Cerego is a flexible platform that effectively drives your team's learning, training, and skill set acquisition. Simple on the outside, while robust and multidimensional inside, Cerego shifts the paradigm of learning software by focusing on how people learn rather than what they learn.
With over 250,000 learners and partnerships with 50+ of the largest and best learning institutions, Yellowdig has analyzed years of efficacy data that demonstrate the platform's impact on learning outcomes. From before enrollment to beyond graduation, Yellowdig impacts the entire student lifecycle, serving as a university social network and a course engagement tool. This simple, yet innovative software platform establishes digital learning communities, increases student knowledge of current even
Delivering learning experiences that drive business results that really matter.
Today's challenge to train machine learning models is not to get the data itself - but to get the clean labelled data - to avoid having a "garbage in garbage out" loop. While current digital transformation by AI is powered by machine learning models, this process of data annotation becomes critical. Kili Technology serves as the training data solution to facilitate data annotation for image, video and text for various Computer Vision and NLP tasks with a robust tool to manage data quality and si
Domo is an agentic platform for the intelligent enterprise that helps organizations build, manage, and distribute AI-powered solutions using governed business data. The platform brings together three core capabilities: a data foundation to connect and govern enterprise data, an activation layer to build AI agents, apps, workflows, and analytics, and a distribution layer to deliver intelligence where people work. Designed for enterprises, Domo works with existing cloud data platforms such as Snow
Free and open source, Ionic offers a library of mobile-optimized HTML, CSS and JS components, gestures, and tools for building highly interactive apps. Built with Sass and optimized for AngularJS.
We’re a values-based technology business with integrity at its core. We’re on a mission to help training and workforce skills providers power brighter futures by making assessment streamlined, compliant and accessible for everyone. We’re a dynamic team, passionately committed to doing whatever it takes to help our clients achieve their goals. We're invested in our people, and committed to making a sustainable difference to our community and the environment.
Soft Tech V6 is a comprehensive software solution tailored for the window and door industry, streamlining the entire process from design and estimation to manufacturing. Developed by industry experts, it enables rapid creation of complex window and door configurations, ensuring accuracy and efficiency. The software's advanced estimation tools provide reliable quotes that reflect true production costs, aiding in better margin control. Its manufacturing modules optimize production processes, ensur
Yonghong Z-Suite is a comprehensive big data analysis platform that streamlines the entire data analysis process, encompassing data collection, cleaning, integration, storage, computation, modeling, training, visualization, and collaboration. By consolidating these functions into a unified platform, it significantly reduces the costs associated with implementation, integration, and training, enabling enterprises to efficiently develop data-driven applications. Key Features and Functionality:
Kedro is an open-source Python framework designed to facilitate the creation of reproducible, maintainable, and modular data science code. By incorporating software engineering best practices, Kedro enables data professionals to build production-ready data pipelines efficiently. It offers a standardized project structure, ensuring consistency and scalability across projects, and supports seamless transitions from development to production environments. Key Features and Functionality: - Pipelin
Schemantic.io: automated context layers across clouds and warehouses, built in hours rather than months, so LLMs and analysts get accurate answers from enterprise data. Enterprise relational data is only as useful to an AI agent or analyst as the context layer above it: what each table means, how tables join, and which entities, events, and attributes they describe. Missing or wrong, that layer caps accuracy no matter which model runs underneath. Anthropic's June 2026 evaluation of its self-serv
Sprih is an AI climate infrastructure designed for enterprises that need to move from data collection to reporting without the usual friction—manual spreadsheets, siloed teams, and disconnected frameworks. At the center is SustainSense, an AI engine that processes environmental data at scale. It draws from a dataset covering 150,000+ companies and 400,000+ processed sustainability reports, enabling organizations to source and contextualize data that would otherwise take months to compile. This
Azure Managed Application that brings multi-agent AI assistance to project management. ProPM Agent is an enterprise AI project execution workspace for PMOs, program leaders, project managers and delivery teams. Available through Azure Marketplace, it helps organizations move beyond disconnected status reporting, siloed documents and manual coordination by bringing project context, governed knowledge, role-aware collaboration and execution workflows into one controlled environment. Unlike gener
CoTrial AI is an advanced artificial intelligence platform designed to revolutionize clinical trials by enhancing efficiency, accuracy, and compliance. It integrates cutting-edge technologies to address longstanding challenges in the clinical research domain, offering solutions that streamline processes and improve outcomes. Key Features and Functionality: - CoTrial Screening: Utilizes machine-executable logic graphs that combine LLM-RAG reasoning, DeepSurv survival modeling, and behavioral tr
Easily register, modify, track, score, publish and report on your analytical models using a visual, web-based interface. Store models within folders or projects. Develop and validate candidate models. Assess candidate models for champion model selection. Then publish and monitor champion models to ensure optimal model performance.