aiReflex
Fraud.com's aiReflex is a digital risk and trust solution designed to simplify your fraud defences, making life easier for you and safer and fairer for your customers.
aiReflex determines which transactions are legitimate in real-time using a multi-layer defence coupled with explainable AI to fight fraud and improve customer trust.
Everything you need to eliminate transactional and application fraud, including:
- Transactional Orchestration
- Adaptive Rule Engine
- AI Engine (Supervised and Unsupervised Machine Learning)
- Simulation Engine
- Dynamic and static lists
- Journey-Time Orchestration
- OmniChannel Case Management
- Centralised Fraud Reporting
Contact us at fraud.com on how we can help you improve your fraud defence while reducing costs and improving customer experiences.
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Stack AI
AI agents that interact and answer questions with users and complete tasks using your data and APIs. AI that can answer questions, summarize and extract insights from any long document. Transfer styles and formats, as well as tags and summaries between documents and data sources. Stack AI is used by developer teams to automate customer service, process documents, qualify leads, and search libraries of data. With a single button, you can try multiple LLM architectures and prompts. Collect data, run fine-tuning tasks and build the optimal LLM to fit your product. We host your workflows in APIs, so that your users have access to AI instantly. Compare the fine-tuning services of different LLM providers.
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NVIDIA DLSS
NVIDIA's Deep Learning Super Sampling (DLSS) represents a cutting-edge array of AI-powered rendering technologies aimed at improving both gaming performance and visual quality. By harnessing the capabilities of GeForce RTX Tensor Cores, DLSS not only elevates frame rates but also provides crisp, high-fidelity visuals that can compete with native resolutions. The newest version, DLSS 4, brings a host of innovative features. It utilizes AI to create as many as three extra frames for each frame rendered using traditional techniques, which can amplify performance by up to eight times compared to standard rendering processes, all while ensuring low latency through NVIDIA Reflex. Additionally, it replaces conventional, manually adjusted denoisers with a network trained by AI, resulting in superior pixel quality in ray-traced environments. This upgrade leads to better lighting effects and more precise reflections. Moreover, it leverages AI to upscale images from lower to higher resolutions without compromising clarity or detail. With the introduction of a new transformer-based AI model, the stability between frames is also significantly improved, allowing for an even smoother gaming experience. This impressive combination of features showcases NVIDIA's commitment to pushing the boundaries of gaming technology.
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Amazon GameLift
Amazon GameLift is a specialized hosting solution for game servers that facilitates the deployment, management, and scaling of cloud servers tailored for multiplayer gaming experiences. Whether you require a comprehensive managed service or only specific features, GameLift harnesses the capabilities of AWS to ensure optimal latency, reduced player wait times, and significant cost efficiency. Utilizing the extensive AWS global network, GameLift effectively handles game server management. It allows for the seamless matchmaking of players into game sessions while enabling the autoscaling of one, hundreds, or even thousands of instances simultaneously, all without the need for manual scaling adjustments in response to varying player demand. The platform provides essential multiplayer game functionalities, including matchmaking, session directories, player data management, and analytics. Additionally, with AWS, you can access services like AWS Lambda for serverless computing that is both scalable and adaptable, as well as features from Amazon GameLift FlexMatch to enhance your matchmaking processes. Overall, GameLift offers a comprehensive solution that caters to the complex needs of modern multiplayer gaming.
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