one an expert in a specific domain. UX analysis. Security auditing. Code architecture. Growth strategy. QA testing. When I deployed the full squad on a project, something clicked.
The Diamond and the Dashboard
Here's what baseball and AI agent squads have in common:
| Principle | Baseball | AI Squads |
|---|---|---|
| Specialization | 9 positions, each mastering their zone | 13 agents, each mastering a domain |
| Parallel execution | All 9 players ready simultaneously | All 13 agents analyze simultaneously |
| Coordination | Shortstop-to-second-to-first double play | Security finding feeds into compliance check |
| Trust the team | Pitcher trusts fielders to make plays | Commander trusts agents to find their issues |
| Score the game | Runs, hits, errors — clear metrics | Scored findings, blocker protocol, quality ratings |
The Proof
I ran the squad on my own apps first. Over 100 internal audits across multiple projects. The results were undeniable:
- App quality scores jumped from low-6s to 9.0+
- App Store blockers caught every single time before submission
- Competitive intel that would take a team weeks delivered in 12 hours
- Code reviews that found dead code, security holes, and architectural issues humans had been stepping around for months
The squad approach wasn't just better — it was a different category of output.
Why SquadOps
After running these squads internally, the question was obvious: why isn't everyone doing this?
The answer: because building multi-agent systems is hard. Configuring 13 specialized agents with scoring rubrics, blocker protocols, and cross-domain coordination isn't something you can set up in an afternoon.
So we productized it. SquadOps gives everyone access to the same squad we built for ourselves. You don't need to configure agents, write prompts, or build infrastructure. You submit a briefing, we deploy the squad, and you get results.
The Vision
The future of consulting isn't bigger human teams billing more hours. It's specialized AI squads delivering better results in less time at a fraction of the cost.
We're building that future. And just like on the mound — we're trusting the team to make the plays.
Deploy your first squad.
Results in under 24 hours. Built by a team that knows what specialization means.
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