CASE STUDY
Product Codebase AI Prototyping Platform
An AI-Prototyping workflow that turns real product data, components, and codebase into high-fidelity AI-generated prototypes.
Problem Solved
The traditional process introduced a lot of distance between a product idea and something people could actually interact with.
A requirement would arrive as a ticket, specification, or user story. From there, the designer had to translate that information into screens, reconstruct missing product context, determine which components and patterns applied, and manually assemble the experience in Sketch. Only after all of that work could the team begin evaluating whether the proposed solution actually worked.
Much of this effort was necessary to prepare a design rather than to design it.
The biggest opportunity was therefore not replacing designers or automating creative decisions. It was removing the repetitive translation work that sits between a requirement and a testable experience.
The new approach aimed to shorten that gap while keeping designers firmly in control. AI could handle context gathering, initial construction, and repetitive implementation, while designers remained responsible for interaction decisions, prioritization, edge cases, usability, and overall product quality.
Problem Solving Approach
The workflow was built around a simple principle: the prototype should be generated from the product itself, not recreated from a description of the product.
Instead of asking AI to invent an interface from a blank canvas, it was given access to the same ecosystem designers and engineers already use:
– The application codebase for domain and product context
– The coded design system for components and interaction patterns
– The design environment for visual and structural guidance
– The issue tracker for requirements and acceptance criteria
– A structured knowledge layer containing reusable design rules
This allowed AI to work with current product knowledge rather than relying on fragmented documentation or manually supplied context.
The team also turned design expertise into reusable system knowledge. Component rules, layout conventions, interaction behaviors, and UX-writing principles were documented in a format AI could consistently interpret. This reduced reliance on tribal knowledge and created a repeatable baseline for generated work. At the same time, the workflow introduced technical guardrails. Prototypes were created in isolated branches and restricted to the presentation layer, keeping experimentation separate from production behavior. Before review, each experience was run in a real browser and checked across the states and conditions that matter in practice.
The result was not intended to be production code. It was a working design artifact: interactive enough to evaluate, realistic enough to communicate the intended experience, and isolated enough to experiment safely.
Outcome
The process shifted prototyping from a predominantly manual production task into a faster, iterative activity. Rather than spending most of the initial effort reconstructing screens, designers could move much sooner into reviewing behavior, testing assumptions, and making product decisions. Stakeholders could interact with a realistic experience instead of interpreting static screens, while engineers could review the implementation in the context of the actual application.
Faster route from requirement to reviewable prototype
Less designer setup effort per request
20+ feature concepts prototyped through the workflow
Key Features Implemented
AI-generated product experiences
Written requirements can be translated into interactive interfaces using existing application patterns, real product context, and established UI components.
Encoded design intelligence
Design-system rules, interaction conventions, layout principles, and UX-writing guidance are maintained as reusable machine-readable knowledge, allowing AI to apply them consistently.
Shared coded design foundation
The component library serves as common infrastructure for design, engineering, and AI. A preconfigured application shell provides the environment needed to turn generated concepts into functioning browser experiences.
Safe experimentation through isolation
Each prototype starts from the latest main branch and remains isolated from production logic and shared application state. This makes rapid exploration possible without introducing unnecessary implementation risk.
Real-browser validation
Prototypes are evaluated where users would actually experience them. Designers can verify interaction behavior, responsive layouts, loading states, empty states, errors, and dark mode before presenting the work.
Design quality checks
Compliance with established design standards is incorporated into the workflow, reducing repetitive manual checks and making quality expectations more systematic.
Living prototypes within the product stack
Because the experience is created within the same front-end ecosystem as the product, it can remain useful beyond the initial design exercise and provide a stronger bridge into eventual implementation.
Technologies
Development
Design
Project Timeline and Team Structure
The six-month engagement was led by an Atlantbh UX/UI design team, with engineering support provided as needed throughout the project.
UX/UI Designers
Methodology
Agile/Scrum with continuous, AI-assisted prototyping and validation.
With decades of experience driving success for industry leaders, we’re ready to help you turn challenges into opportunities.
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