Fraud.net
Fraud.net is the world's leading infrastructure for fraud management. It is powered by a sophisticated collective Intelligence network, world-class AI, and a modern cloud-based platform that assists you:
* Combine fraud data from all sources with one connection
* Detect fraudulent activity in real-time for transactions exceeding 99.5%
* Uncover hidden insights in Terabytes of data to optimize fraud management
Fraud.net was recognized in Gartner's market guide for online fraud detection. It is a real-time enterprise-strength, enterprise-strength, fraud prevention and analytics solution that is tailored to the needs of its business customers. It acts as a single point-of-command, combining data from different sources and systems, tracking digital identities and behaviors, then deploying the most recent tools and technologies to eradicate fraudulent activity and allow transactions to go through.
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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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Veritone Redact
Utilize the AI-driven Veritone Redact software to save both time and expenses while optimizing essential resources by automating the process of redacting sensitive content in audio, video, and image evidence. The demanding resources and high costs associated with manually redacting sensitive information from media can burden public safety agencies. Nevertheless, this process is crucial for protecting witnesses and adhering to freedom of information statutes, as well as fulfilling various public records requests. Veritone Redact efficiently identifies human figures, license plates, officer notepads, and laptops (MDTs), while also allowing users to specify additional sensitive visuals and items within each scene, subsequently automating the redaction of this information across various media formats. After the redaction process is complete, users can swiftly download the modified evidence along with logs that assist in maintaining chain of custody standards, facilitating sharing with colleagues, public defenders, and other essential parties involved in the legal process. This streamlined approach not only enhances efficiency but also ensures compliance with legal requirements and fosters collaboration among stakeholders.
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Amazon Rekognition
Amazon Rekognition simplifies the integration of image and video analysis into applications by utilizing reliable, highly scalable deep learning technology that doesn’t necessitate any machine learning knowledge from users. This powerful tool allows for the identification of various elements such as objects, individuals, text, scenes, and activities within images and videos, alongside the capability to flag inappropriate content. Moreover, Amazon Rekognition excels in delivering precise facial analysis and search functions, which can be employed for diverse applications including user authentication, crowd monitoring, and enhancing public safety.
Additionally, with the feature known as Amazon Rekognition Custom Labels, businesses can pinpoint specific objects and scenes in images tailored to their operational requirements. For instance, one could create a model designed to recognize particular machine components on a production line or to monitor the health of plants. The beauty of Amazon Rekognition Custom Labels lies in its ability to handle the complexities of model development, ensuring that users need not possess any background in machine learning to effectively utilize this technology. This makes it an accessible tool for a wide range of industries looking to harness the power of image analysis without the steep learning curve typically associated with machine learning.
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