Best AWS Marketplace Software - Page 133

Right Party Contact Likelihood

This model picks the best time of day and phone number to contact for collections agents to recover credit card debt on delinquent accounts.

Risotto

Risotto is an AI-powered help desk and ITSM agent that automates internal support where employees already work, in Slack, Microsoft Teams, and email. Built by IT for IT and trusted by teams like Instacart, Gusto, ThoughtSpot, Jobber, Fundrise, and Retool, Risotto resolves up to 70% of Tier-1 tickets end-to-end with an AI agent that understands context and intent, not just keywords. Out of the box, Risotto rapidly automates the requests that flood lean IT teams: password resets, software access, general knowledge troubleshooting, identity and provisioning issues, MDM-related device requests, and more. Time-to-value is a key differentiator. Rather than the weeks of configuration legacy ITSM tools require, many customers see high automation numbers within hours of going live. Risotto fits however your support is set up today. It can augment your existing ticketing system, layering AI automation on top of tools like Jira Service Management, Freshservice, Zendesk, and ServiceNow, or serve as a complete standalone ticketing system in its own right. Unlike keyword-matching chatbots, Risotto holds a real conversation. It asks clarifying questions, walks employees through multi-step troubleshooting, and only escalates what truly needs a human, with full context. For harder, ambiguous problems, Risotto's Tier-2 assistant goes further, researching the issue and suggesting remediation plans. And because it learns from every interaction and from your documentation, it gets more accurate over time. Risotto also automates identity and access management. Employees request access with a simple message; Risotto confirms permissions, coordinates approvals, and provisions through your IAM tools, with time-bound and just-in-time (JIT) access and a complete audit trail. Knowledge management is automatic too: Risotto blends answers from Notion, Confluence, Google Drive, Guru, SharePoint, and Slack, turns support threads into articles, and keeps everything current without manual upkeep. Beyond IT, Risotto extends the same automation to HR, Legal, Finance, Engineering, and Security, with native HRIS integrations like BambooHR and Workday. Fragmented requests across teams unify into a single funnel that auto-triages by intent, while department-level permissions keep sensitive requests private and compliant. While Risotto's bread and butter is workflows across chat, ticketing, knowledge, identity, HRIS, and MDM, it also handles complex tickets across your CRM, procurement system of record, contract management tools, and more. It connects natively to Okta, Microsoft Entra, Google Workspace, GitHub, Linear, Asana, and Jamf, and its MCP server enables native connections to tools like Claude Cowork. Risotto is SOC 2 compliant, HIPAA compliant, and built to clear infosec reviews. Risotto is the best fit for mid-to-large organizations that already use an ITSM (or small organizations looking for their first AI-native ITSM), have lean teams strained by Tier-1 volume, need support across multiple departments, and want to consolidate scattered knowledge, without forcing employees onto a new portal. Get started with a free demo and a free 30-day pilot.

Average Rating: 4.8/5.0

Total Reviews: 4

RLCatalyst4.3.0.1

RLCatalyst 4.3.0.1 is a comprehensive solution designed to streamline the consumption and management of AWS resources, particularly tailored for scientific research institutions. It offers a self-service portal that integrates seamlessly into existing AWS accounts, providing users with one-click access to AWS Service Catalog assets, robust budget management, and secure data governance. This cost-effective, pre-built solution is available in both SaaS and enterprise models, enabling universities and research organizations to adopt it with minimal upfront investment. Key Features and Functionality: - Self-Service Portal: Empowers users with a user-friendly interface to access and manage AWS resources efficiently. - Cost and Budget Management: Provides tools to monitor and control expenditures, ensuring projects stay within budget constraints. - Secure Data Governance: Implements AWS best practices to maintain data security and compliance. - Pre-Built Catalog: Offers a ready-to-use selection of AWS Service Catalog products, facilitating quick deployment. - Flexible Deployment Options: Supports both self-hosted and managed hosting models to suit different organizational needs. Primary Value and User Solutions: RLCatalyst 4.3.0.1 addresses the challenges faced by research institutions in managing cloud resources by providing a centralized platform that simplifies resource provisioning, enhances cost visibility, and ensures secure data handling. By integrating seamlessly with existing AWS accounts, it reduces the complexity and time required to set up and manage cloud environments, allowing researchers to focus more on their scientific endeavors rather than on administrative tasks.

RLCatalyst 4.3.2

RLCatalyst BOTs Server is an Intelligent Automation product that allows you to drive efficiencies across DevOps, TechOps, ServiceOps, SecOps and BusinessOps. RLCatalyst BOTs Server acts as an "Automation Service Bus" and integrates with Cloud Providers, Configuration Management tools and ITSM tools. It provides a mature solution to design, run, orchestrate and govern your Enterprise-wide automation needs.

