Uniphore has announced the launch of X-Stream, a new knowledge-as-a-service solution aimed at simplifying the development of retrieval augmented generation (RAG) applications for enterprises. X-Stream, which integrates into Uniphore’s core data and AI platform, unifies and streamlines the process of preparing AI-ready data. It eliminates the need for multiple tools and steps across the data stack, offering enterprises a single architecture to mobilize multimodal datasets and accelerate the development of domain-specific AI applications.
The key benefit of X-Stream is its ability to merge fragmented data preparation steps into a cohesive flow, allowing enterprises to convert data into AI-ready knowledge, which can then be used with Uniphore’s pre-built, industry-specific small language models or with custom models built by the enterprise. Umesh Sachdev, Uniphore’s CEO, emphasized that the company has solved for challenges like accuracy and AI hallucinations, providing safety measures and guiding customers toward AI sovereignty.
The rise of generative AI has increased the demand for RAG, a method that utilizes predefined databases to deliver accurate responses to complex queries. However, building scalable RAG applications poses challenges, particularly in handling disparate data formats and sources. Enterprises often struggle with data scattered across structured and unstructured formats, such as tables, documents, videos, and conversations. Traditionally, businesses have had to rely on various tools to manage these processes, from data connectors to vector databases, and graph capabilities to improve accuracy.
Uniphore’s X-Stream addresses this complexity by consolidating all necessary tools within a unified architecture. The offering ingests data from over 200 sources, performs intelligent merging and transformation, converts the data into embeddings, and stores it in a vector database. It further enhances AI workflows by generating knowledge graphs and creating synthetic data for fine-tuning models, providing teams with a robust end-to-end solution.
X-Stream also introduces factuality checks and chunk attribution to enhance trust in AI outputs, which can be critical when dealing with large, multimodal datasets. Sachdev noted that X-Stream allows enterprises to significantly reduce the time required to develop production-grade RAG applications—up to eight times faster than traditional methods. Additionally, Uniphore promises customers a return on investment of 4x to 6x within weeks of going live, supported by a usage-based pricing model.
Although similar capabilities are offered by other hyperscalers and startups, such as Amazon’s Sagemaker and Tonic AI, X-Stream stands out due to Uniphore’s 16 years of experience working with unstructured data across voice, video, and text. The company currently serves over 1,500 enterprises, including major clients like DHL, Accenture, and General Insurance.
Gartner predicts that by 2025, 30% of generative AI projects will be abandoned after the proof-of-concept phase due to issues with data quality, risk controls, and costs. Uniphore’s X-Stream aims to mitigate these challenges by providing enterprises with a comprehensive, efficient toolset to manage AI data pipelines and build scalable RAG applications.
