Transforming Support with SearchUnify AI Agents & Model Context Protocol (MCP)
Empowering seamless, secure, and intelligent AI interactions across your enterprise ecosystem
MCP provides standardized APIs that allow SearchUnify AI Agents to effortlessly connect with enterprise platforms like Salesforce, ServiceNow, and Zendesk, as well as open-source tools such as OTRS and GLPI enabling real-time data fetching and dynamic contextual retrieval across diverse systems.
MCP powers AI Agents to automate triage, escalation, and ticketing while enabling self-service through the Intelligent Front-Door Framework. In KCS environments, it also drives AI-based knowledge creation, retrieval, and recommendations for faster resolutions and smarter knowledge management. generation, and system updates can be seamlessly executed using integrated tools.
Security is foundational to MCP. Encrypted communication and role-based access control ensure compliance and transparency. Coupled with SearchUnify’s Governance Layer, AI interactions are monitored through audit trails, explainable AI insights, and human-in-the-loop validation. This architecture also ensures scalability, allowing AI Agents to interact with multiple systems simultaneously without creating integration bottlenecks.
MCP facilitates the retrieval and analysis of real-time support metrics, enabling data-driven decision-making. AI Agents can access actionable insights to inform strategies and improve support outcomes.
AI Agents deliver rich, precise answers by securely accessing multiple federated sources in real time, boosting relevance while keeping data decentralized and compliant.
MCP helps AI Agents harness contextual signals to personalize search results in real time, surfacing the most relevant content based on persona, journey, and intent.
By analyzing historical data trends, AI Agents forecast future support volumes and risks, enabling proactive escalation handling, improved resource planning, and SLA performance.
MCP lets AI Agents use classification models to tag and prioritize tickets based on urgency, topic, or tone—streamlining case triage and enhancing routing accuracy.
Real-time clustering enables AI Agents to detect and group similar issues or tickets instantly, supporting faster bulk resolutions and uncovering trends or knowledge gaps.
AI Agents orchestrate multiple ML models—clustering, classification, and generation—seamlessly through MCP, enabling complex workflows without added latency.
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