Building a Scalable and Fast-Performing Frontend for ArthurAI
Next.JS
frontend development
Performance Optimization
ArthurAI had a great product but faced some serious frontend challenges in maintaining their growth and had no resources to do it alone. We had the concept of handling it so they could take it off their shoulders and focus on developing the platform.
What was the result of this complementation?
Let’s find out.
USA
AI
Frontend Consulting & Development
1 year, ongoing
About ArthurAI
ArthurAI, in their own words, is an AI platform that simplifies the deployment, monitoring, and management of traditional and generative AI models. All of this ensuring scalability, security, compliance, and efficient enterprise use.
Their key products are:
- Arthur Platform: a centralized control plane that allows users to quickly, easily and consistently analyze and monitor Generative AI and Traditional ML models
- Arthur Engine: an open-source distributed evaluation engine that can be deployed on any infrastructure and is highly customizable to allow for users to generate metrics and enforce guardrails for their AI applications
To learn more about the Arthur Platform, Arthur Engine, and their other services, please check out their website. You can also learn more about their open-source Arthur Engine on Github.
Take Care of the Frontend and Maximize Its Potential
ArthurAI has been scaling up fast, gaining new customers for its AI-fueled solutions. However, the catch is that these solutions were developed as separate applications. At first, it was helpful. The company could have released new features, maintaining its growth. But, as time passed, the fragmented frontend of the tools became a challenge.
Each tool had a distinct codebase, requiring multiple logins. As the platform scaled, its usability became inefficient. Separate codebases also led to inconsistencies in design and performance.
Long story short, ArthurAI needed a trusted partner to handle the front end so they could focus on developing the platform. As they lacked these resources on their own, they reached out to us.
That’s how we met halfway.
Unify, Simplify, Lead the Process, And Strive for Top-Quality Outcomes
When stepped into the project, we had to face the following challenges:
- Fragmented frontend.
- Differences in data visualization.
- Performance and scalability issues.

How did we solve them?
Implementing a Consistent Frontend with UX In Mind
As mentioned above, we were not only responsible for developing new screens to make ArthurAI’s platform visually consistent. Our responsibility was to take care of all the frontend area, working as a team of consultants and advisors with coding skills.
Thanks to that, the ArthurAI team could move their focus on researching and developing new features and business possibilities for their platform, and we put effort into making it easy to use.
So, what have we done?
Merging separated codebases into one repository
Usability is like a holy grail for digital products like ArthurAI. So, merging all the separated products into a single, integrated repository was necessary.
Thanks to that, we eliminated the need for multiple logins and built a foundation for future frontend development. Also, a shared repository and design system for the products ensures consistency across different functionalities and improves navigation within the platform.
Morevoer, with one repository, a unified login system, and a common UI, it will be much easier for the ArthurAI team to scale up the platform in the future.
Optimizing dashboards and data visualization
The more clean and intuitive your dashboard is, the better for your UX. And the same replies to presenting data. Arthur.AI knew that handling large-scale AI data was critical for their users, so our job was to make it as pleasant as possible.
To achieve that, we decided to implement:
- Streamlined user onboarding process - the new onboarding allows users to integrate different data sources faster and with minimal effort.
- Real-Time Alerts & Webhooks - notifications giving users real-time KPIs updates.
- Generic Inference Deep Dive Tables - flexible tables that allow seamless integration of all model types, enhancing data exploration.
- RBAC - a security model that restricts system access based on user roles and permissions.
All of this, combined with smooth navigation across the dashboards, contributed to a better user experience and clear data visualization across the platform.
Unifying the frontend framework with a dedicated UI components package
Consistent frontend across such complex platforms like ArthurAI requires a unified UI. To achieve that, we created an internal package containing the key UI components of ArthurAI platform.
The package is used within the existing and new (and upcoming) functionalities to facilitate the maintenance of shared elements across Arthur's ecosystem.

Thanks to the package, developers can now build and expand features faster, while users benefit from a seamless and unified experience across all products.
The ArthurAI team can now continue its growth without worrying about the frontend infrastructure or unintuitive UI. The platform now has a unified repository with all the key products included, which allows for faster onboarding of new users, better data visualization, or more effortless scalability in the future, just to name a few.
Faster Client Onboarding - Simplified and accelerated the onboarding process, allowing new users to integrate their models quickly and efficiently, reducing setup time and improving usability.
Unified Frontend Architecture - Merged separate codebases into a single repository, improving consistency, maintainability, and scalability.
Enhanced User Experience - Streamlined onboarding, improved dashboards, and implemented real-time alerts, leading to a more intuitive and efficient platform.
Optimized Data Visualization - Introduced flexible inference tables supporting all model types, making data exploration seamless and user-friendly.
Faster Development & Maintenance - Created a dedicated UI component package, reducing development effort and ensuring long-term scalability.
