Orum is an advanced AI-driven live conversation platform that helps sales teams supercharge their activities by automating the tedious tasks of manual dialing. It allows sales representatives to focus on engaging in meaningful conversations with prospects. The platform features powerful tools for call automation, analytics, and real-time coaching, helping teams maximize their outreach potential and drive more revenue.

TheVideoReader transforms ordinary video content into an educational and interactive experience. By integrating quizzes, notes, and other engaging tools directly into videos, it enables learners to interact with the material in real-time. This interactive approach not only enhances comprehension but also retention of the subject matter. Educators can quickly customize their content, and students benefit from a more dynamic learning environment.

Wayve focuses on creating self-driving technologies through data and machine learning. By employing deep reinforcement learning, the platform allows vehicles to learn from experiences in real-time, adapting to various driving conditions and environments. This approach emphasizes lower reliance on pre-coded rules, promoting a more flexible and intelligent driving system that can evolve through experiences, making it suitable for urban and complex scenarios.

LangMem provides specialized memory management capabilities for AI agents, enabling them to retain and recall vast amounts of information. This tool allows users to add memories, modify existing information, and retrieve memories based on specific queries. By integrating memory into AI processes, LangMem enhances the contextual understanding and relevance of responses, making it invaluable for applications that require continuous learning and adaptation.

SoccerAgent is a specialized AI framework designed for developing and training autonomous soccer agents using state-of-the-art multi-agent reinforcement learning (MARL) techniques. It simulates realistic soccer matches in 2D or 3D environments, offering tools to define reward functions, customize player attributes, and implement tactical strategies. Users can integrate popular RL algorithms (such as PPO, DDPG, and MADDPG) via built-in modules, monitor training progress through dashboards, and visualize agent behaviors in real time. The framework supports scenario-based training for offense, defense, and coordination protocols. With an extensible codebase and detailed documentation, SoccerAgent empowers researchers and developers to analyze team dynamics and refine AI-driven gameplay strategies for academic and commercial projects.