How Context-Aware AI Turns Figma Designs into Production-Ready Code🚀

Most Figma-to-code tools do what they promise, they turn your designs into working components.

But those tools have a blind spot, and that is your existing codebase.

They create new Button components even though you already use shadcn/ui. They hardcode #3B82F6 instead of using your design tokens. They build form validation that doesn't match the pattern you use everywhere else.

This isn't about code quality, it's about integration(how the code fits in your codebase). Many tools act like every project is brand new, even if you already have component libraries, styling systems, and architectural patterns.

In this article, I'll break down why Figma-to-React tools struggle with real codebases, then show how context-aware tools like Kombai approach this differently through workspace understanding and specialized tooling for frontend tasks.

To start, let’s look at where these tools actually fall short.

The Real Problem: Generic Components in Real Codebases

Most Figma-to-code tools don't see what you've already built.

When a tool creates a form component without knowing your project uses React Hook Form and Zod, it writes custom validation logic. When it generates UI without checking your dependencies, you get duplicate components. The generated code works in isolation, it just doesn't work with your existing architecture.

This isn't just annoying. It's technical debt. Research shows that approximately 40% of developers spend 2-5 working days per month on debugging and refactoring caused by technical debt. Code duplication, which generic tools constantly create. It’s then linked to higher defect rates and increased maintenance costs.

The cost compounds over time. Duplicated components aren't just harder to maintain, they're expensive. Each cloned block multiplies bugs across your codebase. Testing becomes more complex. Updates require changing code in multiple places instead of one.

So what does “good” actually look like? Production-ready React components do three things:

1. Use your existing component library instead of creating new primitives,
2. Follow your established patterns for state management and validation, and
3. Match your styling approach, whether that's Tailwind with tokens or CSS modules.

The difference between working code and production code is integration. Most tools don’t solve for that, but Kombai, which is built specifically for frontend development, does.

How Kombai Approaches This Differently

Kombai scans your workspace before generating anything.

It doesn't just read your Figma design. It indexes your project to understand what components you already have, what styling system you use, and what architectural patterns you follow. Then it generates code that extends your existing setup instead of creating isolated components.

This workspace-first approach is backed by data. In Kombai's internal testing across 200+ real-world frontend tasks, their context-aware generation achieved 85% code compilation success compared to 60-70% for SOTA models without workspace context. The difference comes from understanding what already exists in your codebase.

It reads your configuration files, too. For Tailwind projects, it parses tailwind.config.js to identify color tokens, spacing scales, and breakpoints. Generated code uses bg-primary-500 instead of #3B82F6.

This is different from embedding-based approaches that general-purpose agents use. Those find similar code snippets but miss the functional context. Kombai understands how your components actually work.

I wanted to see this in practice. So I tested Kombai against other tools on the same project.

The Test: Same Form Component, Two Approaches

I set out to see what actually happens when you use Figma-to-code tools on a real project with an existing tech stack.

What I Built

I used a retail store management dashboard. GitHub

The codebase already had established patterns, specific component structure, styling conventions, and architectural decisions.

Then I designed two store management modals in Figma. One for creating new stores (with logo upload, form fields, password input), one for viewing existing store details in read-only mode. Both used dark theme styling with consistent spacing and icons.

The test: Give the same Figma design to four tools and see which actually builds a good UI and integrates the code into the existing codebase by following the established patterns and structure.

Tools tested:

• Builder.io
• Anima
• Figma Remote MCP (via GitHub Copilot with Sonnet 4.5 in VS Code)
• Kombai

Generic Tools: What Actually Happened

I tested Builder.io, Anima, Figma MCP (via GitHub Copilot with Sonnet 4.5 in VS Code), and Kombai. Here are the results.

Builder.io generated code in 1 minute 47 seconds. But the modals were oversized and didn't match the viewport. There were two close icons instead of one. The responsive behavior broke on smaller screens.

Anima had more problems, misaligned elements, a broken layout, and visual inconsistencies.

