Let's start with the elephant in the room: yes, we use AI tools in development, and it's made us significantly more productive. But probably not in the way you're thinking. We're not pressing a button and getting perfect websites. We're still writing code, reviewing everything, testing thoroughly, and taking full responsibility for what ships. AI just helps us do all of that faster and with fewer bugs.
The question isn't whether to use AI in development — it's how to use it responsibly. When clients ask us about AI, they usually want to know two things: Are we cutting corners? And are we replacing human expertise with machines? The short answer to both is no. The longer answer is what this post is about.
What Actually Works (and What Doesn't)
After a year of integrating AI into our development workflow, here's what we've learned:
GitHub Copilot is our daily workhorse. It's not writing entire features for us, but it's incredible at the repetitive stuff — boilerplate code, type definitions, test scaffolding, basic CRUD operations. What used to take 20 minutes of typing now takes 5 minutes of reviewing and refining suggestions. It's like having a very fast junior developer who never gets tired of writing the boring parts.
Claude (that's what I'm using to write this post, actually) excels at code review, documentation, and explaining complex logic. When I need to understand a messy piece of code someone else wrote, or when I'm documenting a complex API, Claude helps me think through the structure and catch edge cases I might have missed. It's particularly good at spotting potential security issues and suggesting more robust error handling.
Cursor has changed how we approach refactoring. Its ability to understand context across multiple files means we can modernize codebases much more confidently. Instead of spending hours tracing dependencies, we can quickly see how changing one function affects the rest of the system.
What doesn't work: Asking AI to architect entire applications, handle sensitive data processing, or make business logic decisions. AI is terrible at understanding business requirements, user experience trade-offs, and performance implications at scale. It also can't replace the human judgment needed for code reviews, security audits, and client communication.
How This Changes Our Daily Workflow
Here's a typical day in our development process, AI-assisted:
Morning: I start by reviewing yesterday's work with Claude. I paste the code I wrote and ask it to spot potential issues, suggest optimizations, and help me write clear documentation. This catches bugs before they hit testing and ensures our code is maintainable.
Development: Copilot helps with the mechanical parts — writing React components, TypeScript interfaces, test files. I focus on the logic, the architecture, and the business requirements. The AI handles the repetitive syntax.
Code Review: When reviewing a teammate's work, I use Claude to help analyze complex functions and suggest improvements. But the final decision on what gets merged is always human — AI suggestions are just another input in our decision-making process.
Documentation: AI is fantastic at taking technical implementation details and turning them into clear explanations for clients or future developers. We still review everything, but it cuts documentation time in half.
Addressing Client Concerns
We get three main questions from clients about AI in development:
"Are you just copy-pasting AI-generated code without understanding it?" No. Every AI suggestion gets reviewed, tested, and often modified before it ships. We treat AI suggestions the same way we'd treat code from a junior developer — helpful starting point, but it needs oversight. We understand everything we deploy because we're responsible for maintaining it.
"What about security and intellectual property?" We don't send sensitive business logic, proprietary algorithms, or client data to AI services. AI helps with generic code patterns, not specific business implementations. For additional security, we use models that don't train on our code when possible.
"Is AI-generated code as good as human-written code?" Sometimes better, often worse, usually different. AI is excellent at writing clean, well-structured boilerplate. It's terrible at understanding your specific business context. The combination of AI efficiency and human judgment tends to produce higher quality code than either alone.
Real Examples from Our Projects
E-commerce Platform (React + Node.js): AI helped us scaffold 80% of the CRUD operations for product management, but we wrote all the payment processing, inventory logic, and security middleware ourselves. Result: 30% faster delivery with the same level of security and reliability.
Marketing Agency Website (Next.js): Cursor helped us migrate from an older React version to Next.js 15, automatically updating component patterns and fixing compatibility issues. What would have been a two-week project took five days, with fewer bugs than a manual migration.
SaaS Dashboard (TypeScript + PostgreSQL): Claude helped us design a comprehensive test suite by analyzing our application logic and suggesting edge cases we hadn't considered. We ended up with better test coverage than we typically achieve manually.
Documentation Project: We used AI to generate initial API documentation from our TypeScript types, then reviewed and refined it. The client got better documentation, delivered faster, at no extra cost.
The Business Impact
For clients, AI-assisted development means:
- Faster delivery without sacrificing quality
- Better documentation because generating it is no longer a time sink
- More thorough testing because AI helps us think through edge cases
- Lower costs for routine development tasks
- More time for us to focus on the complex, business-critical parts of your project
We're not charging less for AI-assisted work — we're delivering more value for the same investment. You get the same expertise, the same quality standards, and the same personal service, but with better efficiency and fewer bugs.
Where We Draw the Line
There are things we'll never outsource to AI:
- Architecture decisions — choosing technologies, designing system structure, planning scalability
- User experience — understanding your users, designing interfaces, making usability decisions
- Business logic — implementing your specific rules, processes, and workflows
- Client communication — understanding requirements, providing updates, making recommendations
- Security reviews — auditing code for vulnerabilities, implementing security measures
- Quality assurance — final testing, performance optimization, production deployment
These require human judgment, business understanding, and accountability. AI is a tool in our toolkit, not a replacement for expertise.
The Future of AI-Assisted Development
We're still early in this transformation. The tools are getting better rapidly, and we're constantly refining how we use them. What we're confident about is that AI makes good developers better — it doesn't replace them. Teams that embrace AI responsibly will deliver better results than teams that don't, and they'll have a competitive advantage in both quality and speed.
If you're considering a development partner, here's what you should ask:
- How do they use AI tools in their workflow?
- What's their review process for AI-generated code?
- How do they handle security and IP concerns?
- What stays human-only in their process?
The best answer isn't "we don't use AI" or "AI does everything." It's "we use AI to be more efficient at the parts that don't require human judgment, so we can spend more time on the parts that do."
That's exactly how we work. AI handles the routine, we handle the complex, and you get the best of both. If you'd like to see how this approach works for your project, let's talk.
This post was written collaboratively between human expertise and AI assistance — the ideas, structure, and business insights are human, while AI helped with drafting, editing, and ensuring clarity. Which is exactly how we approach development: the right tool for the right job.