Frontend teams often get bogged down with repetitive, low-value work — spinning up new pages, wiring up forms, and fixing small design inconsistencies over and over again.
By introducing Cursor, paired with Figma MCP integration and a small set of internal rules, we were able to transform that process. The results were dramatic: faster delivery, cleaner code, fewer QA cycles, and developers spending more time solving real problems instead of reworking boilerplate.
This isn’t about replacing developers. It’s about removing friction so teams can build products faster and more consistently, without cutting corners on quality or security.
The Challenge We Wanted to Solve
If you’ve ever built a complex application — a customer portal, a multi-flow dashboard, an enterprise tool — you know how much time is lost on setup and repetition.
Every new page follows a familiar checklist:
- Create the page and route it properly.
- Pull in the right components from the design system.
- Make sure every pixel matches the design mockups.
- Add validation, error handling, and submission logic.
- Double-check accessibility, spacing, and consistency before QA even gets involved.
Individually, none of these steps are hard. But together, they slow projects down — especially when deadlines are aggressive and teams are working across multiple features at once.
Automation has already transformed areas like testing and deployment. Frontend development, though, remained largely manual. That’s where we saw an opportunity for AI to make a real difference.
The Breakthrough: Rules + AI
AI tools don’t just “know” how your team works. Out of the box, they’ll make guesses — and often the wrong ones.
The turning point for us was realizing that structure is everything.
We built a lightweight rules file that acts like a shared brain for Cursor. It explains how we build our frontends, including:
- Which components map to specific use cases.
- Folder structures and naming conventions.
- Spacing, layout, and accessibility requirements.
- Patterns for validation and API calls.
With that context in place, Alexey Shulga, one of our frontend engineers, can prompt Cursor with something as simple as:
“Create a Preferences page with company selector, description, and Submit.”
And within moments, Cursor generates:
- A fully scaffolded page using our layout system.
- Proper routing and navigation updates.
- Form wiring with schema validation and error handling.
- A clean submit handler tied to the right endpoint.
The output is ready for review immediately — no mystery styles, no one-off components, no “we’ll fix it later” cleanup.
For developers, this means far less time spent on grunt work.
For leaders, it means faster timelines and fewer surprises late in the release cycle.
From Pixels to Production: Why Figma MCP Matters
Before Figma MCP, our workflow relied on screenshots or manual inspection of designs. That meant a lot of back-and-forth — QA catching spacing issues, mismatched components, and minor inconsistencies that added up to major delays.
Now, Cursor doesn’t just look at designs — it reads the actual design data.
Figma MCP gives Cursor direct access to:
- The exact component hierarchy.
- Sizes, tokens, and typography rules.
- The structure behind every layout.
When Cursor generates code, it matches the design system perfectly from the start. Designers stay in their flow, developers focus on logic and performance, and QA isn’t stuck chasing down pixel errors.
This one change alone has cut weeks out of the design-to-development cycle.
Keeping Quality Front and Center
AI-generated code isn’t something you just take and ship. It has to be part of the process, not a shortcut.
Here’s how we manage it:
- Small, testable outputs – We generate utilities, components, and page scaffolds in focused pieces.
- Code reviews as usual – AI code goes through the same review process as human-written code.
- Continuous improvement – Every time we refine something, we add it to the rules file so future generations are even cleaner.
This creates a feedback loop. The more the system learns, the more predictable the output becomes, and the less cleanup is needed over time.
The goal isn’t to trust the AI blindly — it’s to collaborate with it effectively.
Building Trust With Security and Privacy
Moving faster doesn’t mean taking risks with client data or intellectual property.
Our approach to AI is built on strict security practices:
- Figma tokens are always read-only.
- No sensitive data ever goes into prompts or public tools.
- Clients approve every AI-driven workflow before it’s used.
- Logs and audit trails track every automated change.
AI is a tool, but governance is non-negotiable.
The Results We’ve Seen
Once we fully integrated Cursor and Figma MCP, the difference was obvious:
- 20–60% faster delivery of repeatable UI tasks like forms, pages, and layouts.
- Fewer QA cycles spent fixing spacing and alignment issues.
- Developers focusing on flows, performance, and architecture instead of repetitive work.
- Cleaner, more consistent codebases that scale smoothly over time.
- Teams hitting deadlines without last-minute crunch.
These aren’t abstract metrics. They translate directly into faster releases, lower costs, and better team morale.
Why This Matters for Your Team
For engineering leaders — CTOs, VPs, product owners — this approach means projects stay on schedule, budgets stay predictable, and releases are cleaner from day one.
For senior developers, it means fewer repetitive tasks and more time spent solving interesting problems.
And for QA, it means higher-quality code landing in testing earlier, with fewer surprises.
The end result is a team that delivers faster, calmer, and with higher confidence.
Taking the First Step
This isn’t about adopting AI because it’s trendy. It’s about identifying specific, repeatable pain points and applying AI carefully to eliminate them.
At Forte Group, we help teams implement this workflow step by step:
- Setting up Cursor and Figma MCP with the right structure and security.
- Building modern frontends with React and Next.js.
- Connecting design, development, and QA into a seamless pipeline.
If you’re ready to see what this could look like for your team, let’s start a conversation.
