I’m not a traditional programmer. I’m a digital marketing and web production professional who knows code well enough to work with AI at a higher level.
That means we start with your marketing goals, define the problem clearly, and then use AI to help build the solution. Developers build what you ask for. I help you figure out what you need, how to get there and then make it happen.
We start in plain language: what problem are we solving, who uses it, and what “done” looks like.
From there, we translate that into specifications AI can work with. Prompts include context, constraints, examples, and edge cases. Not just “write me code”, so we get useful, testable solutions instead of fragile snippets.
We break the project into small, testable components: data processing, UI, API calls, reporting blocks, and so on.
Each piece is built and tested separately, then wired together. This modular approach makes debugging easier and upgrades less risky.
Generate code, review behavior, refine prompts, and repeat until the tool matches the real-world need.
I keep external notes of decisions, constraints, and specs, and re-inject them into prompts so we don’t lose context or accidentally reintroduce old bugs.
AI can suggest three different solutions to the same problem. I use version control and comparative testing to pick the approach that actually works and is maintainable.
Once something is proven, it becomes the new baseline, not overwritten by the next “gut feeling” answer. I’ll also use multiple AI tools: one for architecture, another for code generation, and sometimes a third for debugging. Cross-checking increases reliability.
Different tools have different strengths. I might use one model to outline architecture, another to generate code, and a third to explain or debug tricky behavior.
This multi-tool workflow surfaces better patterns, catches edge cases earlier, and keeps the final solution grounded in how you’ll actually use it.
Custom reporting dashboards that pull from multiple sources. Marketing calculators or interactive tools for your website. Data integrations between systems that don’t talk to each other.
Automated workflows that turn manual, repetitive tasks into one-click processes. Parsing and processing data in ways existing platforms can’t handle. These are practical, well-defined problems where speed and iteration matter more than enterprise architecture.
Freelance developers are great for big projects, but they won’t think like a performance marketer.
No-code tools like Zapier or Make work for standard workflows, but they create recurring costs, hit usage limits, and break when you need something they weren’t designed to do. Custom code is yours to own and adapt.
Trying AI yourself is fine for simple tasks, but without technical and marketing context, you hit walls: debugging, managing complexity, and knowing when something is stable vs. fragile.
Security-critical systems, deep performance tuning, and complex enterprise architectures still require experienced developers and formal reviews.
Part of my role is recognizing when AI-assisted code stops being the right tool and saying “this should go to a dev team.”
No code ships without testing. We validate behavior with real data, intentional edge cases, and practical checks—not just “it ran once so we’re done.”
Every project includes human-readable comments, usage examples, and documentation of known limitations. That makes future changes easier whether it’s me, you, or an in-house team picking it up later.
AI-assisted development changes the math. Some tools that would be too small or niche for a full dev project become affordable and worth building.
You get fast prototypes, practical automation, and working integrations without the timelines or budgets of traditional development.
Custom reporting dashboards, marketing calculators, data integrations between platforms, automated workflows, and tools that process or parse data in unique ways. If it’s internal-facing, well-defined, and doesn’t require enterprise-level security or scale, it’s probably a fit.
Modular design, version control, testing with real data, and clear documentation. Once a solution is proven stable, it becomes the baseline—I don’t let AI rewrite working code just because it wants to try something new.
Yes. I can build integrations, extensions, or standalone tools that connect to your CRM, analytics, ad platforms, or internal systems. As long as there’s an API or data export, we can usually make it work.
I’ll tell you. If something needs a proper dev team, formal architecture, or security reviews, I’ll say so upfront. Part of the value is knowing when custom AI coding is the right tool—and when it isn’t.
It depends on complexity, but most tools take days to a few weeks, not months. Simple automations or dashboards can be functional within a week. More complex integrations take longer but still move faster than traditional dev cycles.
If you have a specific marketing or operational problem that existing platforms can’t quite solve, let’s explore whether custom code can help.