Harness the Power of Federated Retrieval Augmented Generation for Seamless, Multi-turn Conversational Experiences

SearchUnify Virtual Assistant (SUVA), by harnessing the power of federated retrieval augmented generation, machine learning, NLP, NLQA, generative AI, and an insights engine helps resolve customer, employee, and IT support queries 24x7 - in the most contextual, personalized, and intent-driven manner — with the least amount of user effort.

Elevating SUVA’s Contextual Understanding with SearchUnifyFRAG^TM Approach

Organizations are in a race to adopt Large Language Models (LLMs). While organizations stand to gain a lot of productivity improvements through LLMs when a user question is directly sent to the open-source LLM, there is increased potential for hallucinated responses based on the generic dataset the LLM was trained on.

This is where SUVA’s Federated Retrieval Augmented Generation approach to LLMs comes into play. By enhancing the user input with context from a 360-degree view of the enterprise knowledge base, the LLM-integrated SUVA can more readily generate a contextual response with factual content.

What Differentiates SUVA’s LLM-Fueled Capabilities from the
Rest of the Generative AI Virtual Assistant Players?

Ease of Integration with Leading Large Language Models (LLMs)

SUVA supports plug-and-play integration with leading public LLMs (such as BARD, Open AI, and open-source models hosted on Hugging Face), partner-provisioned LLMs (such as Claude and Azure), and our in-house inference models. This means you can easily kickstart leveraging SUVA’s LLM-infused capabilities with a plug-and-play integration by just inputting the API for your LLM.

More Control over SUVA’s Response Humanization Through Temperature

With SUVA, an easy-to-use UI setting ensures admins get access to temperature control, which is a parameter used in chatbot interactions to adjust the randomness and creativity of the responses generated by the model. This ensures that admins can control the variability in responses based on user persona, use maturity, enterprise use case, and other factors.

Fuel Knowledge Gap Identification through [F]RAG™

Leveraging LLMs as linguistic engines, [F]RAG™ enables SearchUnify Virtual Assistant to swiftly pinpoint gaps in self-service knowledge, ensuring your support knowledge base remains robust and fully equipped to respond to user queries.

Fuel User Confidence through Reference Citations

Utilizing Federated Retrieval Augmented Generation, SUVA provides links to reference sources for its responses, ensuring credibility and user confidence. By including links as part of the response, users can access the origin of the information, fostering trust and reliability in the system's answers.

Fuel Contextual, Enriched Conversations with Adaptive Learning

SUVA implements a robust feedback system allowing users to express satisfaction or provide detailed feedback. This data aids in refining the virtual assistant’s responses, improving user experience over time.

Fuel Fine-tuned Conversations through Synthetic Utterance Generation

By identifying and learning from frequently asked questions, SUVA implements a caching mechanism to train on user queries, reducing the reliance on LLMs for repetitive user questions. This ensures cost-effective and fine-tuned interactions.

Efficient Costing with Respect to Large Language Model (LLM) Usage

By auditing and filtering out queries that are transactional in nature, SUVA segregates intents and stores queries which get multiple hits by caching, thus preventing LLM query hits for repeat queries, hence reducing LLM usage costs.

Better Intent Recognition Assistance for Tree Based Flows

Instead of following a fixed, predefined path, the chatbot uses the language model to determine the appropriate next step based on the user's query, allowing for a more flexible and adaptive conversation.

User Level Personalization and Access Controls

SUVA respects role-based access controls to define user roles and associate them with specific access privileges when it comes to responses. By continuously adapting based on user interactions, SUVA analyzes, engagement patterns, and preferences to improve interactions.

Fallback Response Generation in Case of LLM Downtime

In situations where the LLM in question is inaccessible, the fallback mechanism allows SUVA to provide alternative responses or handle user queries appropriately, from your index (stored in a knowledge base or a separate fallback module).

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