Rivet provides a comprehensive infrastructure for the age of agents, designed to coordinate AI agents, run their environments, automate their tasks, and deploy the applications they build. The platform consists of four core layers: Actors, which provide robust and persistent operations for agents; agentOS, which provides an isolated computing environment for each agent; Workflows, which enable the creation of multi-step processes that can be resumed after failures and restarted; and Dynamic Apps, which deploy the applications created by the agents.
Supernormal is an AI-powered platform that transforms meetings and conversations into organized notes and actionable insights that can be used directly in the workplace.
Tracelight is a platform specialized in working with financial models, data, and office outputs, and aims to integrate various components of the work environment into a unified workflow. The platform enables users to build financial models, create and update PowerPoint presentations, work with documents, and leverage the context and information available in SharePoint and Word, along with a suite of specialized tools for review and analysis.
Juna AI is a platform specializing in building and operating autonomous AI agents for factories and industrial operations. It uses Agentic Factory OS to create agents that handle specific production functions, such as process optimization, parameter monitoring, energy management, root cause analysis, and improvements in production and reliability.
Ludo.ai is an AI-powered platform designed for game developers, individuals, studios, and teams, offering a wide range of tools to help create games and develop their assets and content. The platform enables the generation of game ideas, concepts, content, and playable prototypes, as well as the creation of 2D and 3D images and assets, and the animation of models and characters.
Repowise offers a software-as-a-service (SaaS) platform for code intelligence that combines open source, hosted SaaS, and enterprise options. It aims to provide reusable context around the codebase for AI agents, helping to reduce code consumption, detect code quality issues and risks associated with changes before they are implemented, and generate architectural documentation that is directly linked to the code and updates alongside it.
