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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Pylon
Pylon's intuitive design software allows you to create accurate proposals from anywhere, in less than 2 minutes. Pylon is the only software that allows you to view high-resolution imagery within your app. Pylon's award winning 3D Solar Shading toolkit helps you identify and track shading impacts throughout the year. Pylon's load profile analysis and interval data analysis will help you and your team to better understand customer consumption patterns. Analyze load profiles & interval data. You can close more solar proposals by using interactive Web & PDF proposals and native eSignatures. Fully integrated solar CRM that integrates with your solar design software to convert proposals. Pylon Solar CRM offers 2-way SMS and email communications, team management, lead management and pre-made deal pipelines.
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Google Earth Engine
Google Earth Engine serves as a cloud-centric platform designed for the scientific examination and visualization of geospatial data, granting users access to an extensive public archive containing over 90 petabytes of analysis-ready satellite imagery alongside more than 1,000 carefully curated geospatial datasets. This rich collection boasts over five decades of historical imagery that is refreshed daily, with pixel resolutions reaching as fine as one meter, showcasing datasets from sources such as Landsat, MODIS, Sentinel, and the National Agriculture Imagery Program (NAIP). Through its web-based JavaScript Code Editor and Python API, Earth Engine empowers users to perform analyses on Earth observation data while employing machine learning techniques, thereby enabling the creation of sophisticated geospatial workflows. The platform's seamless integration with Google Cloud facilitates large-scale parallel processing, allowing for thorough analyses and efficient visualization of Earth data. Furthermore, Earth Engine's compatibility with BigQuery enhances its capabilities, making it a versatile tool for users in various fields. This unique combination of features positions Google Earth Engine as an essential resource for researchers and professionals working with geospatial information.
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GeoExpress
The extensive, high-resolution images utilized in the geospatial sector offer critical insights, yet their large file sizes can create significant obstacles for sharing, viewing, and manipulating this information effectively. GeoExpress empowers geospatial experts to compress these images into our unique, industry-standard MrSID format. This format allows for both lossless and visually lossless compression, making it possible for users to reduce file sizes without compromising the quality of the images. Additionally, GeoExpress includes features for editing geospatial imagery during the compression process, enhancing the quality of visual data for subsequent analysis. Its standard editing tools encompass cropping and color adjustment, while also providing functionalities like reprojection and mosaicking. By compressing image files and geospatial data down to as little as 5% of their original size or cutting file sizes by half, you can maximize storage efficiency, streamline usage, and simplify the distribution process without losing visual integrity. This approach ultimately leads to improved accessibility and usability of vital geospatial information.
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