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In-Depth Assessment: We began by conducting a comprehensive assessment of the AI-observability startup's frontend architecture. This involved reviewing the existing codebase, identifying bottlenecks, and evaluating the performance issues.

Strategic Planning: We developed a strategic plan to optimize the frontend architecture. This plan included tackling specific areas such as routing, query parameter manipulation, error handling, code quality, and testing.

Collaborative Development: Our development team collaborated closely with the startup's internal team to ensure that the optimizations align with their long-term goals and objectives.


Approach points

Routing Optimization: We restructured the routing mechanism in the React application, improving page load times and overall user experience. This involved implementing lazy loading and code splitting techniques to reduce initial page size.

Query Parameter Management: We optimized how query parameters were used and manipulated in the application, ensuring efficient data retrieval and manipulation without overloading the client-side.

Error Handling Layers: We established robust error handling layers both in the backend service, which used Apollo GraphQL, and in the client-side application. This enhanced the platform's reliability and user experience.

Code Quality and Testing: We adhered to best software practices and principles, refactoring and enhancing the codebase. Unit tests were written using the Jest library to maintain code quality and catch issues early in development.

Reusable Components: To improve maintainability and code reusability, we created generic and reusable components, reducing redundancy in the codebase.

Linters and Rules: We configured various linters and modified ESLint rules to enforce coding standards and catch potential issues during development.

Micro Frontend Development: We implemented a micro frontend in Ruby for the authentication service, enhancing the overall system architecture and making it more modular.


Solution points

Performance Enhancement: The optimized frontend architecture led to significant improvements in the application's performance, resulting in faster load times, smoother user interactions, and a more responsive user interface.

Error Resilience: The implementation of robust error handling layers in both the backend and frontend improved the application's resilience. This resulted in fewer service disruptions and enhanced user trust.

Code Quality: The adherence to best software practices, unit testing, and the creation of reusable components contributed to higher code quality and maintainability. Developers could work more efficiently and with fewer bugs.

Scalability: The micro frontend architecture introduced scalability and modularity, making it easier to add new features and expand the platform's capabilities.


Outcome points

Series A Funding: The optimization of the frontend architecture significantly improved the startup's product, making it more attractive to investors. As a result, the startup successfully raised $10 million in Series A funding, providing the necessary capital for growth and expansion.

User Satisfaction: With a faster and more reliable platform, user satisfaction increased. This led to higher user retention rates and positive word-of-mouth referrals.

Competitive Advantage: The improved frontend architecture positioned the startup as a leader in AI observability. They gained a competitive edge by offering a high-performance, user-friendly platform.

Development Efficiency: The optimized codebase and adherence to best practices made it easier for the development team to implement new features and maintain the platform. This resulted in faster development cycles and reduced development costs.


Focus on the platform optimization for the Series A funding round

Nortik have been a great partner for us, helping us to build out a strong frontend team that have been critical to the success of our product.

***** *****

VP of Product

Anonymous

Learn how Nortik Software Solutions worked with Seed to Series A Startup clients and how they can support your Robotics & AI product and team.

Nortik Software Solutions Success Story in Robotics & AI for Seed to Series A Startup - A case study | 2024

Our main goals and deliveries included:

- Optimize the way routing was done in React application

- Optimize application usage and manipulation with query parameters

- Setup error handling layer in backend service which used Apollo GraphQL

- Create an error-handling layer in the client-side application

- Design software according to best software practices and principles

- Writing unit tests with Jest library

- Creating generic and reusable components

- Setup various linters and modify es-lint rules

- Writing micro frontend in Ruby for authentication service


Optimizing the whole frontend architecture and codebase

The client stakeholders worked closely with the team at Nortik Software Solutions

AI-observability & machine learning monitoring platform

Working with WhyLabs was really beneficial for Nortik. Besides the great work environment, Nortik was involved in the whole brainstorming process of building the product. We managed to push our limits on a weekly basis as they gave us space to challenge ourselves on a technical ground, and to understand how should a business (SAAS) operate and grow in a healthy manner.

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