Figma MCP (with GitHub Copilot) completed in 1 minute 23 seconds, the fastest of all. It scanned project files through Copilot's workspace access, which seemed promising. But the output had TypeScript errors that required manual fixes. The view modal didn't populate data correctly. I had to debug and fix the data binding. Even after fixes, max-height issues persisted in the layout.

Builder.io and Figma MCP both scanned the project. Builder.io asked for codebase context, while Figma MCP accessed files through Copilot. But scanning doesn't guarantee the code fits your existing setup. Both generated UI that didn't match the exact design and required manual fixes.

Anima generated code in isolation that needed complete refactoring to fit the existing architecture.

Kombai: What It Generated

Kombai started differently. It spent 45 seconds scanning the workspace before generating anything.

During that scan, it identified the tech stack. It reads the existing component patterns and styling approach.

Then it generated both modals in 2 minutes and 40 seconds. The code used the Onest font already loaded in the project. It imported Lucide icons that matched the existing UI. It followed the same dark theme pattern (#0c0c0f) used elsewhere in the app.

The modals rendered correctly without size issues or visual glitches. Kombai also updated the stores table component to integrate the view action with the existing UI flow.

The difference wasn't speed. Figma MCP was faster. The difference was how the code fit. Kombai's code fit into the existing project without requiring refactoring.

The Numbers That Actually Matter

Here's what the test showed:

**Note:* Refactoring time includes fixing layout issues, resolving errors, replacing custom components with existing ones, and adjusting code to match project architecture*.

Even with Kombai, I spent about 2 minutes reviewing code and adjusting minor spacing. No tool is perfect, but the integration depth is different.

What This Means Over Time

The test showed one component. But most teams aren't converting just one design. Let's look at what happens when this becomes part of your regular workflow.

Say you're converting 3 Figma designs per week. Each one needs the same manual work: making it fit with your existing components, fixing layout issues, and debugging errors. The refactoring times from the test were Anima (30 min), Figma MCP (15 min), Builder.io (10 min), and Kombai (2 min).

Here's how that adds up over a month:

Note: Weekly = Per component × 3 components. Monthly = Weekly × 4 weeks. Times based on actual refactoring work from the test above .

The difference isn't just time. It's what you're doing.

With generic tools, those 2-6 hours are spent fixing architectural mismatches. Replacing components, fixing imports, debugging layout issues, and converting styling approaches. You're refactoring generated code to match what you already built.

With Kombai, those 24 minutes are spent on code review, checking logic, adjusting spacing, and verifying patterns. You're doing what you'd do with any code, human-written or AI-generated.

One feels like debugging. The other feels like development.

What I Actually Use Now

After running this test, I use Kombai for frontend work.

Builder.io and Figma MCP both scan workspaces and generate fast. For teams that need visual editing after generation or integration with CMS workflows, Builder.io has features Kombai doesn't. For greenfield projects or quick prototypes, they work. But on established codebases with existing components and architectural patterns, how code fits your setup matters more than speed.

The test showed it. 2 minutes of review with Kombai vs. 10-30 minutes of refactoring with other tools. Over a month of converting 3 designs per week, that's 24 minutes vs. 2-6 hours.

Kombai isn't perfect. I still adjusted spacing and reviewed logic. But the work feels different. With generic tools, I'm fixing compatibility issues. With Kombai, I'm reviewing code like I would from any developer.

If you're working on existing projects with component libraries and established patterns, use a tool that understands those patterns. For me, that's Kombai.

Conclusion

The question isn't whether AI will generate your frontend code. It already is. The question is whether that generated code creates more work than it saves.

Generic tools are getting better at workspace scanning. But understanding file structure isn't the same as understanding architecture. Tools that truly extend your patterns, not just read them, are the difference between code you can merge and code you need to rewrite.

Test it yourself, Kombai offers free credits to start. See how it handles your existing codebase at kombai.com.