B2B AI Agent Frontend

TIQ.solutions asked me if I could create a frontend-concept, design and the implementation of their new emerging B2B AI agents chat platform. TIQ creates their own network of specific LLM implementations that analyze the customers data and generate outputs via other specialized LLMs depending what the user wants to achieve as an output. There could be a combination of statistics, analytics, a blog post article writing agent or a simple company agent that can help other employees summarizing company specific data at a glance. The client provides the necessary APIs (for calendars, documents, other shares or static documents) and TIQ implements them into a regularly trained model. The tokens a company uses get logged so it's possible to charge them by usage. The backend for the frontend is done in Python, pydantic and FastAPI. The chats, user data and everything app specific is saved in a PostgreSQL database. The frontend is created in NuxtJS / Vue, TypeScript and SCSS. Every UI item is a proper reactive component and the whole app is responsively optimized for all screen sizes. Like we know it from other web apps we have a sidebar on the left with all the chats, user settings, favourites and the history of previous chats. The chat view itself has an additional top bar that contains the main information about the current conversation (current files within the context, agent information and additional functionality). We have proper user management, with registration, login and refresh tokens. Companies can manage their employees (and tokens) on their own and also chose who can access a specific model via a company admin panel. The latest changes we implemented is streaming. Like we know it from other AI frontends the different LLMs within a pipeline give proper feedback while they all work together on their tasks and when one output gets passed to the next LLM. Sadly I cannot give you a direct link to the deployment but I would be happy to give a live demo via screen sharing.