How we use AI in our process How We Use AI in
Our Process
We integrate various AI tools into our engineering and design processes to optimize repetitive tasks, improve workflows efficiency, and speed up testing cycles. At the same time, every decision regarding prompts, results, and implementation remains under the full control of our team.
In what areas AI
bring the most value
Development
AI handles standard tasks and debugging, allowing developers to focus on core architecture and solving complex problems. Our engineering workflow integrates AI to handle standard tasks and debugging, allowing developers to focus on core architecture and complex problem-solving. By prioritizing strong architectural foundations over pure automation, we ensure our systems remain stable, scalable, and maintainable for the long term.
Design
AI optimizes layouts and workflows, while designers maintain control over strategy and system scalability. It enables design teams to move from early concepts to interactive prototypes with significantly reduced iteration cycles.
Prototyping
AI enables the rapid creation of interactive prototypes to validate concepts and gather feedback before a single line of code is written. By building coherent systems rather than isolated visuals, we ensure every prototype aligns perfectly with the final technical architecture.
AI Under Our Control
We use AI in a controlled environment to optimize our workflows and save time, while keeping the codebase reliable, sustainable, and scalable. At the same time, all the core business logic is developed by our team.
It’s especially important to pay close attention to complex business logic, system architecture, and code patterns; and, from the design side, to behaviour patterns as well to make sure the product actually works in real life.
AI in Engineering
Architectural Ownership
While AI can significantly streamline development, our engineers maintain full authority over the system’s architectural foundation. We ensure our team and not external tools, defines all business logic, state management, and structural rules. By keeping manual control over every module, we prevent automated code from dictating our system’s design.
This disciplined approach guarantees that every application remains predictable, transparent, and scalable for the future.
Cloud as an Engineering Support
Cloud-based AI tools are actively used in our daily workflow, but always on top of an architecture already built by our team. Once the project structure, component system, and design foundations are properly defined, generating new interface elements such as forms, dialogs, or pages becomes significantly faster.
We use clear rules and reusable patterns to let AI tools rapidly generate solutions that strictly follow our architecture and standards, keeping us in total control of the system.
Controlled Speed and Stability
When cloud tools are used within an established system, development becomes faster and more efficient while maintaining the stability of the technical foundation. The engineering team still controls the architecture, and AI helps speed up repetitive tasks.
However, if cloud automation is the primary foundation, control over development decreases. Systems become dependent on external factors, structure can be disrupted, and long-term scalability becomes more difficult to manage.
To create resilient products, architecture must be controlled internally, and AI should support it rather than replace it.
AI in Design
Interface and Interactive Prototypes
AI is often used to generate initial concepts, brainstorm ideas, and create interactive prototypes. It helps streamline the process and makes it easier to share and test ideas quickly.
Tools such as Figma Make, Magic Path, Magic Patterns, V0, and Banani can generate early interface structures and interaction flows, providing designers with a starting point for further refinement. While these tools can speed up the initial phase, the generated layouts and interactions often require significant restructuring into a structured, production-ready product system.
AI for Design Workflow Optimization
Built-in Figma AI features assist with micro-tasks that improve the efficiency of everyday design work. These tools help automate layout adjustments, support component organization, and simplify repetitive actions within design systems.
Although these features do not replace the design process, they significantly reduce time spent on routine operations and allow designers to focus on product thinking and user experience.
Visual Content Generation
Platforms such as Recraft and Sora AI are used to generate visual assets in both vector and raster formats. These tools support the creation of custom visual content that enhances product presentations, prototypes, and marketing materials.
When used as part of a structured design workflow, AI-generated visuals provide additional flexibility without affecting the consistency of the design system.
Ready to boost your Product?
AI streamlines product development and testing within a structured engineering control.
AI-tools use case
How We Delivered a WMS Demo in 3 Weeks
A client needed to present a working product demo to investors within 3 weeks for a complex warehouse management system. Instead of building a full frontend, we designed the interface in Figma, created an interactive prototype in Lovable, and hosted it on a custom URL. The prototype allowed the client to demonstrate the concept to investors and gather market feedback.
Technology
Our team uses a range of tools to support AI-assisted development and design workflows from interface design and design systems to logic setup, and early product experimentation.
Check what our clients say about working with UITOP
Cole Weiler
CEO, BoltWise“They took way more responsibility than we asked and we were so happy with the results of all their work. Uitop was easy to work with, flexible, and valuable to our company.”
Neil Hosey
ResHub, CTO
Dylan Gambardella
Founder, Different Health“They took extra time to ensure that our frontend developer could easily implement the wireframes.”
Sam Jordan
TrovBase, CEO
Dirk Pauw
CEO, Tavas“We’re most impressed with Uitop’s ability to come up with a system to be able to bring design solutions.”
Jordan Girard
Whiterock, Founder
Jake Heimark
Costa Security, CEO"We are happy with the improved UI/UX, which has made product navigation cleaner, simpler, and more intuitive. The team is great and always there to help."
Michael Mosseri
Total Brokerage, CEO
Nicolas Endress
ClimEase, CEOFrequently asked questions
01/ Who owns the code generated with AI?
All project deliverables belong to the client. AI-generated code is produced within our architectural framework and development standards, ensuring that the final system remains transparent, maintainable, and fully transferable.
02/ How do you ensure data security when using cloud AI tools?
We use AI tools in controlled environments with strict security protocols. Sensitive data is protected through governance processes and secure infrastructure.
03/ What is the difference between an AI prototype and a production product?
AI prototypes validate ideas and gather feedback, while production products include full backend architecture and optimization for real-world deployment.
04/ What tools do you use?
We use a variety of tools, including Raycast, oh-my-zsh (with plugins), asdf, tmux, codex, vscode, neovim, eza, atuin, Postman, Docker Desktop, kitty (terminal), zsh-aliases, Cursor, Regular ChatGPT, DataGrip, Arc Browser, PgAdmin, DBeaver, GitHub Copilot, GitHub Desktop, Swagger, Neo4j Desktop, Apify, PhantomBuster, and RapidApi.
05/ Can AI generate a complex product?
No. AI can be used for quick solutions or prototypes, but for a fully functional and complex product, it’s not suitable. Artificial intelligence helps speed up certain processes, but the actual product requires a full development cycle, including in-depth work on architecture, performance optimisation, and ongoing maintenance.