Let me be clear upfront: I'm not a software developer. Never have been. My background is 28 years in enterprise software—20 years in product management and consulting. I served 1000+ companies and over 3,000+ customers at TekDog helping them optimize operations with SharePoint and Nintex. I understand business problems, workflows, what enterprises need. But writing code? That was always someone else's job. Until 2025, when I spent a year doing R&D on AI-assisted development tools so you don't have to.
How It Started
In 2024, I was working a corporate Product Owner role, dabbling with AI like everyone else—ChatGPT for emails, documentation, brainstorming. Nothing crazy. Productivity helper stuff. Then I discovered N8N and started experimenting with workflow automation using AI. Interesting. Then in December 2024, someone showed me Lovable—an AI-assisted development platform that could generate entire applications from prompts. I tried it. Impressive. Buggy, but impressive. Still felt like a toy. I put it aside and went back to my day job.
Then March 2025 happened. That's when my evenings and weekends became a one-person R&D lab.
The Moment Everything Changed
I gave Lovable another shot in March. The platform had improved dramatically in just three months. What I saw didn't feel like a toy anymore. It felt like something that could fundamentally change how software gets built. As a product leader with two decades of experience, I knew I needed to understand this technology deeply—not just play with it casually. So I started serious R&D. Nights. Weekends. Building prototypes. Internal workflow tools. Data management applications. Approval systems. Things that would help me understand the real capabilities and limitations.
I showed early prototypes to colleagues. They were skeptical.
I heard all the objections. Meanwhile, I kept researching. More prototypes. More experiments. Each one helping me understand what had actually changed and what was still hype. Then other tools started emerging: Claude Code, Replit Agent, Bolt, Cursor, Windsurf. I tried many. I needed to understand what each was good at, where it would fail, and how to work around limitations.
Here's what I discovered: when you understand the technology—what it's good at, what it isn't, and how to work within its constraints—you can turn ideas into working applications in hours instead of months.
But more importantly, I learned that emerging technologies without standards require falling back on industry best practices and adapting them for the new paradigm.
Why Best Practices Matter More Than Ever
Here's what many people get wrong about AI-assisted development: they think AI eliminates the need for proper software development practices.
That's completely backwards.
Emerging technologies have no standards yet. No established patterns. No proven methodologies. No guardrails. That's exactly when you need to fall back on industry best practices. User stories and personas are more important now than ever. They provide the context AI needs to generate relevant, useful code. A well-written user story tells AI exactly what behavior to implement and why. A detailed persona helps AI understand edge cases and UX considerations. Good requirements give AI the constraints and business rules it needs to generate production-ready code instead of generic CRUD.
AI-assisted development isn't about throwing away 20 years of software development best practices. It's about using those best practices to provide the structured context that makes AI incredibly effective.
Discovery still matters. Design still matters. Architecture still matters. Requirements still matter.
What changes is the implementation speed once you've done that foundational work properly.
The CivicXpress Comparison That Made Everything Click
At TekDog, my last major product was CivicXpress—a municipal permitting and inspections platform. Two development teams working around the clock. Eight months of development. Significant investment. Endless meetings about requirements, architecture, design, testing. During my R&D phase, I prototyped workflow modules similar to what we'd built in CivicXpress. Simple approval routing. Form submission. Status tracking. Basic reporting.
What took those teams 8+ months to design, implement, test, and deploy, I could prototype in a weekend.
Not production-ready. Not fully tested. But working well enough to validate concepts and gather feedback. The math was staggering. Not because AI replaced good development practices—but because it accelerated the implementation phase after proper planning and design. The requirements gathering still took time. The architecture design still required thought. The data modeling still needed careful consideration. But once those artifacts existed? The code generation happened at a pace that would have seemed impossible a year earlier. That's when I realized this wasn't just interesting technology.
This was something that would fundamentally change the economics of custom software development.
What Actually Makes This Work
Here's what my year of R&D taught me: AI-assisted development isn't about typing "build me an app" and getting production software. That doesn't work. That will never work.
The key is this: you still need to know what you want to build and how it should be built.
AI doesn't replace thinking. It doesn't replace architecture. It doesn't replace requirements gathering or design. What it replaces is the repetitive, pattern-based implementation work that developers have been doing manually for decades. CRUD operations follow patterns. API endpoints follow patterns. Form validation follows patterns. State management follows patterns. Database schemas follow patterns. UI components follow patterns. Give AI proper direction, break work into focused tasks, provide clear context—and yes, AI can do remarkable things.
But the difference between slop and production-ready software is understanding those patterns yourself.
I spent 28 years in enterprise software. I know what good architecture looks like. I know what data models need to support complex business processes. I know what security and compliance require. I know what makes software maintainable. When I work with AI tools, I'm not asking them to figure out the architecture. I'm providing architecture and asking them to implement following enterprise patterns. I'm not asking them to design the database. I'm giving detailed requirements about entities, relationships, constraints. I'm not asking them to guess at business logic. I'm describing workflows, edge cases, validation rules.
That's the difference. Understanding what you're building. Providing context. Breaking complex problems into focused tasks. Reviewing output critically. Testing thoroughly. All the same practices we've used for decades—just applied to a new set of tools.
