Qloo
Qloo, the "Cultural AI", is capable of decoding and forecasting consumer tastes around the world. Privacy-first API that predicts global consumer preferences, catalogs hundreds of million of cultural entities, and is privacy-first. Our API provides contextualized personalization and insight based on deep understanding of consumer behavior. We have access to more than 575,000,000 people, places, and things. Our technology allows you to see beyond trends and discover the connections that underlie people's tastes in their world. Our vast library includes entities such as brands, music, film and fashion. We also have information about notable people. Results are delivered in milliseconds. They can be weighted with factors like regionalization and real time popularity. Companies who want to use best-in-class data to enhance their customer experiences. Our flagship recommendation API provides results based on demographics and preferences, cultural entities, metadata, geolocational factors, and metadata.
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Borealis
All stakeholder engagement activities can be managed from one platform. Additional modules can be added to strengthen governance, environment, and social areas. Borealis' Stakeholder Engagement module gives you the tools to create more effective engagement plans, strengthen relationships with stakeholders, and align processes with industry best practice. To simplify your day, the Stakeholder Engagement module centralizes all stakeholder information and makes it easily accessible.
Build stakeholder trust with a proven methodology
Plan
Connect engagement strategy to execution. The mapping tool in Borealis makes it easy to analyze stakeholders to prioritize the allocation of resources.
Engage
Communicate with stakeholders in a more relevant and timely manner. Borealis uses AI-driven machine learning to keep records clean and up-to-date.
Measure
Prove compliance with ever-evolving standards. Borealis lets you easily track and progress, generate reports and documentation, and demonstrate the impacts of your efforts.
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Wherobots
Wherobots provides a seamless way for users to create, test, and implement geospatial data analytics and AI pipelines directly within their current data ecosystem, with the option for cloud deployment. This solution alleviates concerns regarding resource management, scalability of workloads, and the complexities of geospatial processing and optimization. By linking your Wherobots account to the cloud database housing your data via our user-friendly SaaS web interface, you can efficiently build your geospatial data science, machine learning, or analytics applications using the Sedona Developer Tool. You can also automate the deployment of your geospatial pipeline to the cloud data platform while monitoring its performance through Wherobots. The results of your geospatial analytics tasks can be accessed in various ways, such as through a single geospatial map visualization or via API calls, ensuring flexibility in how insights are utilized. This comprehensive approach makes geospatial analytics more accessible and manageable for users at all levels of expertise.
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Canvas X Geo
Canvas X Geo combines a rich graphics toolkit with sophisticated geospatial information handling. It is the geospatial illustration software that can be used in conjunction with Geographic Information Systems. You can represent, filter, and integrate many geospatial formats in one powerful application. Import and work with documents with over 1 million objects in high-resolution. Data collaboration is possible with multiple outputs, including figures, cross-sections, and maps. Your geospatial data should tell a story. Key stakeholders can get actionable insights, which will increase productivity and enable better informed decision-making. Canvas X Geo combines a rich graphics toolkit with sophisticated geospatial information handling. It is geospatial illustration software that can be used in conjunction with Geographic Information Systems. A powerful, single-purpose application that can represent, filter, and integrate many geospatial data formats.
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