Turn Interactions from Reactive to Proactive
Transform support with AI-driven conversations that boost efficiency, strengthen trust, and elevate customer experiences.
Multi-turn Conversations
Access Control Settings
Temperature Control
Reference Citations
Multilingual Interactions
Multimodal Intelligence
Agent Handoff
Case Management
Elevating SUVA’s Contextual Understanding with SearchUnifyFRAGTM
Unlike using LLMs trained on generic datasets that risk hallucinations, SUVA uses a Federated RAG approach to LLMs to ground responses in enterprise knowledge, ensuring accurate, contextual, and trustworthy answers.

Analytics Dashboard
Track what matters from engagement to efficiency and turn real-time support metrics into smarter decisions and stronger outcomes.
Engagement Metrics
Understand how users interact with SUVA to optimize session outcomes and self-service adoption.
- Total Sessions
- Engaged Sessions (Deflected | Abandoned | Handover)
- Bounced Sessions
Operational Metrics
Monitor efficiency with real-time insights into response speed and agent workload distribution.
- Average Handle Time (AHT)
- Average Response Time (ART)
Customer Experience Metrics
Measure customer satisfaction trends to ensure support quality aligns with user expectations.
- CSAT
- Average CSAT
Feedback Metrics
Capture user sentiment from every session to continuously refine SUVA’s performance and tone.
- Session Feedback
- Response Feedback
Benefits of SUVA
01
Faster Resolutions
Engages in natural, intent-aware multi-turn conversations that remember context and streamlines the path to a solution.
02
Seamless Integration
Easily integrates with OpenAI, Claude to power intelligent, scalable support with plug-and-play interface.
03
Enhanced Credibility
Backs every response with linked references to ensure transparency and accuracy, instilling confidence in your customers.
04
High Support ROI
Maximizes your return on support investments by combining automation, efficiency, and exceptional customer experience at scale.
05
Improved CSAT
Enhances customer satisfaction by delivering faster, more accurate, and context-aware responses across every support channel.
06
Low Agent Burnout
Minimizes burnout by offloading repetitive tasks and enabling agents to focus on complex, meaningful interactions.
Use cases of SUVA
Customer Self-service
Reduce L1 caseload, faster and contextual self-service
- 35%Â Reduction in Average Handling Time (AHT)
- 43%Â Increase in First Contact Resolution (FCR) Rate
- 85%Â Reduction in Customer Support Operational Cost YoY
Employee HR Support
Streamlined employee onboarding, growth and training processes
- 78%Â Reduction in HR Inquiry Response Time
- 23%Â Increase in HR Task Automation
- 97%Â Increase in HR Data Accuracy and Compliance
Real World Customer Success Stories

Automation Anywhere, a global leader in Robotic Process Automation, sought to enhance support efficiency and improve customer experience by strengthening knowledge capture, VoC integration, and data usability. By deploying SearchUnify Knowbler, they intelligently transformed their content creation, leveraging pre-built templates, AI-generated titles, and customizable prompts to meet real-time support needs more effectively.
Key Outcomes:
Accelerated knowledge delivery, reduced content creation effort, and enhanced support team productivity.
Boost in knowledge creation
More contributing agents
Cut in publishing time

Accela, a leading govtech provider, sought to elevate the precision and depth of its conversational support, ensuring customers received highly accurate, relevant, and insightful assistance. By implementing SearchUnify Virtual Assistant (SUVA), they revolutionized their virtual agent capabilities, meticulously fine-tuning prompts, significantly improving response accuracy, and unlocking actionable performance metrics to drive continuous enhancement.
Key Outcomes:
Lesser cost per interaction and improved user satisfaction, winning a Gold Stevie Award for Best Use of Technology.
Support Cost Savings
Response Accuracy
Gold Stevie Award

Cornerstone OnDemand, a global leader in talent management software, sought to elevate its support experiences and enhance agent productivity. They aimed to achieve this by centralizing fragmented content and gaining deeper insights into customer needs. By implementing SearchUnify's Cognitive Search and Knowbler, Cornerstone OnDemand transformed their knowledge ecosystem, unifying search across all sources, unlocking profound content insights, and streamlining case management through intelligent, AI-driven workflows.
Key Outcomes:
A surge in self-service resolution and boost in CSAT, with improved agent efficiency & faster case closures, and same-day resolutions.
Self-Service Resolution Rate
Higher CSAT
Higher Same Day Resolution

EBSCO, a prominent provider of online research content to institutions in the eLearning space and beyond, aimed to enhance content discoverability and user engagement. By adopting SearchUnify Cognitive Platform, EBSCO streamlined search experiences with enhanced search functionalities and content weighing, making their research content more accessible. Furthermore, in-depth insights helped in refining content, leading to an increased user engagement.
Key Outcomes:
An increase in Academy content views, stronger learner engagement, and a streamlined user experience across the platform.
Growth in Academy Views
Improved Content Visibility
Actionable Insights
Industry Recognitions
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Featured Resources
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Frequently Asked Questions
SUVA is a next-generation conversational AI powered by large language models (LLMs) and agentic AI, designed to provide autonomous, proactive, and personalized support for enhanced customer and employee experiences.
SUVA enhances customer support by providing contextual, personalized responses, reducing the average handling time, and increasing first-contact resolution rates. It efficiently handles L1 queries, minimizing support team load and cutting operational costs.
Yes, SUVA supports plug-and-play integration with leading public LLMs (such as BARD, OpenAI, and Hugging Face), partner-provisioned LLMs (such as Claude and Azure), and in-house inference models.
SUVA leverages a Federated Retrieval Augmented Generation (FRAG) approach, which enhances user input with context from a 360-degree view of the enterprise knowledge base, ensuring more accurate and contextually relevant responses.
Yes, SUVA offers advanced Speech-to-Text (STT) and Text-to-Speech (TTS) capabilities, allowing users to interact with the assistant using voice commands and receive responses in audio format.