rmtoo

rmToo is a free and open-source requirements management tool designed for developers who prefer command-line interfaces and text-based workflows. It enables efficient handling of software requirements by integrating seamlessly with version control systems like Git, allowing for collaborative and versioned management of requirements. rmToo supports the creation of various artifacts, including PDFs, HTML documents, and dependency graphs, facilitating comprehensive documentation and analysis. Its modular and customizable design allows users to tailor the tool to their specific project needs, enhancing flexibility and adaptability. By leveraging existing development environments and tools, rmToo streamlines the requirements management process, making it an ideal choice for teams seeking a lightweight yet powerful solution. Key Features and Functionality: - Command-Line Efficiency: Optimized for command-line operations, rmToo integrates smoothly with text editors and development tools like Emacs, vi, and Eclipse. - Version Control Integration: Full integration with Git enables history tracking, statistics, and baseline management, ensuring comprehensive version control. - Versatile Output Formats: Generates diverse artifacts, including PDFs, HTML documents, and various graphs, all interlinked and integrated within the development workflow. - SCRUM and Agile Support: Provides comprehensive support for SCRUM artifacts and agile project development, enhancing project management processes. - Customizability and Modularity: Offers a highly customizable experience with a modular design, allowing for easy extension and adaptation to specific needs. - Robust Analytics and Editing: Includes advanced analytics modules and an Emacs mode for requirement editing, ensuring quality and efficiency. Primary Value and Problem Solved: rmToo addresses the need for a streamlined, text-based requirements management solution that integrates seamlessly with existing development environments. By eliminating the need for specialized graphical interfaces, it allows developers to manage requirements using familiar command-line tools and text editors, enhancing productivity and reducing the learning curve. Its integration with version control systems ensures that requirement changes are tracked and managed effectively, promoting collaboration and maintaining project integrity. The tool's flexibility and customizability make it suitable for a wide range of projects, particularly those following agile methodologies, by providing essential features like dependency management, traceability, and comprehensive documentation generation.

RoBERTa Base OpenAI

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

RoBERTa Base PyTorch Hub Extractive Question Answering

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

RoBERTa Large Extractive Question Answering Model

This is a Extractive Question Answering model from PyTorch Hub

RoBERTa Large OpenAI

This is a Extractive Question Answering model from PyTorch Hub

RoBERTa Large OpenAI PyTorch Hub Sentence Pair Classification

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

RoBERTa Large Sentence Pair Classification

This is a Sentence Pair Classification model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and then fine-tuning the entire model on [QNLI](https://rajpurkar.github.io/SQuAD-explorer/ ) dataset.

RocketML

RocketML is a Super Fast Computational engine for Machine Learning. Built for scientists and engineers, RocketML scales Machine Learning models with no limits. If you have a large data science/analytics team, RocketML will cut your cycle time and costs of people and hardware.

Average Rating: 4.0/5.0

Total Reviews: 4

RocketML Age & Gender Detection

RocketML's Age & Gender Detection is a high-performance machine learning solution designed to accurately determine the age range and gender of individuals from images. Leveraging advanced deep learning algorithms, this tool provides precise and efficient analysis, making it suitable for various applications across industries. Key Features and Functionality: - Age Estimation: Predicts the age range of individuals in images with high accuracy. - Gender Classification: Determines the gender (male or female of detected faces. - High Performance: Optimized for rapid processing, enabling analysis of large datasets without significant latency. - Scalability: Built to handle extensive image libraries, making it suitable for enterprise-level applications. - Integration with AWS: Seamlessly integrates with AWS services, allowing for easy deployment and scalability within the AWS ecosystem. Primary Value and User Solutions: RocketML's Age & Gender Detection addresses the need for efficient and accurate demographic analysis in images. By automating the process of age and gender identification, it reduces manual effort and enhances the speed of data processing. This solution is particularly beneficial for businesses requiring demographic insights for customer profiling, targeted marketing, and personalized user experiences. Its integration with AWS ensures reliability, scalability, and ease of use, making it a valuable tool for organizations aiming to leverage machine learning for demographic analysis.

RocketML Barcode Detection

RocketML Barcode Detection is a machine learning model designed to accurately identify and decode barcodes within images and videos. Leveraging advanced computer vision techniques, it enables businesses to automate barcode scanning processes, enhancing efficiency and reducing manual errors. Key Features and Functionality: - High Accuracy: Utilizes sophisticated algorithms to detect and interpret various barcode formats with precision. - Scalability: Built on a high-performance computing machine learning platform, it efficiently processes large datasets, making it suitable for enterprise-level applications. - Integration with AWS: Seamlessly integrates with AWS services, allowing users to scale computationally complex machine learning pipelines instantly. - Support for Multiple Data Types: Capable of handling diverse data formats, including images and videos, without the need for extensive preprocessing. Primary Value and Problem Solved: RocketML Barcode Detection addresses the challenge of automating barcode recognition in various environments, reducing reliance on manual scanning and minimizing associated errors. By providing a scalable and accurate solution, it enhances operational efficiency across industries such as retail, logistics, and manufacturing. Its integration with AWS ensures that businesses can deploy and manage the model within a secure and scalable cloud infrastructure.

RocketML Batch Image Object Detection

RocketML Batch Image Object Detection is a high-performance computing machine learning platform designed to process large-scale image datasets efficiently. Integrated with Amazon Web Services , it enables users to perform batch object detection tasks, identifying and locating multiple objects within images simultaneously. This solution is tailored for organizations seeking to accelerate their image analysis workflows without the need for extensive machine learning expertise. Key Features and Functionality: - Scalable HPC Infrastructure: Leverages AWS's global data centers and HPC resources, including GPUs and high-throughput networks, to handle computationally intensive tasks. - User-Friendly Interface: Abstracts complex HPC configurations, allowing users to deploy and manage machine learning models without deep technical knowledge. - Support for Various Learning Methods: Accommodates a range of deep learning techniques, from fully supervised to unsupervised methods, facilitating flexibility in model training. - Cost Efficiency: Optimizes resource utilization to reduce training times and operational costs, achieving significant savings compared to traditional methods. Primary Value and Problem Solved: RocketML Batch Image Object Detection addresses the challenge of processing vast amounts of image data for object detection purposes. By providing a scalable and efficient platform, it enables organizations to accelerate their machine learning workflows, reduce time-to-insight, and lower the total cost of ownership. This solution is particularly beneficial for industries requiring rapid and accurate image analysis, such as healthcare, automotive, and retail.