The Tools I Evaluated
Since March 2025, I've done extensive R&D with Lovable, Encore/Leap, Bolt, Replit Agent, and Antigravity.
Each has strengths:
- Lovable: Rapid prototyping and full-stack applications... I built so many applications... an ungodly amount!
- Encore/Leap: Iterative development, enterprise architecture, scalability
- Bolt: Quick frontend components and UI iteration.. kind of like Lovable's little brother... step brother....
- Antigravity: Complex business logic and architectural, full stack applications.. The FUTURE is GOOGLE
- Replit Agent: Deployment and infrastructure.. rock solid clean applications, great experience.
- N8N: Agent Development and AI workflows. Pretty cool stuff for executing automations.
I don't rely on just one. I use whatever tool is best for the specific task. The tools are getting better every month. What was impossible in December 2024 was routine by March 2025. Love a tool today only to fall in love with another tomorrow... it's an addiction!
Critical insight: These tools don't replace developers. They make everyone who understands software problems exponentially more productive. A developer using these tools can do in a day what used to take a week. A product manager who understands architecture can build prototypes that used to require entire teams.
But only if they're using industry best practices to provide proper context and structure.
What I Actually Built
Over the past year of R&D: Aria (voice-first workflow automation), AriaERP experiments, internal tooling for requirements gathering, architecture design, database modeling, and countless workflow modules, prototypes, and learning projects. How many lines of code across all these research projects? According to Lovable, over one million lines. Most AI-generated. All reviewed and tested by me to understand what works and what doesn't. Is all that code in production? No—it's research. It's learning. It's prototyping.
I spent a year experimenting so I could understand this technology deeply enough to help others use it effectively.
And critically: the prototypes that did work demonstrated that AI-assisted development can produce production-grade code with proper architecture, security, and quality standards. When you know what enterprise software requires and you ensure the AI-generated code meets those standards, the results are remarkable.
When you don't, you get slop. My R&D proved which approach works.
From Corporate to Full-Time BlackProject
On January 8, 2026, my corporate role ended. I could have looked for another Product Owner position. Instead, I took everything I'd learned from a year of intensive R&D and went all-in on BlackProject. The day after I got laid off, I started building FeatureFlow—not prototypes, but the actual go-to-market product. An AI-assisted product development platform that guides teams through complete SDLC workflows. By the end of January 2026, I had something real. I had 15 years of enterprise relationships from TekDog. I had the credibility of serving 3,000+ customers. I had real prototypes, real learnings, and real conviction from spending hundreds of hours researching these tools.
Current state: We're accepting beta customer applications through February 2026. Beta program runs March-May. Market launch in June 2026.
So I went all-in. Full-time. Teaching other teams how to leverage these tools effectively while maintaining enterprise standards.
What This Means for You
If you're a CTO, VP Engineering, or Product Manager thinking "this sounds too good to be true," I understand. I was skeptical too. But I've spent a year researching these tools so you don't have to. The productivity gains are real. The cost savings are real. The speed is real.
What's not real is the hype that AI will replace developers or magically generate perfect production software with zero human involvement.
AI-assisted development is exactly what it sounds like: AI assists skilled people in building software faster. If you're skilled—if you understand architecture, data modeling, business logic, security, and quality—you can prototype and validate ideas now that would have required teams and months a year ago. If you're not skilled, AI won't magically make you skilled. It will just help you build slop faster.
The companies that figure this out now will have a massive competitive advantage.
They'll build custom software in weeks instead of months. They'll pay $50K instead of $500K. They'll iterate based on user feedback instead of being locked into year-long development cycles. They'll own their code, control their roadmap, and move at startup speed with enterprise resources. The companies that wait, that dismiss this as hype, that keep doing software development the old way? They'll be competing against teams moving 10x faster at a fraction of the cost.
That's not a winning position.
One Million Lines of R&D
I wrote over one million lines of code last year for research and development purposes. I'll write millions more this year as we build FeatureFlow and help clients leverage AI-assisted development.
And I'm still not a software developer.
I'm a product leader with 28 years of enterprise software experience who spent a year researching how to apply industry best practices to a completely new technology paradigm.
That's the future.
Not AI replacing developers or eliminating software development processes. AI empowering people who understand problems and processes to validate solutions faster. When combined with proper architecture, enterprise quality standards, and production-ready practices, the results are transformative.
The world changed in 2025. Most people just haven't realized it yet. But they will.
Think This Sounds Too Good to Be True? Let Us Prove It.
Here's my offer: Let's talk about ideas for solutions or products for your business. Give me 1 week to do discovery and research with your team, and 1 week to build. I will deliver a fully documented application with source code that your team can deploy to whatever infrastructure you prefer. Not a prototype. Not a concept. A working application with:
- Complete documentation
- Source code you own
- Architecture your team can maintain
- Deployment flexibility
This shit is real.
Want to learn how to leverage AI-assisted development for your team while maintaining enterprise standards? Want to build custom software 10x faster without sacrificing quality?
Let's talk. Because I spent a year doing the research. Now I can help you apply what I learned.