Let's be direct: we're an AI-centric development team. We use AI tools every single day — and we believe that makes us better at serving our clients.
Some firms are quiet about this, worried clients might think they're getting less value. We think the opposite — AI makes us deliver more value, faster, at lower cost.
But transparency matters. You deserve to know exactly what tools we use and how we use them.
Our core AI toolkit
We keep our AI stack focused and practical. Here are the tools we actually use:
Claude Code — Our primary tool
Claude Code is our workhorse. It's what we reach for first, and what we use most throughout the day.
What we use it for:
- Writing and refactoring code
- Building and running unit tests
- Code review and debugging
- Generating documentation from code
- Explaining complex codebases
- Problem-solving and architecture discussions
Claude Code understands context exceptionally well. We can feed it our codebase, explain the business requirements, and get code that actually fits the project — not generic snippets we have to heavily modify.
ChatGPT
ChatGPT serves as our research and exploration companion.
What we use it for:
- Investigating new technologies and frameworks
- Comparing options with pros/cons analysis
- Quick technical questions and explanations
- Brainstorming approaches to complex problems
- Drafting documentation and user guides
Codex
We use Codex for quick code completions and suggestions as we type.
What we use it for:
- Real-time code suggestions while writing
- Boilerplate generation
- Quick syntax help across languages
Our philosophy: AI as accelerator, not replacement
AI doesn't replace the work that matters. It accelerates the work that shouldn't take as long as it used to.
Think of it like power tools vs. hand tools. A skilled carpenter with power tools isn't less skilled — they're more productive. They can do more work, to the same standard, in less time.
The judgment, the craft, the accountability — that's still human. The repetitive, mechanical parts? Those can be faster.
What AI can't do (and we don't pretend it can)
AI has real limitations. Here's what still requires human expertise:
Problem diagnosis
What's needed: Understanding your business, asking the right questions, identifying root causes that aren't obvious.
Why AI can't do it: AI can answer questions, but it can't ask the right questions. Diagnosis requires understanding context that doesn't fit in a prompt.
Architecture decisions
What's needed: Choosing the right structure for a system that will evolve over years, balancing trade-offs that aren't always quantifiable.
Why AI can't do it: AI can suggest patterns, but it doesn't understand your business trajectory, your team's capabilities, or the non-obvious constraints that matter.
Business judgment
What's needed: Deciding what to build, what order to build it in, what trade-offs to accept.
Why AI can't do it: These decisions require understanding business value, stakeholder priorities, and strategic context that goes far beyond code.
Accountability
What's needed: Standing behind the work. Owning the outcome. Being responsible when things go wrong.
Why AI can't do it: AI has no stake in whether your project succeeds. It doesn't care. We do.
When a system goes down at midnight, AI isn't answering the phone. We are.
The quality assurance layer
Every piece of AI-generated content goes through human review. Always. No exceptions.
Here's the workflow:
- AI generates a first draft (code, documentation, whatever)
- Human reviews for accuracy, appropriateness, and quality
- Human refines based on context AI doesn't have
- Human approves before anything ships
The AI never has the final word. It's a tool we use, not a colleague who makes decisions.
What we check for:
- Correctness: Does this actually work? Does it do what it's supposed to?
- Security: Has AI introduced vulnerabilities? (This happens more than you'd think)
- Appropriateness: Is this the right approach for this specific situation?
- Integration: Does this fit properly with the rest of the system?
- Edge cases: Has AI missed scenarios that humans would catch?
Why this benefits you
Some clients worry that AI involvement means less value. The opposite is true:
Faster delivery
Tasks that took hours now take minutes. We can iterate faster, explore more options, and deliver sooner.
Benefit to you: Projects complete faster. You get value sooner.
Lower cost
We don't bill for the time AI saves. If AI helps us write something in 30 minutes that would have taken 2 hours, you pay for 30 minutes.
Benefit to you: Same quality, less cost.
Better coverage
AI helps us be more thorough. More test cases. Better documentation. Fewer gaps.
Benefit to you: Higher quality, fewer surprises after launch.
More focus on what matters
With AI handling mechanical tasks, our humans spend more time on the hard problems — diagnosis, architecture, judgment.
Benefit to you: Better solutions, not just faster code.
The firms that don't use AI aren't delivering better work. They're delivering the same work slower and more expensively. We'd rather pass the efficiency gains to you.
What this doesn't mean
To be clear about what we're not doing:
We're not outsourcing to AI. AI is a tool we use, like an IDE or a testing framework. The work is still done by our team.
We're not reducing expertise. Our team still needs to be skilled enough to review, correct, and improve AI output. Bad developers with AI produce bad code faster.
We're not compromising quality. Every deliverable meets the same standard it would without AI — we're just getting there more efficiently.
We're not hiding anything. We tell clients how we work. This transparency is intentional.
The bottom line
We use AI because it makes us better at serving you. Faster delivery, lower cost, higher quality, more thorough coverage.
The things that matter — judgment, accountability, expertise — are still human. AI just helps us spend more time on those things and less time on mechanical tasks.
That's a good trade for everyone.
Entvas Editorial Team
Helping businesses make informed decisions
