Uniphore, a leading global technology company specializing in conversational AI and automation, has unveiled X-Stream, a transformative layer in its core data and AI platform. X-Stream simplifies the development of retrieval-augmented generation (RAG) applications by enabling knowledge-as-a-service and integrating essential tools, connectors, and controls for enterprises to harness their multimodal datasets for domain-specific AI applications.
X-Stream provides a unified, open architecture that streamlines the often fragmented process of preparing AI-ready data, effectively serving as a comprehensive solution and eliminating the need for multiple tools throughout the development pipeline.
“X-Stream empowers customers to fine-tune their data and transform it into AI-ready knowledge. This can be seamlessly integrated into Uniphore’s production-ready small language models or utilized to develop custom models. Our experienced data scientists and engineers have tackled challenges related to accuracy and hallucinations, ensuring safety while guiding customers towards AI sovereignty,” stated Umesh Sachdev, Uniphore’s CEO.
Addressing Data Challenges for RAG
As generative AI evolves, the concept of RAG—where AI sources information from designated databases to deliver precise answers—has gained traction. Enterprises are racing to create dedicated RAG-based search and chat applications that leverage their internal knowledge bases for accurate, hallucination-free responses, ultimately enhancing efficiency across various functions.
However, developing and scaling these applications presents significant data challenges. Often, the information organizations need is dispersed across various sources and formats, including structured tables, unstructured text, documents, and videos. To consolidate this data, businesses typically resort to multiple components and data connectors, such as Fivetran, to link their data warehouses, ERPs, HCMs, and internal applications.
Once connected, organizations must enable RAG flow by chunking data, converting it into embeddings, and storing it in a vector database using tools like Milvus, Weaviate, or Pinecone. To enhance accuracy, they may also incorporate graph RAG capabilities, such as with Neo4j.
This fragmented approach quickly becomes cumbersome, often leading to project timelines stretching over several months before achieving a scalable generative AI application.
“We’ve heard from enterprise data leaders about their need for a more efficient method to drive knowledge transformation across voice, video, and text, steering away from traditional data platforms or libraries,” Sachdev explained.
To fill these data gaps, Uniphore’s X-Stream offers a cohesive architecture that consolidates all the necessary tools and controls in one place.
X-Stream ingests multimodal data from over 200 sources, making it AI-ready through intelligent merging and transformation. After initial processing, it chunks the data, converts it into embeddings, and stores it in a vector database, facilitating relevant data access for AI teams and supporting Uniphore's industry-specific models and RAG use cases.
Additionally, X-Stream generates knowledge graphs where context and reasoning are necessary and creates synthetic data to tailor models for specific use cases or industries. It also includes evidence management features like factuality checks and chunk attribution, enhancing trust in AI outputs.
This comprehensive solution accelerates the AI pipeline from data preparation to final output, enabling faster development of production-grade RAG applications.
“X-Stream stands out for two key reasons: it leverages Uniphore’s 16 years of experience with unstructured data spanning voice, video, and text, and it presents a unified platform designed to meet diverse enterprise AI needs,” Sachdev added.
Promising Significant Value
Though X-Stream is newly launched, Sachdev highlighted its potential to optimize AI and data components, allowing for deployment of domain-specific generative AI applications using in-house data up to eight times faster while adhering to the highest standards of quality, compliance, and governance.
“Uniphore employs a usage-based pricing model, with customers typically experiencing a 4x-6x return on investment within weeks of going live,” he noted.
Notably, some of X-Stream’s capabilities overlap with those of hyperscalers and startups, such as Amazon’s Sagemaker, Tonic AI, and Unstructured.io. The scalability of this new offering will be interesting to observe, particularly as more enterprises embrace generative AI for internal and external applications. Uniphore collaborates with over 1,500 companies, including DHL, Accenture, and General Insurance.
According to Gartner, it’s projected that by 2025, 30% of generative AI projects will be abandoned after the proof of concept phase due to poor data quality, inadequate risk controls, or escalating costs.
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