Josys is a next-generation, AI-native platform designed to simplify identity security and governance for the modern enterprise. With AI adoption expanding the attack surface, Josys offers total visibility by discovering and securing every identity—including humans, machines, and AI agents—across all corporate applications. By automating complex governance tasks, the platform allows IT and security teams to instantly identify risks, control access levels, and resolve threats with autonomous precision. Currently trusted by over 1,000 organizations and MSPs worldwide, Josys turns identity governance into a competitive edge through real-time protection and operational efficiency. Visit josys.com for details.
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Stonebranch’s Universal Automation Center (UAC) serves as a comprehensive Hybrid IT automation platform that facilitates the real-time oversight of tasks and processes across both cloud and on-premises infrastructures. This adaptable software solution enhances the efficiency of your IT and business workflows while providing secure management of file transfers and consolidating job scheduling and automation tasks. Utilizing advanced event-driven automation technology, UAC allows you to implement instant automation across your entire hybrid IT ecosystem. Experience the benefits of real-time automation tailored for a variety of environments, such as cloud, mainframe, distributed, and hybrid configurations. Additionally, UAC simplifies Managed File Transfers (MFT) automation, enabling seamless handling of file transfers between mainframes and various systems, while easily integrating with cloud services like AWS and Azure. With its robust capabilities, UAC not only improves operational efficiency but also ensures a high level of security in all automated processes.
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Palette
Minimize mistakes and enhance efficiency by gaining immediate insights into commission accruals and disbursements. Your specific criteria can activate payouts, while all your information can be consolidated into a single reliable source. With our commission plan designer, implementing intricate calculation rules becomes straightforward. Establishing trust is essential, and this is achieved through transparent and verifiable commission processes that ensure timely payments. Commissions can be calculated automatically in real-time, allowing your team to focus on what truly matters. Palette equips your sales personnel with tailored performance dashboards that are refreshed instantly, fostering both transparency and clarity. The data related to commissions can be utilized to address your organization’s most pressing inquiries. You also have the opportunity to A/B test your compensation strategies to evaluate their effects on cash flow and overall business objectives. Optimize the ramp-up process, establish more effective quotas, manage commission expenses, and predict future payouts while extracting crucial insights that drive your business forward. By leveraging these tools, your organization can make informed decisions that align with its growth strategy.
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NEO
NEO operates as a self-sufficient machine learning engineer, representing a multi-agent architecture that fully automates the ML workflow, enabling teams to delegate tasks related to data engineering, model creation, evaluation, deployment, and monitoring to an intelligent pipeline while maintaining oversight and control. This advanced system employs complex multi-step reasoning, efficient memory management, and adaptive inference to tackle intricate problems from beginning to end, encompassing activities such as data validation and cleaning, model selection and training, handling edge-case failures, evaluating candidate behaviors, and managing deployments, all while integrating human-in-the-loop checkpoints and customizable control features. NEO is designed for continuous learning from outcomes and retains context throughout various experiments, providing real-time updates on its readiness, performance metrics, and potential challenges, thus creating a self-sustaining framework for ML engineering that reveals insights and alleviates typical obstacles like conflicting configurations and outdated artifacts. Additionally, this cutting-edge approach frees engineers from tedious tasks, allowing them to concentrate on more strategic projects and enhancing overall workflow efficiency. By streamlining processes and minimizing repetitive work, NEO ultimately catalyzes a transformative shift in machine learning engineering, significantly boosting productivity and fostering innovation within teams. In conclusion, the introduction of NEO marks a pivotal leap forward in how machine learning projects are executed, encouraging a culture of creativity and proactive problem-solving.
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