
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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Predict360, by 360factors, is a risk and compliance management and intelligence platform that automates workflows and enhances reporting for banks, credit unions, financial services organizations, and insurance companies.
The SaaS platform integrates regulations and obligations, compliance management, risks, controls, KRIs, audits and assessments, policies and procedures, and training in a single cloud-based SaaS platform and delivers robust analytics and insights that empower customers to predict risks and streamline compliance.
Happy with your current GRC but lacking a true analytics and BI tool for intuitive executive and Board reports? Ask about Lumify360 from 360factors - a predictive analytics platform that can work alongside any GRC. Keep your process management workflows intact while providing stakeholders with the timely reports and dashboards they need.
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Apache PredictionIO
Apache PredictionIO® is a robust open-source machine learning server designed for developers and data scientists to build predictive engines for diverse machine learning applications. It empowers users to swiftly create and launch an engine as a web service in a production environment using easily customizable templates. Upon deployment, it can handle dynamic queries in real-time, allowing for systematic evaluation and tuning of various engine models, while also enabling the integration of data from multiple sources for extensive predictive analytics. By streamlining the machine learning modeling process with structured methodologies and established evaluation metrics, it supports numerous data processing libraries, including Spark MLLib and OpenNLP. Users can also implement their own machine learning algorithms and integrate them effortlessly into the engine. Additionally, it simplifies the management of data infrastructure, catering to a wide range of analytics needs. Apache PredictionIO® can be installed as a complete machine learning stack, which includes components such as Apache Spark, MLlib, HBase, and Akka HTTP, providing a comprehensive solution for predictive modeling. This versatile platform effectively enhances the ability to leverage machine learning across various industries and applications.
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Predixit
Transform your traffic insights into strategic actions by categorizing users based on their browsing habits and behaviors. By grouping similar users, you can effectively identify those who deserve rewards, motivation, or a nudge to return, while also determining which brands, categories, and offers yield the highest conversion rates. Initiate the process by providing a browsing history feature, showcasing products that users have recently viewed or purchased. Additionally, remind them of items left in their cart that they haven't yet bought. Crucially, ensure that this approach encompasses all users, including those who are anonymous or unregistered. Utilizing advanced AI capabilities, you can enhance the shopping experience through personalized recommendations tailored to each shopper’s preferences and intentions. Furthermore, manage multiple recommendation campaigns by experimenting with various algorithms that align with the purchasing journey, tapping into numerous personalization strategies like affinity based on browsing and non-click behaviors. This comprehensive approach not only boosts engagement but also fosters a deeper connection with your customer base.
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