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We’re excited to introduce the latest enhancements to X-Stream, the knowledge layer powering Uniphore’s Zero Data AI Cloud. X-Stream unlocks the full potential of enterprise data by transforming structured and unstructured information into AI-ready knowledge, driving the next generation of question-answering systems, intelligent agents, and enterprise applications.
With X-Stream, AI responses are not only highly accurate but also grounded in your organization’s data. Configurable guardrails and robust access controls ensure that users only access the information they are authorized to see. For instance, a customer support agent can rapidly troubleshoot issues by searching across resolved tickets in multiple languages, reducing resolution times and enhancing customer satisfaction. Similarly, a sales representative can tap into past meetings and call data to craft personalized pitches, boosting customer engagement and driving better business outcomes.
As one of the four core layers in Uniphore’s Zero Data AI Cloud, X-Stream plays a pivotal role in redefining how enterprises build and deploy AI solutions. By removing traditional bottlenecks in data preparation and integration, X-Stream empowers organizations to unlock their data’s potential without requiring costly and time-intensive engineering efforts.
This latest release introduces a powerful suite of capabilities designed to streamline data workflows, improve user experiences, and make scaling retrieval-augmented generation (RAG)-based applications easier and more efficient than ever before.
What’s new in X-Stream
Pipeline configuration: Customization meets control
In today’s data-driven world, no two datasets are the same—so why take a one-size-fits-all approach to transforming them into AI-ready knowledge? That’s why we’re introducing customizable pipeline configuration in X-Stream, empowering organizations to tailor data workflows to meet their unique needs.
Why this matters: Whether you’re a data manager or a non-technical admin, configuring pipelines can often feel like navigating a maze of complexity. With this feature, we’re bridging the gap between raw data and AI-ready knowledge, enabling users to configure workflows without requiring advanced technical skills.
- Advanced chunking options: Break down data using flexible chunking methodologies like fixed-length and semantic chunking to optimize how information is segmented for your use case.
- Embedding model selection: Choose the best-fit embedding models for your data, ensuring optimal knowledge representation for downstream applications.
- Prompt-based document enrichment: Enhance documents by customizing prompts to add context and relevance, improving how AI models understand and utilize your data.
- Pipeline templates: Save and reuse pipeline configurations as templates, eliminating repetitive tasks and ensuring consistency across projects.
Multiple project support: Collaboration without compromise
Handling diverse use cases across teams often comes with challenges like redundant data ingestion, inefficiencies, and limited visibility. With X-Stream’s new Multiple Project Support, teams can now create and manage multiple projects within the same tenant, delivering greater flexibility and collaboration for a wide variety of use cases.
Why this matters: Managing datasets and workflows across teams and departments is often riddled with inefficiencies such as redundant data ingestion, inconsistent configurations, and effort duplication. This feature ensures teams can collaborate effectively, share resources efficiently, and maintain control over sensitive or work-in-progress initiatives.
- Centralized multi-project management: Create and manage multiple projects within the same tenant, enabling flexibility to handle diverse AI use cases simultaneously.
- Dataset reusability: Configure and reuse datasets across projects without redundant ingestion, reducing both time and storage costs.
- Enhanced access controls: Protect sensitive work-in-progress projects by setting them as private, or mark completed configurations as public for seamless team collaboration.
Context preservation for multi-turn conversations: Driving seamless engagement
One of the biggest challenges for AI in conversational settings is preserving context across multi-turn interactions to ensure coherent and relevant responses. X-Stream’s Context Preservation for Multi-Turn Conversations ensures AI agents deliver relevant, natural, and human-like responses throughout an ongoing session by dynamically referencing key information like user questions, prior AI responses, and conversational intent.
Why this matters: In customer-facing applications like chatbots and AI agents, a lack of context can result in fragmented or repetitive interactions—undermining the user experience. By preserving context, X-Stream ensures AI solutions deliver contextual and relevant responses, improving satisfaction and driving better outcomes.
- Dynamic context referencing: Retain key information from user queries, AI responses, and conversational intent across multiple turns for enhanced relevance.
- Intelligent follow-ups: Support follow-up questions and deeper engagement without losing the thread of the conversation.
- Continuity in interaction: Maintain a seamless conversational flow, even in complex, multi-step interactions.
Cross-language question answering (QA): Breaking down language barriers
Language should never be a limitation when accessing critical information. With X-Stream’s new Cross-Language Question Answering (QA) capability, users can ask questions in their preferred language and instantly retrieve answers from datasets in any other language. This eliminates the need for manual translation or duplicating content, enabling seamless access to knowledge across multilingual datasets.
Why this matters: In today’s globalized world, organizations manage diverse teams and datasets spanning multiple languages. Without the ability to bridge linguistic gaps, knowledge often remains siloed, limiting collaboration and innovation. Cross-language QA ensures that language is no longer a barrier, allowing teams to unlock the full potential of their multilingual content.
- Instant multilingual access: Retrieve answers from datasets in any language, regardless of the language used in the query.
- No translation overhead: Eliminate the need for manual translation or content duplication, reducing time and effort.
- Seamless discovery across languages: Effortlessly search, discover, and leverage information from global datasets.
Improved user experience: Transparent and intuitive answers
Navigating AI-driven results shouldn’t be a guessing game. With X-Stream’s Answer and Search Results, users now benefit from a more transparent and intuitive interface. Answers are displayed side-by-side with their sources, complete with clickable references, allowing users to verify information and dive deeper into the original content effortlessly.
Why this matters: In AI-driven systems, trust is built on transparency. Users need to see not just the answer, but also where it came from. This enhancement streamlines access to information, empowers users to make more confident decisions, and fosters trust in the reliability of AI responses.
- Side-by-side display: View answers alongside their original sources for clear, immediate context.
- Clickable references: Explore source content with a single click, enabling deeper understanding and verification.
Subject matter expert (SME) feedback for model fine-tuning
AI models need to understand the nuances of your business to deliver meaningful and accurate results. With X-Stream’s SME Feedback for Model Fine-Tuning, subject matter experts can provide detailed feedback on model outputs, helping refine AI behavior to align with domain-specific requirements. This feature enables SMEs to evaluate outputs, highlight inaccuracies, suggest improvements, and guide AI fine-tuning—all without requiring technical expertise.
Why this matters: Generic AI models often fall short when handling specialized, business-critical scenarios. SME feedback ensures that AI systems evolve to meet the unique needs of your organization, bridging the gap between generic outputs and the tailored responses that drive real impact. By incorporating domain expertise directly into the AI lifecycle, organizations can achieve better accuracy, user trust, and business alignment.
- Granular feedback mechanism: Allow SMEs to provide targeted feedback on model outputs, identifying inaccuracies and opportunities for improvement.
- No technical expertise required: Empower experts to contribute directly without needing technical skills or complex tooling.
- Real-time refinement: Continuously improve AI outputs and behaviors with iterative feedback.
Driving the future of AI-ready knowledge
These enhancements underscore our dedication to empowering organizations with the tools they need to unlock the full potential of their data. From simplifying data preparation to enabling seamless collaboration and delivering highly accurate, explainable AI-driven insights, X-Stream’s latest features are designed to drive meaningful outcomes. With X-Stream, you’re not just managing data—you’re transforming it into actionable knowledge that fuels innovation and propels your AI initiatives to the next level.
Ready to transform your enterprise knowledge into a competitive advantage?