One Engineer. Zero Overhead.

Most consultancies sell you a team, then staff your project with whoever is available. The person you met in the sales conversation is not the person doing the work. Requirements pass through account managers and project managers before reaching an engineer, and feedback travels back the same way. Things get lost. Assumptions get made. The client wonders why the delivered system does not quite match what was discussed. I do it differently. When you hire Tampa Dynamics, you work with me directly. I am on the calls. I am making the architecture decisions. I am writing the code, reviewing the security controls, and configuring the deployment pipeline. There is no intermediary between your requirements and the person responsible for meeting them. This model works because the economics of software development have changed. AI handles a substantial portion of what used to require multiple engineers: boilerplate code, test scaffolding, documentation, routine research. Infrastructure-as-code makes environment provisioning repeatable and auditable. These tools do not replace engineering judgment — they make it more efficient. What I deliver is senior-level thinking on every decision, applied directly to your system, without coordination overhead.

I use AI agents extensively — for code generation, test scaffolding, documentation, and research. This is not about cutting corners. It is about directing human attention toward the decisions that require it: architecture tradeoffs, security modeling, the nuanced requirements that come with regulated industries, and the edge cases that only surface when someone has seen a domain long enough. The distinction I hold onto is this: AI handles volume, I handle judgment. An AI can generate a CRUD API from a schema in minutes. It cannot evaluate whether that schema reflects the right access control model for a HIPAA-covered system. An AI can write unit tests for a function. It cannot determine whether the function is testing the right behavior given the compliance requirements the system needs to satisfy. On AI projects specifically, I build the systems that other teams will use — retrieval-augmented pipelines, model guardrails, intent detection, audit logging for AI interactions. I apply the same standard to those systems that I apply to any system handling sensitive data: if it cannot be audited, it should not be in production.

Solo does not mean isolated. There are areas where bringing in a specialist produces a better outcome than doing it myself, and I am transparent about where those lines are. For product design and UX work that goes beyond component-level decisions, I have worked with a small group of designers I trust for years. They understand technical constraints and do not need extensive onboarding to contribute meaningfully. For specialized compliance expertise — specific state health data regulations, legal holds requirements, or financial industry-specific obligations — I bring in domain experts rather than rely on my general familiarity. What does not change: I remain the primary point of contact, I remain responsible for the architecture and the technical decisions, and you do not suddenly find yourself in a room full of people you have never met. Collaborators are brought in for specific, scoped contributions where their expertise meaningfully improves the outcome.