We use AI tools to accelerate development. Not to replace developers – to make them faster.
AI handles the repetitive parts: boilerplate code, standard patterns, scaffolding. Senior developers handle the important parts: architecture, quality control, business logic, and making sure everything actually solves your problem.
The result? Faster builds without the risks of fully automated code generation.
AI isn’t magic. It’s a tool. Like any tool, it’s useful for some things and dangerous for others.
Pattern-based code generation. Standard CRUD operations, form handling, API integrations, UI components. AI can generate these quickly and accurately because they follow predictable patterns.
Scaffolding and boilerplate. Project setup, configuration files, repetitive structure. Things that take time but don’t require creative decisions.
Code suggestions. Autocomplete on steroids. AI suggests the next line, the next function, the next component based on context.
Documentation. Generating code comments, README files, API documentation from existing code.
Architecture decisions. Should this be a monolith or microservices? What database makes sense? How should components communicate? AI can’t make these calls because they depend on business context, future plans, and trade-offs that only humans understand.
Business logic. The rules that make your product unique. AI can implement logic once it’s defined, but it can’t figure out what the logic should be.
Quality judgment. Is this code maintainable? Will this scale? Is this the right approach for this specific situation? AI optimizes for working code, not good code.
Security. AI can introduce vulnerabilities it doesn’t understand. Every piece of AI-generated code needs human review for security implications.
Here’s how AI fits into our development process.
Before any AI touches the codebase, a senior developer makes the key decisions:
These decisions come from experience, not algorithms.
With the approach defined, AI helps execute faster:
This is where the time savings happen. Tasks that would take hours happen in minutes.
AI often over-engineers. It suggests complex solutions when simple ones work better. Human review catches this.
These require judgment that AI doesn’t have.
Here’s what sets our approach apart: we always choose the simplest path.
AI tools love to over-engineer. Ask an AI to build a score tracker and it’ll suggest React, a database, user authentication, and cloud hosting. Sometimes that’s right. Often it’s overkill.
Real example:
A client needed a golf score tracking app.
| AI Suggested | We Built |
|---|---|
| React frontend | Plain HTML + Alpine.js |
| PostgreSQL database | Browser localStorage |
| User authentication | None needed |
| Cloud hosting | Static file hosting |
| 30-40 hours | 8 hours |
The client didn’t need accounts or cloud storage. They needed to track scores on their phone. localStorage works perfectly for that use case.
AI’s solution was technically correct. Our solution was right for the situation.
This is why human oversight matters. AI optimizes for capability. We optimize for fit.
How much faster is AI-assisted development?
For prototypes: 60-80% faster than traditional development. A prototype that would take 2 weeks can be done in 3-5 days.
For production builds: 20-40% faster. Production code needs more review and refinement, so the gains are smaller but still significant.
Where the time goes:
| AI Suggested | We Built |
|---|---|
| React frontend | Plain HTML + Alpine.js |
| PostgreSQL database | Browser localStorage |
| User authentication | None needed |
| Cloud hosting | Static file hosting |
| 30-40 hours | 8 hours |
The shift is clear: less time on repetitive work, more time on the hard parts that actually matter.
It depends on the code and the oversight. Simple, pattern-based code? Very reliable. Complex business logic? We don’t use AI for that.
Every line of AI-generated code gets reviewed by a senior developer before it ships. If something doesn’t meet our standards, it gets rewritten.
Yes. AI tends to over-complicate things, but our review process catches this. We enforce simplicity, consistent patterns, and clean architecture regardless of whether code was written by AI or human.
The codebase you receive looks like it was written by experienced developers – because it was reviewed and refined by experienced developers.
AI can introduce vulnerabilities it doesn’t understand. That’s why security-critical code is never delegated to AI, and all AI-generated code goes through security review.
We follow the same security practices whether using AI or not. AI doesn’t change our security standards – it just changes how fast we write the non-security-critical parts.
The opposite. Using AI effectively requires more skill, not less. Junior developers don’t know enough to catch AI’s mistakes. They can’t evaluate whether a suggestion is good or over-engineered. They can’t make the architecture decisions that AI can’t make.
AI is a force multiplier. It makes skilled developers faster. It doesn’t make unskilled developers skilled.
We’ll tell you upfront which approach makes sense for your project.
Not necessarily less, but you get more. AI makes us faster, which means your budget goes further. A project that would take 6 weeks might take 4 weeks, leaving budget for additional features or more polish.
By the time code reaches you, it’s been reviewed and refined. There’s no meaningful distinction between “AI-generated” and “human-written” in the final product. It’s all been validated by experienced developers.
We use multiple tools depending on the task. The specific tools change as the technology evolves. What doesn’t change: human oversight on everything.
No. AI handles the boring parts so developers can focus on the interesting parts. The judgment, creativity, and problem-solving still come from humans. AI just makes them faster.
