Elevate Customer Experiences with SearchUnify Virtual Assistant (SUVA)

SearchUnify Virtual Assistant (SUVA)

Elevate Customer Experiences with SearchUnify Virtual Assistant (SUVA)

Harnessing Advanced Technologies for Seamless Support

SearchUnify Virtual Assistant (SUVA) leverages federated retrieval augmented generation, machine learning, NLP, NLQA, and generative AI to resolve customer, employee, and IT support queries in a contextual, personalized, and intent-driven manner 24x7. This approach minimizes user effort, providing efficient and effective assistance.

Enhancing Contextual Understanding with SUVA's Approach

By incorporating the Federated Retrieval Augmented Generation approach, SUVA ensures that user queries are augmented with a 360-degree view of the enterprise knowledge base. This enables more accurate and contextual responses by leveraging factual content and minimizing generic or hallucinated responses.

Differentiated Capabilities of SUVA in the Virtual Assistant Landscape

SUVA offers ease of integration with leading Large Language Models (LLMs), allowing plug-and-play functionality with public and partner-provisioned models. Administrators can control the response humanization through temperature settings, offering variability based on user persona and enterprise needs.

Enhanced Interactions with Voice Activation and Audio Support

SUVA provides advanced Speech-to-Text (STT) and Text-to-Speech (TTS) capabilities, enabling users to interact via voice commands and receive responses as audio messages. This not only enhances accessibility but also makes conversations more natural and dynamic, creating engaging user experiences.

Identifying Knowledge Gaps Through [F]RAG™ and Reference Citations

SUVA utilizes [F]RAG™ to identify gaps in self-service knowledge, ensuring a robust support knowledge base. Additionally, it provides reference citations in responses, enhancing credibility and user confidence by allowing users to access the source of information.

Improving Conversations Through Adaptive Learning and Synthetic Utterance Generation

SUVA employs a feedback system to refine responses over time and reduce reliance on LLMs for repetitive queries. By implementing a caching mechanism for user queries, it ensures cost-effective interactions while improving the virtual assistant's efficiency.

Supporting Multi-modal FRAG and Voice-based Self-Service

SUVA extends support for multi-modal FRAG, enabling responses from video and audio files along with textual content. Moreover, it offers voice-based self-service with multilingual interactions, allowing users to engage in their preferred language through voice channels.

Cost-efficient LLM Usage and Enhanced Intent Recognition

SUVA optimizes LLM usage costs by filtering out transactional queries and caching repeat queries, reducing the reliance on LLMs. It also enhances intent recognition through flexible tree-based flows, offering adaptive conversations based on user queries.

Personalization and Fallback Mechanism for Continuous Support

SUVA supports user-level personalization and access controls, adapting responses based on user interactions and preferences. In cases of LLM downtime, the fallback mechanism ensures alternative responses, maintaining seamless support and user engagement.

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SearchUnify Virtual Assistant (SUVA)