Ship real software faster.
It's how I work.
Move at AI speed.
TXFRM helps founders and small teams turn ideas, prototypes, and existing products into working software in weeks, not months.
Build with engineering judgment.
Fifteen-plus years of engineering experience guide what gets built, how it fits together, and whether it will hold up in the real world. AI accelerates the implementation.
From first product slice to modernization, AI integration, and technical leadership.
How TXFRM delivers
AI has dramatically shortened the time required to build software. The real opportunity is to reach meaningful product outcomes faster.
AI-Native Delivery
AI compresses the path to working software
I use AI throughout implementation, testing, review, and documentation. That removes much of the repetitive production work and creates shorter loops between deciding, building, testing, and learning.
Engineering Judgment
Speed still needs direction
The hard part now is understanding the product, shaping the system, evaluating tradeoffs, and making decisions that balance immediate value with security, maintainability, and future needs.
Working Outcomes
Every engagement should create tangible progress
That may be a deployed product slice, a functional AI workflow, a stabilized codebase, a production-ready foundation, or a technical direction supported by real implementation.
The default is not a deck about what could be built. The default is to build.
Any of these look familiar?
Moving quickly with AI can get a product surprisingly far—until prompting harder stops producing meaningful progress. That is usually the point where experienced engineering judgment matters most.
We vibe-coded ourselves into a corner.
The product works, but each new feature creates unexpected problems. The architecture emerged one prompt at a time, and now it needs a clear model for how the system fits together.
The product works, but the system is getting hard to trust or change.
Performance problems, fragile architecture, and accumulated AI-generated code are making each new change riskier—and prompting harder is not fixing it.
The demo works, but we aren’t sure it is ready for real users.
A convincing prototype may still lack the security, monitoring, error handling, and operational foundations a production product depends on.
Every agent recommends a different architecture.
AI is very good at generating plausible options. Choosing among them depends on business priorities, existing constraints, and acceptable risk—a judgment call, not a prompt.
We built a lot quickly, but now we don’t know what to do next.
Rapid development can produce more features than product clarity. The next step is often simplifying, testing the riskiest assumption, or preparing what already exists for customers.
If any of these sound familiar, the engagement options below are built for exactly these situations.
Ways to Work Together
Every engagement is shaped around the outcome you need. These are common starting points—and smaller initial engagements are welcome.
Product Sprint
1–2 weeks
Go from an idea, rough prototype, or unresolved product problem to something concrete—rapid clarity through implementation, not weeks of abstract discovery.
Good fit for
- Validating a product direction
- Building the first usable slice of an MVP
- Creating an investor or customer demo
- Evaluating or stabilizing an AI-generated codebase
- Resolving an important architectural decision
Typical deliverables
- Working software or a deployable product slice
- An interactive demo or functional AI workflow
- Source code and documentation
- A focused roadmap for the next milestone
Outcome: Something real you can use, test, show, or continue building on.
Product Build
2–6 weeks
Build and launch a meaningful product milestone without assembling a full development team. We define a practical scope together, and I deliver working software in short loops, adjusting as the product becomes real.
Good fit for
- Prototype to usable V1
- AI feature integration
- Rescuing or extending an existing codebase
- Backend or platform modernization
- Preparing a product for real users or scale
What the engagement includes
- Product and technical scoping
- Hands-on implementation
- Testing and technical review
- Deployment and launch support
- Documentation and next-step planning
Outcome: A working, execution-ready product milestone—not merely a plan for one.
Fractional CTO
Ongoing
Senior technical leadership without the cost or commitment of a full-time executive hire. I work with your team one or two days per week to guide technical direction, improve delivery, and mentor developers.
Good fit for
- Founders who need a trusted technical counterpart
- Teams without consistent senior engineering leadership
- Products outgrowing their early architecture
- Organizations adopting AI-assisted development
What the engagement includes
- Architecture and technical strategy
- Direct contribution where needed
- Engineering workflow improvement
- Team mentorship
- Hiring and vendor guidance
Outcome: Continuous technical clarity, stronger execution, and senior judgment scaled to the needs of the team.
Senior engineering judgment. AI-native delivery.
Gabe Blair
Owner of TXFRM, your experienced product and engineering partner.
I've spent more than 15 years building, improving, and rescuing software across agencies, startups, product teams, and enterprise organizations.
I work best as the single accountable owner of delivery: shaping the product, defining the architecture, writing the code, and operating what ships. Clients get one person who combines product, technical, and operational judgment—and who stays responsible for the result.
Software Engineering in the Age of AI.
AI can now write, review, test, and audit large amounts of code, and I use those capabilities every day. What it cannot do is decide what is worth building, which risks matter, or when a shortcut becomes a trap.
AI accelerates implementation. Experience determines direction.
What experience still decides
Architecture and system boundaries
I define the responsibilities, data flows, and interfaces between components before accidental dependencies harden. Complexity gets contained where it genuinely belongs, so individual parts can evolve without every change rippling through the product.
Product scope and prioritization
I identify the outcome the product needs to create, the assumptions that remain unproven, and the smallest piece of working software that produces useful evidence—separating immediate requirements from speculative future needs.
Security, reliability, and production readiness
I focus on the risks that are material to the product’s current stage: sensitive data, authorization, compliance obligations, operational failure points, and decisions that would be expensive to reverse.
Code quality and maintainability
I evaluate whether the system has clear concepts, sensible boundaries, useful tests, and observable behavior—so future changes stay safe and comprehensible for both people and AI agents.
Tradeoffs and accountability
Every product takes shortcuts. I make the compromises deliberate, distinguish the ones that preserve options from the ones that quietly create expensive constraints, and stay accountable for what ships.
Ready to make real progress?
Bring me the idea, the prototype, the codebase, or the problem that is slowing you down.
We'll identify the highest-leverage next step and start turning it into working software.