
SearchUnify is an enterprise Agentic AI platform built to transform customer support through autonomous, intelligent operations. Powered by SearchUnifyFRAG™ (Federated Retrieval Augmented Generation), the SCORE framework, and Agentic RAG, the platform unifies siloed enterprise data to deliver contextually accurate, real-time responses across the support lifecycle.
Its core product suite includes Cognitive Search for AI-powered retrieval across fragmented knowledge sources, SearchUnifyGPT™ for cited generative AI answers across every support touchpoint, SUVA (Virtual Assistant) for autonomous multi-turn query resolution, Knowbler for knowledge creation and management, and Agent Helper for real-time AI assistance during case resolution.
The Agentic AI Suite deploys eight purpose-built agents across the support lifecycle. The AI Support Agent handles end-to-end self-service case resolution. The AI SupportPlus Agent autonomously resolves CRM tickets, initiates clarification loops when context is missing, and hands off complex cases with full context. The AI Classification Agent manages real-time case tagging and routing. The AI Escalation Manager predicts and mitigates escalation risk. The AI Knowledge Agent automates knowledge creation and identifies content gaps. The AI Competency Agent resolves complex L2 technical workflows autonomously. The AI Agent Partner provides in-the-moment guidance to support teams. The AI Case Quality Auditor evaluates 100% of closed cases for continuous improvement.
The platform supports BYOLLM, Model Context Protocol (MCP), and a governance layer with role-based access control, PII masking, and compliance with ISO 27001, HIPAA, and SOC 2. It integrates with 100+ enterprise platforms including Salesforce, Zendesk, and ServiceNow.
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The HiveMQ Platform provides a scalable, reliable data backbone with an event-driven MQTT architecture. Here are a few highlights:
1. MQTT Broker: At the heart of the HiveMQ platform is a fully MQTT-compliant broker purpose-built for fast, reliable, bi-directional data movement between IoT devices and enterprise systems.
2. Edge Data Integration: HiveMQ Edge seamlessly integrates edge data by converting industrial protocols into standardized MQTT, enabling an interoperable IIoT infrastructure.
3. IoT Streaming Governance: Data Hub transforms data in flight, passing only the most relevant, contextualized data to cloud and enterprise systems.
4. UNS & IT/OT convergence Enabler: Commonly used as the backbone for Unified Namespace architectures and seamlessly connects OT devices with IT systems for full visibility and interoperability.
5. Distributed Data Intelligence: HiveMQ Pulse unifies and contextualizes data across the enterprise for smarter decisions exactly where they matter most.
6. Maximum Interoperability: Runs anywhere on-premises or in public or private clouds. Efficiently connects to streaming applications, databases and data lakes with a Java SDK to build your own
7. Scalability to Support Growth: Elastic scaling with automatic data balancing and smart message distribution. Proven benchmark of up to 200M active clients with 1.8B messages/hour
8. Business Critical Reliability: Zero message loss with persistence to disk and offline queuing. No single point of failure due to masterless cluster architecture and zero downtime upgrades
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Siemens Industrial Copilot
Siemens Industrial Copilot is an AI-driven assistant aimed at enhancing the efficiency of industrial companies by optimizing workflows, scaling operations, and fostering innovation throughout the industrial value chain, encompassing aspects from design and planning to engineering, operations, and service. Created in collaboration with Microsoft, this tool facilitates collaboration between humans and machines by automating tedious tasks, alleviating workloads, and addressing workforce skill shortages. Engineering teams benefit from the ability to create, refine, and debug intricate automation code, allowing for faster development cycles and heightened productivity levels. Maintenance personnel can utilize a chatbot interface to articulate equipment issues, seek information, troubleshoot machinery problems, and explore proposed solutions. By assisting with essential maintenance activities, from configuration to resolution, it plays a crucial role in minimizing machine downtime while simultaneously enhancing overall operational efficiency. Ultimately, the introduction of Siemens Industrial Copilot marks a significant advancement in the way industrial processes are managed and optimized.
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Cognite Atlas AI
Cognite Atlas AI serves as a low-code platform designed for industrial AI agents, leveraging AI-ready data to streamline workflows and enhance business results across various organizations on a large scale. By utilizing a user-friendly low-code interface, along with prebuilt agent templates and specialized industrial tools, digital teams can effortlessly create, modify, and deploy AI agents without the necessity of coding. The platform, which is driven by Cognite Data Fusion's Industrial Knowledge Graph, equips agents with contextualized industrial information, ensuring the delivery of dependable insights and the ability to automate intricate tasks relevant to the industry. It integrates diverse data types such as time-series data, events, documents, visual data streams, and 3D engineering models, effectively dismantling data silos and facilitating precise solutions to challenging inquiries. Users have the capability to develop customized AI applications that are adept at comprehending industry-specific jargon, procedures, and standards, thereby supporting various workflows including root cause analysis, equipment troubleshooting, and operational decision-making. This innovative approach not only boosts efficiency but also fosters a deeper understanding of the processes that drive industrial operations.
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