AI Design to Code

From Chaos to Control: How a Team Shipped 40 Screens in 6 Sprints with Niral.ai

When Design Overload Becomes a Daily Struggle

Imagine a mid-sized product team staring at 40 Figma screens with looming deadlines. Designers are tweaking margins, developers are translating those tweaks into code, and project managers are juggling timelines. Every handoff is an opportunity for mistakes. Screens get misaligned, interactions fail, and APIs don’t connect as expected. Weeks stretch into months, and frustration grows.

The stress isn’t just about missing a launch, it’s about wasted energy, repetitive work, and the creeping feeling that no matter how hard you try, scaling your app efficiently seems impossible.

The Traditional Approach: Where Teams Hit a Wall

• Before discovering Niral.ai, teams often fell into a predictable pattern. Designers finalized screens, exported assets, and handed them to developers.
• Developers translated visuals into code manually, constantly verifying alignment, responsiveness, and interaction states. APIs had to be manually integrated. Any change meant revisiting multiple screens.
• The process was labor-intensive, error-prone, and mentally exhausting.
• Even experienced teams could spend weeks just refining minor interactions, and the larger the app, the more tangled the process became.

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Enter Niral.ai: Turning Design into Actionable Code

The first time the team tried Niral.ai, skepticism was high. Could AI really bridge the gap between design and production-ready code for a 40-screen project?

The tool’s promise was simple yet powerful: import Figma designs, configure interactions, and execute clean code generation in a fraction of the usual time.

It wasn’t magic, it was a system built to handle scale without sacrificing customization.

At the heart of Niral.ai’s approach is the DICE methodology, which makes the workflow predictable and manageable:

• Design: The team finalizes UX and UI in Figma. Each screen is carefully crafted, keeping scalability and consistency in mind.
• Import: Figma screens are imported along with API definitions. No messy exports or manual reconnections.
• Configure: Designers and developers define component states, actions, and responsiveness, adjusting logic without touching every line of code.
• Execute: Niral.ai generates production-ready code that’s ready to push to Git or deploy locally.

This structured approach allowed the team to see a clear path through the 40-screen mountain.

A Sprint-by-Sprint Journey Through 40 Screens

Instead of vague promises, let’s break down how the project unfolded across six sprints:

Sprint 1: Building the Foundation

• The first sprint focused on importing designs and setting up core components.
• The team defined reusable components for headers, footers, navigation menus, and interactive buttons.
• By standardizing these elements early, they ensured consistency and saved time in subsequent sprints.

Sprints 2–4: Bringing Screens to Life

• With the foundation in place, the team tackled the bulk of the screens. Using Niral.ai’s AI-driven code generation, they were able to rapidly convert designs into functional code.
• Minor adjustments, like responsive behavior or state changes, were configured within the platform, avoiding repetitive manual coding.
• By the end of sprint 4, more than 30 screens were operational, and developers spent less time fixing alignment issues and more time fine-tuning user interactions.

Sprint 5: Connecting the Backend

• APIs were integrated seamlessly, as Niral.ai allowed pre-configured backend connections.
• Rather than manually coding API calls for each screen, the team used the tool to link front-end components with backend endpoints efficiently.
• Error rates dropped, and integration timelines shrank dramatically.

Sprint 6: Polishing and Launching

• The final sprint focused on QA, minor reiterations, and deployment preparation.
• With 40 screens already in functional code, testing became more predictable.
• Iterations involved adjusting component logic, refining interactions, and ensuring responsiveness across devices.
• By the end of sprint 6, the team had a polished, production-ready app without the usual scramble that accompanies large-scale projects.

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Real Benefits Beyond Faster Delivery

Beyond speed, the team experienced several transformative benefits:

• Consistency Across Screens: Reusable components and pre-configured logic ensured uniformity.
• Reduced Cognitive Load: Developers focused on problem-solving instead of repetitive code translation.
• Rapid Iteration: Changes in Figma automatically propagated, minimizing back-and-forth.
• Full Ownership: Teams could download the code, sync with Git, and customize freely.

One developer reflected, “Instead of getting buried in alignment fixes, I could finally concentrate on building features that mattered.” Designers echoed similar sentiments, enjoying the ability to iterate without worrying about technical handoffs.

Addressing the Skepticism: Quality and Control

• For teams wary of AI tools, Niral.ai provides transparency. Generated code is clean, editable, and fully owned by the team.
• There’s no black box; every component can be adjusted, and API integrations are visible and configurable.
• This clarity builds trust, especially for larger projects where accountability is crucial.

Lessons Learned: Making Scale Manageable

Several lessons emerged from this project:

1. Invest Time in Core Components Early: Standardizing headers, buttons, and navigation saved hours in later sprints.
2. Leverage AI for Repetitive Tasks: Manual translation of design to code is error-prone. AI accelerates this without sacrificing quality.
3. Iterate in Parallel: Configuring states and actions as screens are generated avoids a bottleneck at the end.
4. Integrate Continuously: Using Git sync and pre-configured APIs prevents last-minute chaos during deployment.

These principles make scaling from a handful of screens to 40 or more achievable within a normal sprint cadence.

Looking Ahead: How AI Will Shape Large-Scale Development

• The success of this project highlights a broader trend: AI-powered design-to-code tools are not just speed boosters; they change the way teams approach app development.
• Teams can now treat large-scale design projects as iterative, manageable tasks rather than overwhelming monoliths.
• This approach encourages experimentation, rapid iteration, and higher fidelity between design intent and functional apps.

Wrapping Up: From Overwhelm to Clarity

Shipping 40 screens in six sprints is no longer a distant dream. By adopting a structured methodology and leveraging AI intelligently, teams can reduce errors, save weeks of repetitive work, and focus on building meaningful user experiences.

Niral.ai isn’t just a tool, it’s a framework that transforms the chaotic process of scaling apps into something controllable, predictable, and even enjoyable.