# Building the OpsSentry AI Frontend with Next.js, TypeScript and Hindsight > Source: > Published: 2026-09-29 03:40:42+00:00 AI-powered incident management needs more than an intelligent backend. The interface also needs to make complex information easy to understand and act on. For OpsSentry AI, I worked on the frontend development, building a production-style operations interface using Next.js and TypeScript. The frontend brings incident monitoring, AI assistance, service information, and Hindsight memory capabilities together in one interface. **The Frontend Stack** The frontend is built using: Next.js TypeScript React FastAPI API integration Responsive UI design Using Next.js and TypeScript provides a structured foundation for developing and maintaining the application. **Designing the Operations Dashboard** One of my main areas of contribution was the frontend experience for the operations dashboard. The dashboard is designed to provide a centralized view of important operational information, including: Production health and service telemetry Active incidents Incident details AI incident assistance Service topology Post-mortem intelligence Workspace settings Command palette The goal was to create an interface where users can move between different operational tasks without making the experience unnecessarily complicated. **AI Incident Assistant** OpsSentry AI also includes an AI incident assistant as part of the frontend experience. The frontend communicates with the application's API to support AI-powered interactions. This creates a direct interface between the user and the intelligence layer of the application. The project also includes a fallback experience so that the frontend can remain demonstrable when the backend service is unavailable. **Bringing Hindsight Memory into the Interface** One of the interesting parts of the project is Hindsight. Hindsight provides memory capabilities that allow an AI system to work with information from previous interactions. Two important concepts are: **Hindsight Retain** Retain is about preserving useful information so that it can be used later. For an incident-management application, this can help maintain useful context from previous operational experiences. **Hindsight Recall** Recall is about retrieving relevant information when it is needed. This can allow the AI experience to make use of previously retained context instead of treating every interaction as completely isolated. The frontend includes a Hindsight memory explorer, giving the application a dedicated interface for interacting with this memory-oriented functionality. **Frontend Architecture** The frontend is organized around reusable components and pages so that different parts of the operations workflow can be maintained independently. The application also integrates with the backend through the API layer, including the /api/chat endpoint. This separation between the frontend and backend makes it easier to develop the user interface while keeping the underlying AI and service logic separate. Project Visual Here is a view of the OpsSentry AI frontend project and its major features: What I Learned Working on the frontend of an AI-powered application helped me understand that building an AI product is not only about the AI itself. The interface plays an important role in making AI capabilities useful to people. Through this work, I gained practical experience with: Next.js TypeScript React-based frontend development API integration Responsive UI design AI application interfaces Hindsight memory concepts Designing interfaces for incident-management workflows Conclusion My contribution to OpsSentry AI focused on creating the frontend experience that connects users with the application's operational and AI capabilities. With Next.js and TypeScript, the frontend brings together incident monitoring, AI assistance, service information, and Hindsight Retain and Recall capabilities into a unified interface. Building this frontend gave me valuable experience in developing user-facing interfaces for modern AI-powered applications and understanding how memory can become an important part of the AI user experience.